Construction Research Congress 2020: Infrastructure Systems and Sustainability

Critical infrastructure is the guarantee for the normal operation of economic and social life. Once it is destroyed, it is urgent to restore it in time and minimize the impact of the damage. The paper takes the critical infrastructure interdependent network as the research object, and discusses the joint optimization model and algorithm design of the optimal restoration scheme after the network was destroyed. It first proposes a topology model based on infrastructure and travel networks, and considers the infrastructure impact scale with restored time as the evaluation mechanism. Then, the planning model to minimize network performance loss are constructed. Besides, ant colony algorithm is proposed to solve the optimal restoration scheme. Finally, taking illustrative and practical examples as the experimentations, the feasibility of the model and algorithm is verified. The contributions of this paper are mainly to propose a joint optimization model for the optimal restoration scheme of the infrastructure network, and meanwhile, analyze the related factors including restoration routes, restored time, and impact scale.

Installing new pipes and maintaining existing infrastructure are of top priority for every city. The process of installing/maintaining these pipelines can have undesirable environmental impacts resulting from airborne emissions from the construction equipment used. City engineers have the options of using traditional open-cut installation or trenchless technology method known as horizontal directional drilling (HDD). This paper presents empirical evidence highlighting the benefits of HDD over open-cut procedure based on productivity and airborne emission of equipment using a real-time wireless smart sensor. This research project involved the installation of 514 L.F. of 12-inch, DR18 fully restrained PVC water pipe using HDD and 576 L.F. of 12-inch C900 DR18 fully restrained PVC water pipe using traditional open-cut methods in the city of Enid, OK. Field data on productivities of the HDD and open-cut installations and airborne emission of equipment used were collected and analyzed. The productivity of the HDD installation was found to be 40% higher than that of the open-cut installation. The airborne emissions of the equipment used in the HDD and open-cut installation were comparable, with lower CO emission in the HDD installation.

One of the factors that determines the viability of investment in portfolio-based community solar is the volatility in power generation as a result of temporal, spatial, and environmental risk factors. In the context of community solar, volatility in power generation is undesired and considered as a risk factor which is often quantified by the variance or standard deviation of the generated power. One drawback to such an approach may be that any deviation above the average generated power is considered as risk. However, in reality, only the portion of the volatility that falls below the average generation is suboptimal, and in fact, investors welcome higher power generation. This study investigates the application of alternative risk models to variance, namely semivariance, mean absolute deviation, and conditional value-at-risk in reducing the volatility and improving the power generation performance of portfolio-based community solar. To this end, a community consisting of seven houses was used as a case study, and the effects of implementing alternative risk quantification techniques on power generation were studied using a modified modern portfolio theory model. The results of this study indicate that alternative risk models perform similarly; however, they can provide the community with more diverse options for generating solar power with less unfavorable volatility. The findings of this study can be of great help in removing the barriers to renewable energy penetration by providing more economically efficient energy portfolios.

Private investment through revenue bonds has become an important source for public agencies seeking to implement transportation public-private partnerships (P3) projects. It has been a long-standing assumption that private money is available and sufficient compared to a limited number of P3 projects in the pipeline. This assumption, however, remains unsubstantiated. Based on the neo-classic economic prediction that prices go high if demand exceeds supply, this study presents the construction and performance of the P3 bond index to evaluate private capital for P3 projects. The index measures the return of P3 bonds and helps encourage private investment in P3 projects.

Metro rail systems play a key role in urban development due to its relatively low ratio between energy consumption and transport capacity. As sustainable development became an international priority in the last decade, public authorities and the society have shown interest for more sustainable methods for delivering metro rail services. Research revealed that the sustainable level of a transportation system is related to system effectiveness and efficiency. Emerging technologies offer possibilities to promote such practice through intelligent operation and maintenance. As such, this paper presents a technology integration framework to facilitate effective and efficient operation and maintenance for metro rail projects. Cloud computing, fully automated operation, building information modeling, prognostic and health management, and artificial intelligence have been identified as key technologies for promoting metro rail sustainability. The framework, therefore, is formed by these technologies, along with their summarized functions and their inter-relations. In addition, strategies such as development of collaboration intensive environment and facilitate deepen cooperation between industry and academic are also acknowledged, which are expected to be integrated with the technology implementation. Moreover, case study of a pilot metro rail project in China with the proposed technology integration framework and strategies is presented. This paper provides valuable insights into the successful delivery of sustainable metro rail system.

Wind farm projects have recently gained popularity in many countries. However, since wind farms are a novel type of infrastructure for energy production for which limited historical data are available, numerous unique challenges are encountered during their construction. One of the main challenges involves risk management. Many researchers and practitioners have investigated on- and off-shore wind farm projects in terms of risk identification. However, they have mostly focused on off-shore wind farm projects; there is little research on risk identification for on-shore wind farm projects. To address this gap in the research, this paper develops a risk breakdown matrix (RBM) for the construction of on-shore wind farm projects. Due to a lack of research on risk identification for on-shore wind farm projects, in this paper, the case-based reasoning (CBR) technique is used to develop the RBM. First, the construction work packages (CWPs) of on-shore wind farm projects are identified. Then, by comparing the CWPs of these projects to those from other types of construction projects, the work-package-level risks that affect each CWP are identified based on the similarities between on-shore wind farm projects and other types of construction projects. The RBM developed in this paper can be used for the risk identification and risk management of on-shore wind farm projects. The contributions of this paper are twofold: First, it introduces CBR as a risk identification technique for on-shore wind farm or other similar construction projects, which is a topic that has not previously been comprehensively studied. Second, it identifies the work-package-level risks affecting these projects and maps each risk factor to the affected CWPs.

Bridges are important element of civil infrastructure systems as they support the society and the economy on daily basis. In many countries, existing bridge infrastructure is aging and deteriorating. Bridge management systems (BMSs) have been utilized to select appropriate maintenance, repair, and replacement (MR&R) actions to maintain existing bridges within acceptable safety and serviceability limits. Bridge inspection and bridge condition assessment are important steps needed to make bridge management decisions. The main limitation of bridge condition assessment through inspection is the uncertainty inherent in bridge inspectors’ subjective ratings while using linguistic expression to quantify defects associated with the various bridge elements. This can significantly affect reliability of the condition assessment process. This research reviews the fuzzy logic and the stochastic analysis utilized in bridge condition assessment to account for uncertainty and subjectivity and proposes a combined method to address both limitations in bridge condition assessment. Then, a systematic method for bridge deck condition assessment is proposed and demonstrated with a case study. The method benefits from both the fuzzy membership functions and the stochastic approach to account for uncertainty in bridge condition assessment. The method is simple and practical but it needs be validated further with more case studies to be accepted in practice.

Buildings are the major energy consumer in cities. Understanding the energy dynamics in cities is the premise for urban system design and policymaking for large scale building retrofitting. Research developed various urban building energy simulation tools, however, due to computational complexity, city-scale simulations usually consume enormous computational resources and take an extremely long time for running. This study proposed a distributed adjacent block model to decompose city buildings into inter-connected building networks regarding inter-building effects, such as shading and reflection. After a validation test in a real city block, the proposed method shows higher computational efficiency and reliability.

A natural disaster often generates large quantities of debris and waste, which challenges local waste management systems. Therefore, it is important to effectively prioritize affected areas for debris collection, mobilize limited resources, and manage local waste management systems based on resilience. The impact of disaster debris spatially varies depending on how it is distributed and processed, and thus resilience after the impact should be defined with a spatial dimension. However, current resilience metrics, which are related to a temporal view of system performance, do not sufficiently capture differential impacts across affected geographic locations over time. This study proposes a spatiotemporal framework for improving resilience of a post-disaster waste management system where resilience is measured using three dimensions: performance, road segments, and time. It is important to note that disaster waste logistics resources are arranged with respect to roads (e.g., by sending hauling trucks along a road to collect the surrounding debris). As such, two-dimensional spatial information (i.e., x- and y-coordinates) is reduced to one-dimensional information (i.e., a series of road segments). The framework will enable decision makers to compare the resilience of different roads and arrange resources for disaster debris management in a more systematic manner.

Owing to the increasing dynamics and complexity of construction tasks, workers often need to memorize a big amount of engineering information prior to the operations, such as spatial orientations and operational procedures. The working memory development, as a result, is critical to the performance and safety of many construction tasks. This study investigates how the format of engineering information affects human working memory based on a human-subject virtual reality (VR) experiment (n=90). A VR model was created to simulate a pipe maintenance task. First, participants were asked to review the task procedures in one of the following formats, including 2D isometric drawings, 3D model, and VR model. After the review session, participants were asked to perform the pipe maintenance task in the virtual environment based on their working memory. The operation accuracy and time were used as the key performance indicators of the working memory development. The experiment results indicate that the 3D and VR groups outperformed the 2D group in both operation accuracy and time, suggesting that a more immersive instruction leads to a better working memory. A further examination finds that the 2D group presented a significantly higher level of intrinsic cognitive load and extraneous cognitive load in the working memory development compared to the 3D and VR groups, indicating that different engineering information formats can cause different levels of cognitive load in working memory development, and ultimately affect the final performance. The findings are expected to inspire the design of intelligent information systems that adapt to the cognitive load of construction workers for improved working memory development.

Owing to the increasing complexity of construction tasks and operations performed in confined workplaces, workers rely progressively on working memory, i.e., the short-term and temporary storage of information pertaining to near future events, to ensure the seamless execution of construction tasks. Although literature has discovered a strong relationship between engineering information formats and the quality of working memory, there is still a clear theoretical disagreement on the implications of the complexity of engineering information in the development of working memory. This study addresses the knowledge gap with a human-subject experiment (n=60). Participants were required to review one of the two instructions for a pipe maintenance task: a simple 2D isometric drawing with bulletins (2D-simple) and a complex 2D isometric drawing with rich text (2D-complex). After the review session, the participants were asked to perform the pipe maintenance task in a virtual reality (VR) environment. Collected data include participants’ task performance (accuracy and time), pupillary dilations, and gaze movements. The results show that the 2D-simple group outperformed the 2D-complex group in terms of both accuracy and time. An attention pattern analysis using approximate entropy (ApEn) of gaze movements suggests that a higher ApEn in the vertical axis, i.e., a more irregular and complex gaze movement between instructions, may result in a more efficient use of working memory and thus contributes to a better performance. This study provides preliminary evidence regarding the impact of engineering information complexity on the working memory development of construction workers.

Traffic congestion has increased dramatically in the great Cairo metropolitan area (GCMA) of Egypt. The congestion is due to various reasons including insufficient parking spaces. Currently, building new multi-story garages is not economically attractive for investors in comparison to residential buildings. Thus, there is a need for developing an incentive model to motivate investors into constructing multi-story garages as community service buildings. The objective of this paper is to present a novel decision-making approach for governmental policies that incentivize mid-range investors to construct multi-story garages instead of residential buildings in congested districts. The proposed model utilizes game theory to identify an equilibrium strategy accounting for the needs of investors and governmental agencies. The authors carried out the following three step methodology: (1) develop a parametric game theoretical model that represents the players along with their strategies and payoffs; (2) test the model on a hypothetical case study based on actual data for an under construction residential project; and (3) analyze the results of the model and its sensitivity to its input parameters. For future work, the proposed model will be validated on a human-lab via a game theoretical platform. Even though the proposed model is developed to address the GCMA’s congestion problem, it is dynamic and transformable. The model can be generalized to play a significant role in enhancing the existing infrastructure through incentivizing the private sector to construct community service projects.

Green building construction has received global interest over the past two decades. The definition of a green building has evolved from energy-efficient buildings to net-zero buildings, and now is expanding towards net-positive buildings. Even though these concepts entail significant environmental benefits, they largely overlook life cycle environmental impacts associated with building construction. While net-zero to net-positive development is expected to result in operational environmental benefits, the energy demand reduction and renewable energy-related interventions carry added life cycle impacts during the construction and demolition stages. Therefore, without taking a life cycle thinking perspective to green building assessment, it is difficult to quantify the exact environmental benefits. To address this issue, this research aims to develop life cycle performance benchmarks for green buildings and to provide a mechanism to rate “green” buildings based on life cycle environmental performance. A methodological framework was developed to evaluate the aggregated life cycle performance of green buildings, thus enabling the best choices in green building design through a more holistic perspective. The proposed framework is deployed by combining life cycle thinking with BIM, which can be used to monitor and track building inputs and construction practices under various scenarios. Outcomes of this research will redefine the concept of green buildings and will lay out the foundation for constructing zero impact on civil infrastructure. The findings will assist the construction industry, community developers, and government authorities to ensure that sustainable development goals are being met in a true and holistic sense.

There is a transition from centralized electricity generation to distributed energy resources (DER). An increasing number of customers are adopting distributed solar generation (DSG). However, utilities face the difficulty of predicting electrical demand, and therefore dispatching and controlling generation. The goal of this research is to study the evolving dynamic market behavior affected by DER investment decisions. To that end, agent-based modeling (ABM) is used to study the emerging behavior of the market under those conditions. The model is developed from the existing and validated AMES framework, which is used for agent-based computational economics (ACE) simulation of wholesale power markets. The steps of the research are to: 1) develop a hypothetical service area for use as a case study; and 2) implement a customer agent-type with decision-making capabilities. The hypothetical case study is developed using actual data from Tennessee, New England, and other data sources. It is simulated using ABM, as a proof-of-concept. In addition to the hypothetical case study, two counterfactual scenarios are also simulated. The results show that the decreasing demand for power has a large effect on the adopting of DER. In most areas, the number of customers decrease by almost 50% over the duration of 25 years. Demand from generators decrease, and high-cost generators, like coal-powered generators, are the most affected. The results are illustrative and are expected to change if applied to other service areas. The model requires further work to completely simulate the behavior and decision-making rules of the customers. This research is beneficial to utility companies in responding to the increasing adoption of DER.

As a hallmark of Industry 4.0, collaborative robots (cobots) have been applied in various established industries such as manufacturing. In the construction industry, there is a growing interest and expectation regarding the use of cobots to improve productivity and safety. Although evidence from other industrial applications supports the benefits of cobots in performance improvement, the underlying mechanism remains unclear, let alone the evaluation and validation in construction operations. In this study, we hypothesize that the presence of cobots releases construction workers from less productive motor actions and enables workers to focus on work planning activities, which is more closely related to productivity. To test the hypothesis, we implemented a prototype of human-robot collaboration for a valve manipulation task that is commonly seen in industrial facility turnaround shutdown. A virtual reality (VR) model was created as the experiment platform, where participants (n=20) were asked to operate 24 valves in a given sequence according to the provided instructional information. The efficiency and task performance of test subjects were examined to quantify the benefits of robotic assistance, which was measured with an eye tracker. The results indicate that with the physical assistance of cobots, task performance was improved, and the user cognitive patterns were more preferred as they could allocate more neural resources and attention time on activity planning instead of repetitive motor actions. The findings are expected to set a foundation for better visibility, enhanced responsiveness, and better collaboration in the labor-intensive industry.

Understanding occupant thermal comfort in buildings is important in the context of their physical and psychological well-being. Thermal comfort is a subjective multi-faceted phenomenon that involves the interaction of the body, the mind, and the ambient environment; thus, each occupant typically has different thermal perception in the same thermal conditions. As the importance of occupant thermal comfort estimation has increased, numerous studies have been conducted in this field. In order to accurately model occupant thermal comfort in buildings, human activity levels and corresponding metabolic rate need to be reflected in thermal comfort modeling. However, prior studies heavily rely on an assumed metabolic rate without consideration of actual human activity changes, which may lead to inaccurate estimation of occupant thermal comfort. To address this limitation, this study aims to model occupant thermal comfort in consideration of actual human activities using physiological responses and real-time metabolic rates. For evaluation, case studies were conducted in a real office environment. This research is expected to provide improved understanding of occupant thermal comfort in the context of various indoor activities, which helps control the building energy systems in accordance with occupant thermal preferences.

Sewer pipe systems are of vital importance in the development of cities, especially in the context of highly populated areas where hygiene is a key factor in avoiding large-scale epidemics. However, the deterioration problem led by age is one of the major issues that need regular monitoring and maintenance to keep the drainage system operational at all time. In order to schedule maintenance effectively, an accurate prediction model of the pipes’ condition is required as a reference for prioritization. Given the importance of this assessment, this research proposed a neural network-based approach to automatically predict the condition of sewer pipes. Taking advantage of the availability of both the large volume of data and increasing computational power, a data-driven method is employed in this research. Historical data containing the recorded information of sewer pipes serves as the input of the model, and the output is the condition of the sewer pipes. The final neural network model could serve as a reference for the purpose of efficient scheduling of sewer pipe maintenance work by assigning different priorities to sewer pipes.

Megaproject social responsibility (MSR) and innovation are two important determinants to promote sustainable delivery and performance of megaproject. Nevertheless, there have been few, if any, studies discussing the complex relationships between MSR, innovation, and project performance. Motivated by this, this study aims to fill this gap and contribute to a new area of MSR by analyzing relationships between MSR and project performance, considering the mediating and moderating effect of innovation. Various research methods, including questionnaire survey development, sample selection, data collection, and data analysis are employed to analyze four hypotheses, results show that megaproject social responsibility has a positive and significant effect on project performance, and this relationship is partially mediated by innovation. Meanwhile, the stronger the innovation ability is the stronger the relationship between political responsibility and project performance. Those findings provide contributions to the existing studies that have found inconsistent results on the direct and indirect effect of social responsibility and organization/project performance. In practice, megaproject managers can improve project performance by paying more attention to social responsibility and innovation.

Accurate prediction of short-term electric load plays an indispensable role in improving real-time control and operation of building energy. However, due to various influencing factors such as building thermal environment, weather conditions, and operating hours, there is huge degree of uncertainty and randomness in historical building electric load data containing both linear and nonlinear components, which makes predicting short-term electric load accurately a challenging work. To improve the accuracy of building electric load prediction, the paper proposes a hybrid model incorporating autoregressive integrated moving average (ARIMA) with support vector machine (SVR) optimized by the immune algorithm (IA), aiming to grasp the respective merits of the ARIMA and the SVR. Among them, ARIMA is used to predict linear component of electric load series while SVR is used to predict the nonlinear component of the electric load series. Then, by integrating the prediction results of the two models, the final predicted value of the short-term electric load series is obtained. To examine the precision of the proposed model, experiment on the electricity load (half-hourly data) of a general hospital building in Shanghai obtained from the intelligent building energy support system (i-BESS) of Shanghai Hospital Development Center (SHDC) is conducted. The performance of the hybrid load prediction is verified using statistical metrics, namely, mean absolute percentage error (MAPE) and the root mean square error (RMSE). Empirical results indicate that ARIMA-SVR2 model outperforms other models, including single models (ARIMA and SVR) and the other two hybrid models (ARIMA-SVR1 and ARIMA-SVR3), displaying lowest MAPE and RMSE (0.85% and 4.47). Therefore, the proposed hybrid model can be promoted as a useful tool for efficient prediction of short-term electric load in the future.

There has been an increasing body of research utilizing human trajectory data to capture and analyze human movements. Previous studies demonstrated inherent transition regularities of human mobility with potential high predictability. However, we still have very limited understanding of how the predictability could change under the influence of natural disasters. In this study, we utilize mobility dataset with ample sample size and high resolution under catastrophic events to quantify their disruption on human mobility. We develop a framework adopting information theory metrics and long short-term memory (LSTM)-based predictive model to collectively quantify the perturbation effect on human mobility during Hurricane Irma. Moreover, we associate such effect with the evacuation behavior and investigate the heterogeneity of individuals. Despite observing a universally instant spatial constraint for all individuals, we found that the evacuees were able to withstand their spatial-temporal patterns with higher resilience compared to non-evacuees. We also show that the displacement distance was not significantly correlated with predictability change, hinting that additional economical and sociodemographic factors should be considered to understand the mobility recovery pattern more thoroughly.

Public-private partnership (PPP) is widely used in infrastructure projects. However, due to the positive externality of the projects, it’s common for the private sector to meet rising deficit, which leads to inevitable cost reduction and poor operation performance, as a result subsidy from the government is necessary. This study focuses on subsidy negotiation for PPP projects in deficit, assuming that the private sector takes measures to reduce cost until it receives the subsidy. First, a bargaining-game model is established to describe the process of subsidy negotiation between the government and the private sector, including the discount factors of both parties. Second, the Nash equilibrium solution of the model is derived and the influence of discount factors on on the optimal subsidy is discussed. The results indicate that when the total amount of negotiable subsidy is insufficient, the optimal subsidy and the government saving value decrease in the private sector’s discount factor, otherwise they increase in the private sector’s discount factor. Besides, the optimal subsidy and the government saving value decrease in the government’s discount factor. This study contributes in literature on the determination of government subsidy and provides negotiation guidelines for both parties in practice.

Natural disasters are the result of interactions between natural hazards and complex human factors. Many experts believe, and previous studies imply that vulnerability to natural disasters is linked to socioeconomic characteristics of urban communities. This association can be analyzed from different perspectives. In this study, the association between socioeconomic factors and physical damage to housing units during natural disasters is empirically and statistically examined to test whether the hypothesis that socioeconomic factors are related to vulnerability of residential structures can be supported empirically by available data. Spatial post-disaster housing damage assessment data published by the Federal Emergency Management Agency for Hurricane Harvey in Texas, and Hurricane Irma in Florida both in 2017, as well as socioeconomic data collected by the United State Census Bureau was analyzed using correlation and regression analysis. The results of the analysis did not indicate strong or statistically significant empirical evidence that supports the hypothetical association between socioeconomic factors and damage to housing units. In other words, there is no statistical evidence that indicates housing units in zip codes with different socioeconomic factors such as average income, percentage of minority ethnicities, and population density in Texas and Florida suffered disproportionally during Hurricane Harvey and Hurricane Irma in 2017.

Resilient transportation networks are a critical component of urban societies during and after a disaster, necessary for emergency services, rescue operations, and access to major population and activity centers. Previous studies observed that beside physical damages to transportation infrastructure, post-disaster unusual traffic patterns may lead to gridlock, congestion, and failures in transportation networks. However, little is known about analyzing the impacts of post-disaster unusual human mobility and traffic patterns on the performance of transportation networks. The objective of this study is to empirically and statistically examine two hypotheses: 1) post-disaster unusual traffic patterns perturb the topology of transportation networks; and 2) perturbed topological features of the transportation network affect the network performance. Historical records of taxi GPS traces in New York City were used to examine the two hypotheses on the New York City road network after Hurricane Sandy in 2012. The results of the statistical process control using the exponentially weighted moving average control chart show that post-disaster unusual traffic patterns perturbed the topology of the New York City transportation network and significantly shifted the network betweenness index from its usual variations. The outcomes of the Granger causality test confirm the second hypothesis and indicate that the deviated betweenness index resulted from the perturbed network topology is statistically associated with the network closeness index indicating that the perturbed topological features of the network affect its performance. The outcomes of this study will help decision makers empirically analyze impacts of post-disaster unusual traffic patterns on performance and resiliency of transportation networks.

Natural disasters produce a significant amount of waste that hinders the reconstruction of infrastructure systems. Although many studies have been conducted regarding construction waste recycling post-disasters, sustainable initiatives to integrate hazardous waste during reconstruction phases are often overlooked. For example, quarry rock dust (QRD), a hazardous waste obtained during the quarrying process, is a sustainable, economical, as well as more resilient alternative to sand in concrete structures, and improves the concrete’s strength by ten percent. However, to date QRD is not effectively utilized in construction. The study utilizes the case study of a major earthquake that struck Nepal in 2015 and destroyed more than 491,620 buildings in 105 seconds. The post-disaster reconstruction phase encountered major challenges, especially related to construction raw material availability (like sand), thus delaying the reconstruction to more than three years. The two objectives of this study are to identify: (1) factors that impact the successful utilization of hazardous materials such as QRD and; (2) feasibility (in terms of cost, availability, and practicality) of integrating QRD, as the primary material in the reconstruction of infrastructures to accelerate the recovery phase in future disasters. To achieve these objectives, the research team conducted 115 semi-structured surveys to professionals who are actively working in post-disaster reconstruction in governmental and private organizations. The survey response was then, analyzed using linear regression analysis to determine the impact of identified factors on utilization of QRD in reconstruction. The results of the study: 1) provided post-disasters’ lessons learned and; 2) generated preparedness activities that could be implemented in developing countries. Through this study, the research team was able to communicate with Nepal agencies to reconsider utilizing construction by-products in the aftermath of disasters, thereof accelerating the reconstruction phase.

The recent climate change has led to the development of various clean energy polices and technologies. As a result, the current rapid transition of energy systems due to the development, has the potential to significantly impact on how communities respond to any undesirable climate-related events. However, ‘energy divide’ (inequalities in access to energy services), would be the result of uneven distribution of the clean energy technologies. Empirical analyses of residential electric vehicle (EV) chargers in Seattle, WA, revealed that (1) there is a clustered pattern in the distribution of the EV charger installations, (2) housing stability and economic status explain the uneven distribution, and (3) some neighbors are more sensitive to the economic status and housing stability in terms of residential EV charger installations. The study results revealed social equity issues based on the uneven distribution, i.e., certain communities are underserved for the clean energy technologies. The study results will help policy makers to better support any underserved communities (e.g., those who rent houses and have less finance for EV and charger) under limited resources by devising equitable incentive distributions in response to clean energy technologies.

Construction worksites present numerous health and safety challenges. To address the challenges continuously monitoring the structural health, inspecting suspicious points at regular intervals, and promptly repairing the unstable structures play an important role in civil engineering as much as designing and building structures. This research aims to explore technologies for detecting failures in civil structures during construction. To apply image processing techniques and to facilitate the automated detection of structural failure points, we conducted a small-scale structural experiment for capturing chronological images taken from a video camera. This article examines three traditional and innovative image processing techniques: 1) count of pixels with changed intensities, 2) correlation coefficients, and 3) local correlation coefficients as robust image-based processing techniques. It was found that the local correlation method worked most efficiently, saving the computational time. As a future efficient usage, the local correlation method will be advantageous to developing possible potential algorithms for the automated detection system of failing points of civil structures in the civil engineering field.

“Parking difficulty” is a common problem in metropolitan areas. In recent years, this problem has become more and more serious in not only mega, large cities, and even medium-sized cities in China. Nowadays, to solve these problems, the development of parking garages was developing toward the direction of intelligence and three-dimension. This paper introduces a new-type intelligent stereo parking garage, which adopt the construction method of the prefabricated caisson. It realizes the process of self-service access vehicle by using the handling robot. This novel parking garage has been successfully applied in Haicang Cultural Center. Furthermore, this paper adopts the SWOT method to analyze the popularization and universal application of this garage. The intelligent stereo parking garage has the advantages of a small area, large parking quantity, intelligent access, safe, and efficient. In addition, it could realize different combination from single drum garage to multi-combination drum garage. Besides the good merits, this garage also has some problems, such as traffic streamline organization difficulty, access congestion during the rush hours. All of these will be further optimized in practical application of the next step work.

Despite the broader acknowledgement of shared responsibilities in emergency management, one of the hidden and overlooked issues in disaster literature is the integration of multi-sector stakeholder values: the things that are of importance to the stakeholders (e.g., safety, profit, electability). Stakeholders (e.g., different levels of government, the private sector, the non-profit sector, and the communities) hold numerous values with varying degrees of importance, forming a system of value priorities. Stakeholder values and value priorities—referred to as value systems—are not static in a disaster context; they are dynamic, time-sensitive, and event-driven. A more in-depth understanding of the dynamics of stakeholder value systems is surely needed to allow policy-makers to introduce more pro-active and timely measures towards more resilient communities. To address this need, this paper focuses on identifying and understanding the stakeholder values in the context of Hurricane Michael. Semi-structured interviews (n=24 with 30 interviewees) were conducted to understand what public and private stakeholders value in different phases of Hurricane Michael. Based on the interview results, ten stakeholder values were identified: safety, resource efficiency, natural resource preservation, culture preservation, community growth, community adaptability, community cohesion, social welfare improvement, personal achievement, and business development. This study advances the knowledge in the area of disasters by empirically investigating public and private stakeholder values across different phases of the disaster. Such knowledge will help practitioners implement disaster resilience strategies in a way that accounts for diverse stakeholder needs and priorities, thus facilitating human-centered decision making towards building more resilient communities.

In line with the notion of sustainability as a development concept, prefabricated buildings are gradually becoming an important way to achieve sustainable development in the construction industry. In recent years, China has vigorously advocated assembly-style construction. However, this development is still at the initial stage of promotion, and research in related fields is relatively rare. In addition, research on the evaluation of the sustainability of fabricated buildings is limited. Through literature review and expert interviews, this study used the triple bottom line (TBL) to establish a scientific and rational evaluation index system for prefabricated building sustainability. Combining network analysis and entropy weight methods, research data of Guangzhou City were taken as example, in which the weight of each indicator is obtained. This study investigates the main factors affecting the sustainable development of China’s prefabricated buildings through research and provides a theoretical basis for promoting the sustainable construction and operation management of prefabricated building projects.

Developing nations such as India are facing a major socio-economic challenge of addressing the growing housing shortage. The shortage mainly implies to the low-income groups who reside in congested and inappropriate habitats due to unavailability of affordable shelters. To ensure socially inclusive development, making affordable and adequate housing accessible to all has been the motto of the developing economies. However, promoting massive housing construction programs could create a significant burden on natural resources, energy, water, and land and leads to increased levels of pollution and carbon emissions. It eventually contributes to global warming and climate change, that in turn negates the growth of the economy. Against this backdrop, decoupling economic growth due to housing expansion, from environmental impact is a multifaceted challenge to the construction industry. This paper presents a framework based on system dynamics to facilitate addressing the decoupling challenge associated with the provision of affordable housing in India. System dynamics based modeling facilitates to capture complex interactions and dynamic influences associated with a system and also helps in evaluating various policy scenarios. Hence, the proposed framework helps to evaluate the dynamic interactions between regulatory norms, economic fluctuation, social expectations, and environmental impacts in the process of providing affordable housing. The study shows that about 40 percent reduction in carbon emissions can be achieved in affordable housing projects through suitable decoupling measures and policies. The study concludes that to address the decoupling challenge in the frontier of affordable housing, the policies that reduce the environmental impacts associated with the production and consumption of construction materials should be given due attention along with strategies related to energy consumption during the use phase.

Railway network exhibits significant socio-technical systems features, comparing with other types of critical infrastructure systems. Railway network has higher physical complexity and system complexity with the comprehensive representation of networks. External disturbances have a negative impact on the performance of railway network. Resilient railway network can maintain systems performance in the process of disturbances occurrence, and quickly recover to original performance level after disturbances occurrence. This study reviews previous research on railway network resilience, and gives a new measuring framework and method based on network diversity characteristic. In the empirical study section, we construct China railway network and analyze its topology structure. China railway network resilience are calculated and analyzed at system and node levels. The conclusive points can be outlined as: (1) China railway network system's resilience is 1.7368, and its degree distribution follows power-law distribution, which is resistant to randomized failures and vulnerable to targeted failures of central nodes. (2) China railway network nodes' resilience show significant differences, 82 nodes resilience are higher than network system resilience. (3) China railway network nodes' resilience present a spatially unbalanced characteristic, the average values of China railway network nodes' resilience in eastern region is higher than western region by 1.1%.

The application of the large-scale slurry-balance shield machine in super-large tunnel excavation (diameter>14m) is much more difficult and risky than the common shield tunneling. In this study, a super-large shield tunnel, Wuhan Yangtze River Road-Rail Tunnel (WYR³T) in China, is introduced. Civil information model (CIM) is used to monitor the operation of the super-large shield machine through a thousand parameters. For the site engineers, how to efficiently monitor and interpret the engineering data to guarantee construction safety remains a challenging issue. In order to identify the significant control parameters of the shield tunneling, this study proposed a global sensitivity analysis (GSA) framework for the tunneling data flow in CIM. The results show that (1) the sensitivity results fluctuate greatly, and different soil sections show different input-output correlations, and (2) identifying the most significant and insignificant factors helps to improve the understanding of the model behavior. This research contributes to (1) the body of knowledge by proposing a robust data analysis methodology that can cope with aleatory and epistemic uncertainty, and (2) the state of practice by providing a GSA model to identify the significant control parameters of the shield tunneling.

The technical resilience of an urban road network determines the efficiency and speed of rescue activities after an earthquake. Rescue activities require an efficient urban road network in order to transport important relief supplies, casualties, and rescue teams. It is, therefore, critical to ensure the technical resilience of an urban road network in response to seismic shock. This paper proposes a quantitative methodology for assessing the technical resilience of an urban road network following an earthquake. Travel time reliability is used to measure said technical resilience, presenting changes to this reliability over time. Using the changes in travel time reliability after an earthquake, this paper calculates the loss in technical resilience and three capacities of technical resilience: absorptive capacity, adaptive capacity, and restorative capacity. A test urban road network is simulated to implement the quantitative process. This paper proposes a ranked benchmark of technical resilience loss based on the test urban road network to determine which measure is needed for the test urban road network. These analysis results could help to identify the potential risk of urban road networks in advance, particularly at regular intervals, as well as the vulnerability of a city’s urban road networks, ultimately contributing to the implementation of the proper measures to improve vulnerable areas.

Infrastructure increasing bill, due to failure to fill the investment gap between infrastructure needs and available funds, is still persistent. Many fund-allocation optimization models were developed to find a solution to this everlasting problem. However, the pace of these efforts is not compatible with the fast deterioration of infrastructure due to its vulnerability to exogenous factors that accelerate its deterioration beyond the expected rate. There is a lack of research efforts that have formulated infrastructure vulnerability in the prioritization and fund-allocation algorithm. Accordingly, this research proposes a fund-allocation optimization model that maximizes infrastructure physical performance under budget constraints, considering a new vulnerability-based stochastic deterioration modelling. The model computes first an overall vulnerability index, for each asset in the network, which is function of the attributing factors that may vary from one geographical location to another. The vulnerability index is then incorporated in a Markov-based deterioration behavior modelling to include the vulnerability impact in the fund-allocation algorithm. In this research, the proposed model is applied to a road network as one type of infrastructure to examine its performance. Thus, an empirical study was conducted to capture the exogenous factors that would make roads vulnerable to faster deterioration, including: excessive traffic loading, climate change, neighboring disturbance, etc. Applying the model and comparing it against the existing models, results demonstrated rationality behind the generated funding decisions using the proposed model, and the cumbersome consequences of ignoring vulnerability. Thus, the model can help policy-makers make realistic funding decisions to maintain infrastructure performance.

The integration of renewable energy resources and hybrid plug-in electric vehicles (PHEVs) to the power grid plays an important role in responding to the increasing demand for electrical energy all over the world. In this paper, the authors aim to propose an improved scheduling model for home appliances and PHEV considering battery pack degradation cost and incentives for the vehicle to grid (V2G) service. The significant number of PHEVs provides an opportunity for storing electric energy in residential buildings and supplying the grid energy shortage. The stored energy can be delivered to the power grid at peak hours, which is called V2G service. It is clear that although V2G service provides attractive revenue opportunities, operating the electric vehicles in V2G mode imposes extra degradation cost on owners; therefore, battery-pack degradation cost in V2G service should be considered in the optimal scheduling of its operation. A semi-empirical lithium-ion battery degradation model is used for degradation cost analysis of PHEV battery pack due to the characteristics such as accuracy and low computational load. The proposed model is implemented on a case study to investigate the influence of degradation cost and incentives on the operation of PHEV in charging or V2G mode.

In recent decades, natural disasters and financial crisis have been the catalyst to the conception of sustainable development and resiliency fundamentals to help cities become more resilient to natural disasters and economic turmoil. Among many resiliency fundamentals, socio-economic resilience (SER) is a critical component for a resilient city. Many researchers and organizations have developed measures for SER assessment, however, usage of a standardized metric to assess cities has not been sufficiently addressed in previous research work. This research study provides a comprehensive standardized framework for measuring SER that directly and indirectly affects citizens. The research study is built on the SER measures captured in previous literature and validated by experts in the field of socioeconomic resilience to develop key performance indicators (KPIs) that can serve as a standardized metric for SER. Some of these KPIs are income level, unemployment, percentage of the population having access to social protection, and the percentage of people having access to public transport. For any given city’s SER, these KPIs are rated over an informed numerical scale and the total points are calculated (SER score) to provide a platform for comparing different cities in terms of their strengths and weaknesses of SER using this standardized metric. The outcome of this research provides a standardized performance metric for SER through an intense assessment of KPIs, followed by calculation of a total SER score for different cities. This research study strives to provide an experts-validated quantitative assessment metric that implores state governments, city officials, and the citizens to collectively improve the overall social and economic resilience.

With the growing construction works over the world, the waste generated from construction and demolition is increasing at an enormous rate. In India, 50 million tons (MT) of construction and demolition waste (CDW) was generated in 2013. With the passage of time, several obsolete and structurally weak buildings in various cities will need to be demolished and reconstructed under several government schemes of redevelopment. Thus, it is expected that a huge amount of CDW will be generated in near future which may lead to several environment issues. The CDW management is a broad area which includes various domains such as estimation and quantification of CDW, reducing, reusing, and recycling (3Rs) of CDW and its management in general, role of human factors on CDW practices, demolition and logistics management of CDW, and its life cycle analysis. These issues are interrelated and should be studied in a holistic manner. This paper attempts to conduct a holistic review of CDW from practical, managerial, and research point of view. The study uses the science mapping approach to review 476 journal papers indexed in the web of science and published during 2000 to 2019. Outcome of this study could benefit researchers, academicians, and industry consultants in enhancing their knowledge of construction and demolition waste management.

Green construction has been able to mitigate the substantial negative impacts of the construction industry on the triple bottom lines of sustainability. However, existing attempts for sustainable construction are not sufficient to compensate the massive negative effects of construction processes on the environment, society, and economy. The fundamental requirement to further progress sustainable construction is to identify its barriers and mitigate them. In doing so, concerns and limitations of primary parties who are involved in the construction industry must be identified. The present study serves two purposes, 1) ranking the barriers of sustainable construction in the United States based on subject matter experts’ perspectives, and 2) determining the share of owners, contractor, and government-related barriers that hinder continuous improvement of sustainable construction. Analysis of the collected data demonstrated the following as the primary barriers, 1) owner’s unwillingness to pay the extra initial costs of green buildings, and 2) contractors’ disinclination to consider a longer project delivery. The research also revealed that, compared with the other project players, barriers related to owners are more impactful in hindering sustainable delivery of construction projects. The findings are expected to facilitate further adoption of sustainability in the construction industry.

Green infrastructure solutions like bio-retention systems, permeable pavements, and water harvesting programs hold great promise for urban stormwater management. However, these solutions are held back by status quo bias during the engineering design and construction process. Status quo bias inhibits solutions that do not align with industry norms. The research presented in this paper tests an approach to neutralize the harmful effects of status quo bias. Twenty engineering graduate students were asked to evaluate two decision scenarios about stormwater management. Each scenario included one green and one grey design and construction option. Half of the students were randomly chosen to receive additional information about a newly approved municipal resolution endorsing green infrastructure. The purpose of the resolution information was to change perceptions about status quo by acting as a formal, social endorsement for green over grey infrastructure. The resolution information reduced the students’ perceived risk associated with green infrastructure and increased their willingness to select green infrastructure as an engineering solution. The change in risk perceptions was also observable in their brains. Students wore a neuroimaging cap to measure brain activation in regions associated with risk processing. When given information about the green infrastructure resolution, students reduced processing in the region of the brain associated with risk aversion. This study offers neuro-cognitive and behavioral evidence demonstrating how the green infrastructure resolutions can help overcome status quo bias in stormwater management.

In an age of constant disruptions from various sources, including chronic stresses (e.g., climate change, population growth) and acute shocks (e.g., earthquakes, floods), human resilience capacities to cope with and adapt to changes are becoming more and more important. The resilience capacities of humans are greatly affected and shaped by the environments in which individuals grow, live, and work. Transportation infrastructure, together with other utilities and buildings, create the physical living environment and contribute to the development of human resilience to disruptions. Despite many existing researches on human resilience and transportation infrastructure planning respectively, a holistic understanding of the relationships between transportation infrastructures and human resilience is still missing. Therefore, the objective of this study is to build a conceptual framework for the influencing mechanisms of transportation infrastructure on human resilience. The conceptual framework was developed using concept mapping based on qualitative data collected from existing literature. The proposed conceptual framework provides a theoretical foundation for investigating and quantifying the impacts of transportation infrastructure on human resilience. It could help decision makers and practitioners to better plan and design transportation infrastructure towards resilience individuals and communities.

Drinking water infrastructure in the U.S. is in a deteriorated state needing immediate intervention that is sustainable. Although many technologies are being developed to inspect buried pipeline assets, they are still expensive and human-dependent to use for comprehensive condition assessment and prioritization of the most critical assets for immediate rehabilitation and replacement planning. This paper presents a novel system-level condition assessment framework where monitoring data from distribution infrastructure is leveraged to predict the condition of assets using evolutionary optimization and machine learning algorithms. Pipeline roughness values and effective hydraulic diameters (given the possibility of graphitization/corrosion) are two parameters that would reveal their overall condition, and therefore these two parameters will be used to demonstrate the framework presented in this paper. In this respect, a modified benchmark water distribution network is used to represent an ageing, deteriorated network by randomly reducing effective pipe diameters and roughness coefficient values. Subsequently, a novel reverse engineering optimization method is leveraged to minimize the mean square errors of operational parameters (e.g., pressure and flow) via both predicted (through optimization) and modeled data obtained from a given set of monitoring stations. Roughness values and effective hydraulic diameters are the decision variables in this optimization framework that are to be predicted. EPANET 2.0 software is used for modeling the water distribution network performance in this study. Faster convergence is achieved through fine-tuning of genetic algorithm properties. Specifically, the computational efficiency and prediction accuracy benefits derived from appropriately narrowing down on the upper and lower bounds of the decision variables through multiple runs of the optimization process will be demonstrated in this paper. The framework proposed in this study offers great analytical capability to predict the condition of various assets in a water distribution network without having to undertake expensive inspection procedures.

Inspection on curved continuous rigid frame bridges (CRFBs) is necessary but challenging due to the complexity of bridge structures. Revealing the “true” condition of CRFBs requires accurate field observations and reliable interpretation method to interpret the observed spatial changes. Non-contact vision-based systems (e.g., LiDAR) offer a promising alternative for structure change detection. Unfortunately, appropriate interpretations of the detected changes of CFRBs is challenging due to coupling deformations of connected bridge elements. Unreliable interpretations of change correlations of bridges can profoundly affect the effectiveness of maintenance planning. Finite element models (FEMs) developed based on the as-designed conditions could help explain how structural changes influence each other. Updating parameters of FEMs could simulate how deteriorations of structural elements influence the patterns of correlated structural changes. Unfortunately, FEM updating can be a multiple-solution problem—multiple possible structural properties and boundary conditions could produce similar geometric shapes of the structure under the same loading condition. In many cases, the combinations of FEM parameter values that form the solution search space can be exponentially large. Examining the exponentially large number of combinations of FEM parameter updates is computationally intractable for large structural systems. As a result, existing studies that use field data for updating FEM and interpreting structural changes could only handle structures with a few elements in controlled environments. This study established a method for FEM updating based on vision-based correlated change analysis. The method focuses on integrated used of change correlation analysis and FEM simulations for reducing the computational complexity. The results indicate that the correlated change analysis provides the potential of eliminating possibilities of certain combinations when updating the FEM for improved computational efficiency.

Natural gas is becoming increasingly popular across the globe due to its growing affordability and lower carbon footprint compared conventional fossil fuels. It is also suitable for distributed power generation using combined heat and power (CHP) units. The continuous functioning of a gas distribution system is therefore crucial. Majority of the studies on reliability assessment of gas networks have studied the networks in the aftermath of the earthquake using connectivity-based metrics, while this paper presents a flow-based quantification method to evaluate the reliability of city-level gas distribution networks. A representative gas distribution network is initially designed for an urban area that is prone to liquefaction. Seismic reliability of the designed network is subsequently investigated for a hazard of magnitude M_w=7. The reliability metric used in this study is the fraction of demand satisfied immediately after the seismic event. Appropriate fragility functions for various components of a gas distribution network have been identified from the literature and used in a Monte-Carlo simulation (MCS) approach for reliability assessment. A computationally efficient, physics-based linear-pressure analog (LPA) model has been developed and used to solve the steady-state flows in the gas distribution networks. The novelty of this work lies in the use of LPA model that will minimize the computational effort associated with the numerous iterations in MCS as it leverages linearized formulations of the physics-based gas network dynamics. This work provides a framework which can be used to advance the reliability assessment of city-level gas distribution networks. The framework presented in this paper can be used to improve restoration and rehabilitation strategies for gas networks in order to enhance their performance during earthquakes.

Bus transit acts as a significant catalyst in the process of sustainable and resilient urbanization. However, within any given urban agglomeration, residents do not benefit from the transit services on an equal footing. This is particularly the case for socially disadvantaged groups such as seniors, children, and low-income households. Generally, a walking distance of 400 m is used as a walkable distance threshold for bus stops in public transit design practice and age-friendly city guidelines. Unfortunately, due to irregular transit service regions or a dispersed distribution of bus stops, age-restricted communities which have become a popular residential option for older adults are not necessarily built in locations meeting the 400 m-criterion and thus provide limited accessibility to nearby bus stops. In this context, this paper aims to apply integer linear programming (ILP) to help redesign the existing bus route (or route segment) surrounding the locations of age-restricted communities in order to minimize the operation cost for transit agencies under a series of constraints including satisfying accessibility requirement of bus transit to seniors. Finally, a numerical example, based on the grid street pattern, is illustrated to demonstrate the applicability of the proposed method.

Automating the translation between point cloud data and building information model (BIM) has been an interest of construction industry. Recently, with the development of novel scanning technologies, comparing the applicability of the point clouds produced by these technologies and investigating the potential of automatic workflow have become possible. Previous research has been devoted to investigating the methodology of point cloud segmentation and point cloud comparison. However, there has not been sufficient study into the workflow of implementing those methods and integrating them with available technologies. The aims of this study were to develop a semi-automatic workflow of converting point cloud data to Autodesk Revit BIM model, which represents the locations and the dimensions of key structural elements (e.g., ceiling, floor, and walls, etc.). While the traditional method is to use rule-based algorithm to complete classification, recent advancement in machine learning research has shown promising future in these areas. A hybrid approach that uses machine learning algorithm to perform transformation and deterministic algorithm to perform parameterization is proposed. We also developed a close-loop workflow based on existing BIM model to calibrate the segmentation and parametrization algorithm. The semi-automatic work represents a significant reduction in time consumed to produce the structural BIM representation. This innovative workflow shows a promising future of fully automatic workflow in the construction industry.

Current electricity demands, increased regulatory requirements, and growing populations require innovation in power production and delivery. Cost overruns of power and pipeline construction projects have the potential to lead to the loss of the economic justification for the project. Project performance and the profitability of power and pipeline construction projects often look different in a regulated environment than a non-regulated environment. This is hypothesized to be due to the Averch-Johnson effect. The Averch-Johnson effect is produced when fair rate of return regulation encourages a firm to invest more than is consistent with the minimization of its costs. This research seeks to understand how capital investments vary for power and pipeline projects in both regulated and non-regulated environments by analyzing 1917 projects in the Independent Project Analysis, Inc. project database. The goal of this research is to present a statistical analysis of cost performance across different project types and project sectors to assess if the Averch-Johnson effect holds true. The results of this paper found that regulated power and pipeline projects were overestimated as compared to non-regulated projects of similar scope.

With the increasing need of sustainable development globally, many world-leading construction companies have devoted great efforts to implementing sustainable practices. RobecoSAM publishes Sustainability Yearbook annually since 2004, which lists the sustainability leaders in various industries, including construction industry. Learning from those sustainability leaders in construction industry can provide useful references for construction related companies when developing their sustainable development strategies. Based on comprehensive literature review, this paper thus identified 51 methods used for improving sustainability performance and 34 outcomes achieved through these methods. The methods and outcomes are used for coding the sustainable practices of sustainability leaders in construction sector. With the coding system, sustainability reports issued by sustainability leaders in construction sector were analyzed with content analysis. Then, social network analysis was employed to identify the key adopted methods and achieved outcomes (KAMAO) from these leaders. The findings provide valuable guidance for international contractors to have a better understanding of the top sustainable methods adopted by sustainability leaders in construction sector and the top outcomes achieved by these leaders.

Technical advancements such as building-integrated renewable systems and autonomous vehicles (AVs) require existing electricity grids to be transformed into adaptable microgrids considering future energy supply and demand. Interactions between energy demanders and suppliers should be predicted to quickly respond and adapt to future demands or uncertain events. However, communities consisting of buildings, transports, and people are complex and their interactions are difficult to be recognized. This research investigates the interactions among elements in communities featuring AVs and solar photovoltaics (PV)-integrated buildings. A framework for establishing smart energy networks that can be adjustable spatially and temporally is developed by communicating buildings and vehicles. The methodology is tested in buildings and road networks in Kyojima 1, North Sumida, Tokyo, Japan. Existing energy demands and transportation systems are reviewed to establish future scenarios for applying AVs and solar PV buildings. The scenarios are analyzed using community clustering and Voronoi diagram algorithms, in which sustainable community boundaries are examined using prediction data from parametric energy modeling and spatial network analysis. The framework can support decision-making for future community boundaries, thereby distributing energy securely and efficiently. This research will also contribute to preparing communities to share energy information and to achieve resilient and sustainable infrastructure systems.

Despite the extensive use of positioning and navigation in outdoor space, indoor positioning and navigation systems, essential for intelligent building and smart city services, are unsatisfactory in either performance or price, sometimes in both. This paper analyzes and compares the performances and prices of existing indoor positioning technologies that are categorized into a few classes according to their spatial sensing and referencing methods. Based on the previous work in walkability, this paper proposes a novel walkability network-based augmented reality (WaNAR) method using smartphones with AR positioning function for positioning and navigation. In WaNAR, drifting of the AR positioning signals are corrected continuously by the ground-truth 3D indoor/outdoor walkability network (e.g., nobody is supposed to walk through a wall) in a 3D model. The error at the vertical axis of the walking direction is corrected continuously and that of the walking direction is compensated at every turn. WaNAR can be used in both indoor and outdoor navigation, its performance and price are proved to be largely improved compared to existing technologies. The only investment for a typical building is a 3D drawing of indoor walkable space in a few staff-hours. WaNAR has broad application prospects at various positioning and navigation scenarios.

The provision of air transport services in under-served and un-served airports is a major concern worldwide. This problem could be overcome by increasing the number of passengers and routes to those airports. In spite of India being the third largest domestic civil aviation market in the world and many government policies being implemented, the regional airports are not sustainable. In this background, the paper applies system dynamics to develop a regional connectivity model that helps to plan, manage, and financially support the airports for their sustenance and growth. The factors that contribute to regional connectivity are listed using literature survey and pilot studies. Their structural relationships are sketched using causal loop diagram, which characterises the model behaviour. The main factors are population, tourism, and economic potential of the region, the growth of low-cost-carriers, and the level of airfares. The second level output, stock, and flow diagram provides dynamic patterns and describes them in terms of structural relationships between various factors. This model helps to identify the conditions under which regional connectivity will evolve. This can help policymakers to foresee patterns and causes of dysfunction in the airports.

A sustainable building performance requires the efficient use of resources while providing a comfortable and healthy environment for building occupants. While energy efficiency and environmental comfort metrics are commonly studied in the literature, they are mostly evaluated independently, potentially overlooking conflicting relationships that may exist between them in actual buildings. This paper presents a novel post-occupancy evaluation and energy modeling (POEEM) framework that overcomes the mentioned gap by combining the capabilities of post-occupancy evaluation (POE) and building energy modeling (BEM) to comprehensively assess the impact of energy conservation strategies on both buildings and their occupants. The framework consists of four main stages that include: (1) data collection on building design, performance, and feedback from occupants on the quality of their indoor environmental conditions, (2) building energy modeling and calibration to simulate current energy consumption levels, (3) statistical modeling of occupant-focused metrics such as comfort and perceived productivity, and (4) integrated evaluation of the previously-developed models to test strategies that minimize energy consumption without compromising occupants’ comfort and working conditions. In this paper, the framework is illustrated and validated through a case study of a green office building located in Abu Dhabi, UAE, where the authors assess the impact of alternative lighting intensities on building energy use, reported occupants’ comfort, happiness, and productivity levels. The results indicate that lighting energy levels can be reduced by up to 20% without compromising any of the studied occupancy metrics, confirming the potential of the proposed framework to identify occupant-centric strategies that improve building performance holistically.

One significant obstacle improving energy efficiency of buildings and reducing greenhouse gas emission is a lack of understanding occupant behavior in buildings. Existing studies focused on investigation of occupant behavior at the individual level. Besides, significant differences between females and males regarding energy-related behaviors have been highlighted in many studies. However, group-level energy behaviors and gender difference in buildings have received limited attention so far but remain of paramount importance, because most occupants stay in shared space. To fill this research gap, this research investigated the characteristics of group-level energy behaviors for males and females under shared internal space respectively. Data concerning energy use and energy-related behaviors were collected from energy management system and questionnaire respectively. The results showed that there were significant differences in building energy consumption between male and female occupants. Regardless of gender, the group members who occupied the room longest have highest correlation with the group energy consumption and reflect the ratio of group-level occupancy in rooms. For males, the characteristics of most group-level behaviors in shared space mainly depend on the average level of all group members’ personal related energy behaviors. Contrary to the males, the results show that the features of most female group-level behaviors are more likely to be explained by the group members who perform worst in the group. The results obtained could help quantify the impact of occupant behaviors on energy consumption with empirical data, and thus improve occupant behavior for energy conservation in buildings at group level.

The successful implementation of BIM (building information modeling) could bring a significant amount of benefits for building projects, such as better visualization, reduced amount of rework, and improved productivity and quality. However, the implementation of BIM for infrastructure in horizontal infrastructure projects is much behind compared to BIM in vertical building projects. There is only a small number of horizontal infrastructure projects that utilize BIM for infrastructure in China. Many agencies still do not have strong beliefs in the benefits of using BIM for infrastructure. Meanwhile, the number of research projects conducted on BIM for infrastructure is also much smaller than the ones conducted on BIM. Thus, the purpose of this research is to investigate the implementation of BIM for infrastructure in Jiangsu, China, through case studies and interviews. Two utility tunnel projects using BIM for infrastructure are studied in particular. The real applications of BIM for infrastructure as well as the perspectives of the BIM experts and the client on BIM for infrastructure (i.e., its differences from BIM for buildings, its costs and benefits, and its future in China) are summarized. In addition, the observed benefits and challenges of using BIM for infrastructure are concluded accordingly.

Renovating existing buildings to become more energy efficient is critical not only for energy sustainability but it also provides financial benefits and can improve the well-being of occupants. Nevertheless, the current energy efficiency efforts in the building sector are insufficient for decision making in a portfolio of buildings. Despite the growing literature, the context-driven reasons for decision-makers to undertake or to neglect energy efficiency retrofits are still not well understood. The goal of this empirical study is to present the motivations and challenges that influence energy efficiency investment decisions for existing buildings in varying contexts when evaluated towards multiple buildings. The study entails a literature review and a descriptive analysis of 283 survey data by facility management professionals across the United States (U.S.). The study seeks to understand how the building physical, functional, and social contexts may differently impact retrofit decisions. In general, the survey results indicate that the key drivers and barriers to implementing energy efficiency retrofit are related to economic feasibility and technical practicality. When the data were examined based on contexts, the analysis revealed that building physical, functional, and social contexts in this case study did not significantly influence the ranking of the three most important factors. The outcome of this research will help policymakers and experts understand the barriers that need to be addressed, as the study indicates that the identified factors could be generalized to a different group of buildings regardless of the contexts. Developing effective intervention strategies should focus on better economic incentives for a shorter payback period to make more attractive returns and reduce operational costs. Additionally, the availability of affordable and compatible technologies will encourage the energy efficiency retrofit investment during the maintenance period.

Interconnection between power infrastructure and gas networks is of great importance especially with the occurrence of restructuring in such energy systems, which can be investigated by evaluating the effect of each network on the performance and efficiency of the other interconnected network. This paper aims to propose a robust energy management framework of integrated power infrastructure and gas networks considering the effect of renewable energy sources and gas/non-gas fired power generation plants. The main objective of the proposed robust framework is to provide a robust decision-making process against uncertainties associated with power system parameters such as energy market prices. Consequently, the robust optimization method will be implemented to study the uncertain parameters of the integrated network. In addition, the proposed model aims to investigate the effect of interconnectivity between power infrastructure and gas networks (i.e., power system and natural gas network constraints) on the daily schedule of power generation plants, gas suppliers, and performance of the networks. The proposed model is applied to a 6-node natural gas network and a modified 6-node electric power system with the penetration of renewable energy sources to investigate the performance of the proposed model.

Designers are crucial professionals to incorporate green principles into the design process to mitigate most of the environmental impacts produced in the construction industry. Green regulations and incentives have been implemented to promote designers’ green design behaviors. However, they are not enough to stimulate designers’ self-initiated and voluntary conduct for green design and to induce more persistent and deeper changes. To narrow down the gap, the research, on a new social-psychological level, investigated how designers’ social relationships work on their green design intention through attitude. Then, with the data collected from 179 respondents by a questionnaire survey, partial-least squares structural equation modeling (PLS-SEM) was employed to validate the hypotheses. The results show that designers’ social relationships positively correlate with green design attitude and intention, and attitude mediates the relationship between social relationships and green design intention. Furthermore, three aspects of social relationships have different effects on attitude and green design intention. The findings imply that enhancing designers’ social relationships is an effective approach to foster attitude and encourage green design intention. Correspondingly, referential proposals were developed for policymakers and organizational managers at the end. The research is valuable for the sustainable development of the construction industry.

Pavement condition monitoring is important for scheduling maintenance activities. Approaches to gathering pavement condition data can vary from visual inspection techniques to using advanced pavement profilers. Common shortcomings of these include high costs and high skill requirements. Another method is to use accelerometer data collected from smartphones to estimate the pavement condition. In this research, an Android application is developed to use acceleration vibration to estimate road condition. Fourteen pavement sections were selected on which to conduct test runs. Pavement condition index (PCI) data from city of Houston road network database is used to train the estimation model in the Android application. Multiple linear regression models were proposed using statistics extracted from acceleration data as explanatory variables. Furthermore, to test the repeatability of observations, two random pavement sections were selected to perform three test runs. It is observed that, except for standard deviation and variance among vertical acceleration data, other statistics showed high repeatability. Results from this research show that acceleration vibration has a good linear relationship with PCI ($R^2$ value 0.85 to 0.9). The results of this study can be useful for low-cost continuous monitoring of road network conditions.

Energy efficiency in the building stock is a substantial contributor to infrastructure sustainability. In Latvia, the residential buildings’ thermal energy use for space heating accounts for 80% of total energy use. Therefore, reducing thermal energy consumption for space heating needs through the implementation of energy efficiency measures, local codes, and regulations can ultimately lead to cost savings for building owners and stakeholders. This paper presents a study on the effect of Latvian building codes related to energy efficiency on the thermal energy savings in residential, public, and industrial buildings. Three scenarios were developed based on three different protocols with a 10-year forecast analysis. Evaluation of the proposed building code implementation practices and their feasibility in Latvian building stock is discussed for these buildings with regards to thermal energy savings potential.

The abundant generation of construction waste poses a major challenge to the sustainable development of China's resources. The resource utilization of reclaimed asphalt pavement (RAP) is an effective measure to alleviate this challenge. This paper aims to investigate the determinants of RAP resource utilization behavior in mainland of China. Based on the theory of planned behavior (TPB), three contextual constructs (i.e., governmental supervision, potential benefits, and project constraints) were introduced for modeling the RAP resource unitization behavior. Based on the initial model, eight constructs were identified and eight hypotheses were proposed. Data were collected through questionnaire survey, a structural equation modelling (SEM) analysis was conducted to test the proposed hypotheses. The research findings show that perceived behavioral control, project constraint, and behavioral intention have a significant positive impact on RAP resource utilization behavior, and the path coefficients from large to small are perceived behavioral control (0.497)>behavioral intention (0.402)>project constraint (0.229). Nevertheless, the government supervision has a significant negative impact on behavior (-0.194) while potential benefits is insignificant determinants for adopting RAP utilization behavior. The research provided a theoretical basis for guiding the stakeholders’ better implementation of RAP resource utilization measures.

Adaptive reuse of buildings is considered a visionary practice and a superior alternative for new construction, in terms of sustainability. It is considered key for transitioning from a resource-based construction economy towards a circular one. Current approaches to support project design, planning, and execution with building information modelling (BIM) are insufficient to support adaptive reuse projects. BIM is insufficient when we think of adaptive reuse as a flow of building materials and components through a circular value chain, and when we conceive of existing assets as the future source of construction materials. In this paper, we show with examples that current BIM models do not support important project activities of adaptive reuse projects. Then, we identify needs and requirements for information models that support these activities. The requirements focus on how to effectively represent parts, materials, and systems, as well as, interfaces between them. We will also suggest additional properties that need to be defined in BIM models for adaptive reuse projects. The main contribution of this study is the development of a framework for an integrative BIM approach for improving adaptive reuse projects outcomes inside of a circular economy (CE) context.

During and after disasters, highways provide vital routes for emergency services, relief efforts, and evacuation activities. Thus, a timely and reliable assessment of disaster impacts on highways is critical for decision-makers to quickly and effectively perform relief and recovery efforts. Recently, social media has increasingly been used in disaster management for obtaining a rapid, public-centric assessment of disaster impacts due to its near real-time, social, and informational characteristics. Although promising, the employment of social media for assessing disaster impacts on highways is still limited due to the inability of extracting accurate highway-related data from social media. To overcome this limitation, a systematic approach is proposed to identify highway-related data from social media for assessing disaster impacts on highways, and a case study of Hurricane Harvey in Houston, TX, is employed for the demonstration. The approach is constructed through three steps: (1) building data sources for social media and highways of interest in Houston, respectively; (2) adapting the social media data to each highway through a developed mapping algorithm; (3) assessing disaster impacts through analyzing social media activities in terms of their intensity, geographic, and topic distributions. Results show that the proposed approach is capable of capturing the temporal patterns of disaster impacts on highways. Official news and reports are employed to validate the assessed impacts.

Cambodia suffers from devastating droughts in the dry season and floods in the wet season. These impacts can partly be attributed to ineffective irrigation systems and water resources infrastructure that are unable to retain water throughout the dry season. Water harvesting—i.e., the collection and management of floodwater or rainwater runoff to increase water availability for domestic and agricultural use as well as ecosystem sustenance—is one approach that could help improve Cambodia’s climate resiliency. This research uses a combination of geospatial analysis and analytical hierarchy processes to determine suitable sites for water harvesting reservoirs. To determine ideal sites for water harvesting reservoirs, pairwise comparisons were performed between: soil drainage, geologic porosity, precipitation, and slope. Model weights were assigned to decision parameters based on prior water harvesting studies. A preliminary model showing suitable sites for water harvesting reservoirs was developed. This preliminary model can help guide future water infrastructure projects aimed at improving climate resiliency by identifying what sites are suitable for water harvesting reservoirs.

Economic shocks occur and resilient communities must have resources in place to anticipate, mitigate, and recover. During these shocks, social capital is a fundamental resource to a resilient community. This research seeks to explore the relationship between social capital and resilience to economic shocks. Using county level data, 2,666 counties experienced an economic shock between 2008 and 2009. A new framework integrating bonding, linking, and bridging social capital provided a theoretical structure using data from the Census Bureau and the Bureau of Labor Statistics. Correlations then were conducted to measure the relationship between various social capital measures and an area’s ability to recover following an economic shock. This research expands understanding of community features that lead to economic resilience. Preliminary correlations amongst bonding variables found educational equality and the percent of the population under age 65 as having the strongest positive relationship with economic resiliency. Additionally, the number of religious organizations per 10,000 people and the percent of union membership from the linking variables have a negative relationship with economic resiliency. Finally, the percent of the population eligible to vote had a negative relationship with economic resiliency. This research examines how social factors can influence economic matters, highlighting the importance of examining all aspects of a community to researchers and policymakers to produce a resilient economy resistant to economic shocks.

Significant amount of resources such as materials, energy, water, and land are consumed during the design, construction, operation, maintenance, and end-of-life phases of buildings. There is an increased focus on reducing the environmental footprints associated with buildings. Green building ratings are used as practical tools for implementing sustainability in construction projects. There are several green building rating tools used across the globe. In India, three green building ratings are used, namely the U.S. LEED, IGBC LEED, and Green Rating for Integrated Habitat Assessment (GRIHA). The certification levels vary across green building ratings. For example, the U.S. LEED rating ranks buildings at “Platinum”, “Gold”, “Silver”, and “Certified”. Similarly, the GRIHA rating ranks buildings at “5 Star”, “4 Star”, “3 Star”, “2 Star”, and “1 Star”. This study examines the relationship between certification levels and sustainability indicators of GRIHA certified green buildings in India. Sustainability indicators such as the energy performance index (EPI), energy use reduction, and water use reduction are considered. The analysis is based on the data collected for 45 GRIHA certified green buildings. This study presents some insights on the relation between certification levels and sustainability indicators of green buildings.

The 2030 Global Sustainable Development Agenda of United Nations highlighted the critical importance of understanding the integrated nature between enhancing infrastructure resilience and facilitating social equity. Social equity is defined as equal opportunities provided to different people by infrastructure. It addresses disparities and unequal distribution of goods, services, and amenities. Infrastructure resilience is defined as the ability of infrastructure to withstand, adapt, and quickly recover from disasters. Existing research shows that infrastructure resilience and social equity are closely related to each other. However, there is a lack of research that explicitly understands the complex relationships between infrastructure resilience and social equity. To address this gap, this study aims to examine such interrelationships using social media data. Social media data is increasingly being used by researchers and proven to be a reliable source of valuable information for understanding human activities and behaviors in a disaster setting. The spatiotemporal distribution of disaster-related messages helps with real-time and quick assessment of the impact of disasters on infrastructure and human society across different regions. Using social media data also offers the advantages of saving time and cost, compared to other traditional data collection methods. As a first step of this study, this paper presents our work on collecting and analyzing the Twitter activities during 2018 Hurricane Michael in disaster-affected counties of Florida Panhandle area. The collected Twitter data was organized based on the geolocations of affected counties and was compared against the infrastructure resilience and social equity data of the affected counties. The results of the analysis indicate that (1) Twitter activities can be used as an important indicator of infrastructure resilience conditions, (2) socially vulnerable populations are not as active as general populations on social media in a disaster setting, and (3) vulnerable populations require a longer time for disaster recovery.

Construction waste, as a by-product of construction activities, has raised great concern worldwide due to its adverse effects on the environment. Extensive studies have been conducted to devise various strategies to improve the construction waste management performance. Various measures guided by the 3R principles have been proposed in the last few decades, which has indeed relieved the construction waste issues. However, with the increasingly severe construction waste issues in the predictable future, it is also highly necessary to have other innovative strategies in addition to the 3R principles as handling construction waste issues is a systematic project. Amid all the strategies, the management of inert portion, termed as construction waste material (CWM), occupying the predominant volume in the total construction waste has received increasing concerns. Now, a promising solution is to boost the CWM cross jurisdictional trade, which very few studies have been conducted. However, the de facto extent of this trade is still too modest and the barriers against it are still yet to be known. Therefore, this study aims to bridge the research gap by investigating the potential barriers to CWM trading through adoption of political, economic, social, technological, environmental, and legal (PESTEL) framework with a case study of Greater Bay Area (GBA) in China. It is discovered that insufficient political and economic incentives, weak public consciousness, lack of labelling of compliant materials, asymmetric information, and imperfect legal system have hindered the development of CWM trading. Through the case study of GBA, some significant hints and references can be obtained for GBA and beyond to devise more targeted strategic framework to boost the CWM cross jurisdictional trade so that CWM issues can be generally relieved if not fully eliminated. The research deliverables are of benefit for various jurisdictions to relieve the long-lasting construction waste issues.

Leaning utility poles are often overlooked in maintenance. Once a utility pole leans to one side, it generates a non-uniform stress distribution on the ground, which leads to decreased structural stability. Therefore, even if leaning poles have not fallen for the present, they pose more potential probability of failure than straight poles. Despite the significance, the extent to which leaning poles are vulnerable to wind loads has not been fully explored. This paper aims to evaluate the probability of failure of leaning poles under extreme wind loads. For analyzing the structural reliability, two failure modes are considered: flexural failure due to wind load and overturning failure by leaning. Based on the two failure modes, fragility curves for utility poles with respect to the angle of the leaning are developed. The developed fragility curves were used to identify potentially vulnerable poles in power distribution infrastructure networks in Houston, TX, when potential extreme wind events occur. The proposed method will benefit practitioners by facilitating effective risk-informed decision-making to prioritize maintenance tasks over numerous leaning poles in power distribution infrastructure systems before hurricane seasons, which helps enhance the community resilience to cascading blackout.

Population dynamics can impact water and wastewater infrastructure and lead to changes in residential water pricing. For instance, cities experiencing urban decline must fund the costs of water systems despite fewer customers. This study seeks to assess the association between water and wastewater affordability and city attributes. Statistical modeling is used to examine the link between different classifications of cities (shrinking, stabilizing, and growing) and the median affordability index (service charge as a percentage of median household income). Data for this study is from the 2016 American Water Works Association’s Water and Wastewater Rate Survey, spanning utilities in 272 North American cities. Additional data comes from publicly available sources, such as the U.S. Census Bureau. Results show that there is an association between median household affordability and city attributes, specifically, population dynamics, population density, and regional location. Both decision-makers at utilities and government organizations can use these results to inform investment decisions. For instance, utilities in growing cities may devote fewer resources to affordability programs compared to shrinking or stabilizing cities.

Dependency upon non-renewable energy sources has created challenges related to climate change, wars over energy supplies, famine, and cycles of deforestation concerns. As populations increase and economic development progress, energy demand grows, and ultimately the scalability of the problems associated with non-renewable energy resources. Solar energy plays a promising role to solve the problem and it is foreseen as the most promising renewable energy source due to its availability and benefits. Despite the promising effects, only a limited amount of electricity is currently produced globally from solar power. In order to realize the importance of tapping into solar energy, it is crucial to reveal the potential amount of electricity that could be thus produced. Many criteria should be taken into consideration before switching to solar energy. One of the most important criteria is the building location and surrounding environment. For example, a house on a hill or less shaded area will get more direct sunlight and generate more solar energy compared to a house in a shaded area. A multi-criteria methodology based on geographic information systems (GIS) and artificial neural network (ANN) is used in this research to assess community benefits of switching to solar energy by evaluating houses location and surrounding environment. The methodology presented in this research uses a tool for automated extraction of photography from 360° video data at locations of interest. The extracted imagery provides a data set to train a deep learning neural network to predict whether a house location is a good fit for solar energy. This research presents a state-of-the-art methodology to assess community benefits of switching to solar energy by using GIS and deep learning to automate the assessment of buildings location potential to harvest solar energy.

Adverse impact of natural disasters become astounding when infrastructure possess a poor resiliency level. Hence, multiple studies have been being conducted to develop a resilient system reducing the negative consequences of disruption and decreasing the reconstruction time after the disaster. In this regard, most studies have focused on limited number of resilience dimensions and have not investigated all aspects of resilience characteristics for transportation infrastructure. Therefore, the aim of this study was to develop a comprehensive conceptual model determining the relationships of resilience dimensions in critical and interdependent transportation infrastructure. To achieve the objectives of the study, more than hundred and fifty related scholarly articles were scrutinized thoroughly. Eighteen dimensions of resilience including robustness, redundancy, and resourcefulness were identified. It was found that the resilience dimensions have strong relationship with reconstruction timeline due to natural disasters. In addition, it was concluded that adaptability, which is the ability to learn from disastrous event to resist the next event more effectively, has a positive correlation with preparedness prior to the disruption and robustness of the system. In the end, a conceptual model was developed and proposed to determine the phases related to the identified resilience dimensions as well as their relationships with each other in transportation systems. The developed model provides the foundation to measure resilience of critical transportation systems and networks in different cities and counties. This study will also significantly help decision-makers and departments of transportations to prioritize their maintenance plans based on resilience of their transportation infrastructure and adopt appropriate proactive strategies to reduce unintended consequences of disruptive events.

In 2015 and 2016 combined, approximately 2.4 million first-time asylum seekers sought refuge in countries from the European Union, of which nearly 50% were registered in Germany. This population influx was primarily housed in urban environments, placing additional demands on the urban infrastructure systems from hosting communities. As such, the alternatives used by local authorities to provide services to displaced persons (e.g., temporary versus permanent infrastructure) can impact how hosting communities react to such methods (for instance, support versus opposition). This study proposes a framework to include public perceptions of hosting communities in the decision-making process of how to provide water infrastructure services to displaced persons. An agent-based model is developed to capture potential interdependencies between local authorities and hosting communities during the decision-making process of implementing infrastructure alternatives to explore the impact of including public support and opposition toward different infrastructure alternatives. This model is enabled by publicly available data (e.g., percentage of displaced persons accommodated by each state), 10 semi-structured interviews with decision-makers in the summer of 2016, and 416 valid responses from a survey deployed to the German public in 2016. The results revealed that the alternatives that involve making temporary changes to provide water infrastructure faced the highest levels of public opposition. Furthermore, the local context influences how hosting communities perceive different infrastructure alternatives. As such, infrastructure alternatives supported in the context of one community may be opposed by other communities. Understanding the effects of including hosting communities in the decision-making process may assist local authorities and utility engineers in developing infrastructure alternatives that can be sustainable and supported by the hosting communities.

Coastal water supply infrastructure systems (CWSIS) are exposed to saltwater intrusion (SWI) exacerbated by sea-level rise (SLR). While different factors contributing to the physical damage of SWI on CWSIS are well studied, little is known about the adaptive decision-making of institutional agencies in response to SWI impacts. This study developed a novel multi-agent simulation model that captures the coupled impacts of sea-level rise stressors and adaptation decision-making processes of utility agencies on the long-term resilience of CWSIS. The model was implemented using data collected from South Miami-Dade service area and then was used to design and test different scenarios pertaining to SLR and SWI projections as well as to adaptation decision-making factors. Accordingly, the long-term resilience of the system was assessed under various scenarios. The results showed the capability of the proposed simulation model for robust adaptation planning that leads to enhanced resilience in CWSIS under the impacts of SWI.

This paper presents community detection in actor collaboration networks to understand patterns of collective actions in resilience planning and management in interdependent infrastructure systems (IISs) prior to Hurricane Harvey in Harris County, Texas. Resilience in IISs is influenced by the collective actions of actors involved in planning, operating, and maintaining of physical systems. Hence, evaluating the patterns of collective actions in actor networks is important for understanding and improving the resilience planning and management in IISs. This study mapped four bipartite actor collaboration networks at different frequency levels in Harris County, Texas, prior to Hurricane Harvey based on a stakeholder survey. Then, the patterns of collective actions were assessed using the bi-Louvain community detection algorithm. The results show that, prior to Hurricane Harvey: 1) actor communities were segregated based on their government and non-government type in the network at yearly collaboration frequency level, 2) with the increase of the collaboration frequency, the network cohesion in the actor network became weaker. The results imply segregation between different communities and insufficient coordination across government and non-government actors. The study provides a new way to examine collective actions based on evaluation of communities in actor networks in IIS. The results could provide new insights into increasing cohesion in actor collaboration networks to improve the resilience planning and management in IISs.

Making roadways more resilient against both major and minor disturbances is an important goal for urban communities. Resilience is found in many transportation-related disciplines, such as freight movements and logistics, but it has not been explored much in traffic congestion analysis for performance assessment. In this study, we have explored the effect of roadways geography (layout abstraction) on the resilience of roadways to recurrent daily traffic jams. In doing so, we symbolized the recurrent congestions as an internal disturbance which can have a considerable impact on the performance of the road network. We characterized the resilience of road sections with a multi-dimensional approach and characterized the roadways geography by clustering the layout of the roadway. We illustrate the capability of our approach by analyzing an empirical dataset that measures the resilience of roadways in major freeways in the Los Angeles area, California, for November 2018. The traffic flow data for characterization of resilience behavior in daily congestions and layout of roadways in terms of high occupancy vehicles (HOV) lanes, on-ramp, and off-ramp is collected from the Caltrans performance measurement system (PeMs). The findings of these metrics could be used for performance-based planning and prioritizing resource allocation to the most critical areas.

Energy consumption is rising worldwide and the building sector is responsible for a large portion of it. One of the most effective ways to improve the energy efficiency of buildings is known as building energy retrofitting. However, selection of an optimum combination of energy-saving measures from a great number of possible combinations to perform building energy retrofitting process is a complicated and time-consuming task. This study aims to propose a simulation-based multi-objective optimization method that simultaneously improves environmental, economic, and social aspects of buildings. This model guides the stakeholders of the building industry through decision-making process of building energy retrofit that enables them to achieve the best tradeoff among objective function. After the optimization process, final combinations of retrofitting strategies are obtained and compared with the existing building. Final results demonstrate that performing building energy retrofit in the case study building significantly improves the predefined objective functions for this case study.

During extreme population displacement events, water and wastewater infrastructure systems may be challenged as host communities attempt to provide service to suddenly increased populations. As such, improving the resilience of water and wastewater systems is necessary to provide continuous services to both host and displaced persons. In protracted displacement, infrastructure’s resilience is further challenged by the need to transition from short-term humanitarian response to long-term development strategies. This study identifies examples of constraints that create the transitional space of the humanitarian-development nexus. Focusing on the current population displacement in Lebanon, data was collected through 10 semi-structured interviews in 2018 with leadership in local municipalities. Using open coding, the interview content was qualitatively analyzed, and emergent themes related to constraints across the technical, social, economic, and institutional dimensions were revealed. Results indicate that the stress from housing and different water and wastewater-use profiles have impacted each system’s capacity to meet the increased demand. Additionally, the ability of municipalities to respond to the increased demand has been stressed by lack of funds for developing new water and wastewater infrastructure projects, as well as discrepancy in stakeholders’ priorities in regard to these projects. Findings of this study would better inform the development efforts in providing continuous water and wastewater services to both host and displaced persons.

Reporting normative feedback to residential energy consumers has been found effective at reducing residential energy consumption. Upon receiving normative feedback households tend to modify their use to become in line with group norms. The effect of normative messages is partially moderated by how personally relevant normative reference groups are to the individual. Advanced energy metering technologies capture households’ energy use patterns, making it possible to generate highly similar and relevant normative reference groups in a non-invasive manner. Unfortunately, it is not well understood how similar individuals are to other group members. It also remains unknown how much individuals identify with behavioral reference groups. Therefore, this research aims to investigate how households perceive behavioral reference groups used in normative comparisons. Survey questionnaires are collected from 2,008 participants using Amazon Mechanical Turk. It is found that while households’ behaviors are more similar when grouped based on energy use profiles than based on geographic proximity, they identify more closely with proximity-based groups. Also, members’ group identification increases as individuals have higher similarity in energy use behaviors with other group members. This implies that enhancing the identity of profile-based behavioral reference groups will lead to an increase in norm adherence, and in turn reductions in household energy use.

Transportation asset managers leverage predictive analytics to optimize highway maintenance by lowering maintenance expenses and enhancing performance over the life span of assets. Deterioration prediction approach—a major element of life cycle planning—can highly impact the accuracy and effectiveness of prioritizing maintenance tasks. Forecasting future conditions is often a data-driven process based on historical condition measurements, the history of maintenance activities, and contributing factors to degradation. Therefore, inadequate data on all road segments leads to some shortcomings in predictive analytics and limits scalability and extensibility of the models. To resolve these problems, most research studies used family groups of assets with similar deterioration characteristics to investigate degradation. Subjective classifications are the most commonly used techniques to find similarities between asset items at different locations and to categorize them into family groups. The subjective categorization becomes a challenge when a large variety of contributing factors are considered. To tackle this challenge, we used a clustering approach. By minimal human intervention, clustering technique uses fewer assumptions about inter/intra correlations in dataset. We used k-means and agglomerative hierarchical algorithms to extract similarities over a large set of road segments. We implemented the algorithms on a case study in Virginia. We considered a range of factors, including weather and traffic, to identify family groups of flexible pavement segments. We additionally determined the optimal number of clusters by using the elbow and average silhouette score methods. Since the data clustering was accomplished by minimal subjective assumptions, the outcomes of the study provide insights into reliable condition predictions and can be used to develop prediction models for each family group. Consequently, the results of this research have potential benefits to improve the maintenance planning practices for states’ Department of Transportation (DOT).

The importance of infrastructure to the economic and social development at the local and global scales can never be overemphasized. Infrastructure investment, especially private infrastructure investment in most countries, remains continuously insufficient. Key obstacles to efficient financing of infrastructure projects are traditional finance instruments’ multiple drawbacks. As a derivative of the blockchain technology and a creative financing instrument, tokenization improves transparency, increases access to alternative capital sources, and liquidity, and mitigates counterparty risk through decentralization of infrastructure project financial system. This research attempts to shed light on the potential benefits and obstacles of infrastructure tokenization based on an analysis of the current literature. A conceptual framework of infrastructure tokenization is subsequently proposed to improve the efficiency of infrastructure finance. This research concludes that infrastructure tokenization would tremendously benefit the transportation infrastructure system and concedes that there is a lag in regulatory and technical reform to augment its application. Other researchers and industry practitioners can use the findings of this study as a stepping-stone to further improve the application of blockchain technology in infrastructure finance.

Highway infrastructure systems include a variety of assets: pavements, bridges, shoulders, culverts, signs, and pavement markings. Neighboring assets that are under the identical environment might be relatively deteriorated; therefore, there might be a correlation between the condition of neighboring assets. Additionally, the condition of each asset might impact that of neighboring assets. Most studies on deterioration prediction only focused on one asset type and investigated its deterioration independently. Therefore, the possible inter-relation between the condition of multiple neighboring assets and their mutual impacts are often ignored in the past research studies. We implemented an exploratory analysis to investigate the possible spatial and temporal correlation of the condition of multiple asset items for a case study of roadway in Virginia. We considered a range of asset classes in our study, including pavements, shoulders, and ditches. The dataset consists of inspection points during four years of inspection records. In our analysis. we found the locations with the highest probability of defects in multiple asset items. Then, we investigated the dependency between the density of defects in adjacent assets. The results indicate the spatial and temporal inter-relation and correlation between the condition of different asset types. By investigating weather, traffic, and maintenance history at susceptible locations to degradation, the results of this study can provide insight into the recognition of main factors in the deterioration of multiple assets. In addition, modeling the mutual impacts of different assets helps to improve the robustness of condition prediction approaches.

The objective of this paper is to investigate the interdependent transportation-flood control infrastructure vulnerability in the face of the fluvial flooding. A percolation simulation tool is employed to evaluate the flood-disrupted network functionality. First, a spatio-topological analysis is performed to model flood control infrastructure as a channel network and assess the vulnerability of the channels. Second, transportation infrastructure is modeled as a network of roads and inundation probabilities are estimated using their distance to the nearest channel together with the vulnerability of the channel. Finally, the post-flooding functionality of the road network is evaluated using graph-based measures. The framework is tested on a combined watershed in Harris County, Texas. Results show that failure of 10% of roads leads to a considerable loss of functionality in the road network. There are clusters of inundated roads in areas where the high density of development is concentrated around channels. The flood inundation also induces secondary impact by isolating the cluster of roads from the giant component. Such indirect connectivity disruption generally occurs in the areas with lower redundancy. The proposed framework provides an effective tool to inform the collaborative decision-making of flood control and transportation agencies to ensure network accessibility and better prepare the community for future flood hazards.

A natural disaster such as a flood affects all irrespective of age, race, gender, income group, physical ability, and others. In particular, the lower income groups run the highest risks for destitution and permanent displacement after a flood. Previous studies have revealed that the disaster damage borne by lower income groups are considerably higher than others in the society are. However, it is vital to identify the underlying factors affecting their disaster vulnerability according to historical disaster scenarios. This study aimed at revealing the major factors, particularly demographic indicators, that cause the lower income groups to be more affected by floods than others, including the analyses of how each of these factors increase their risks as compared to other groups. This study includes the in-depth analysis of disaster vulnerability of lower income communities in the East Baton Rouge Parish in the state of Louisiana, as it is both prone to floods and has a high population of lower income groups. In this study, we collected the factors that might render a section of the society vulnerable to natural disasters from previous studies and analysed them with respect to the different income groups to identify those, which may have some correlation with the lower income group. The outcomes of this study show that various factors including their geographic location, quality of housing and construction, lack of facilities and infrastructure, and social alienation exacerbate the resilience of the communities against natural disasters.

Transportation is a critical infrastructure, which is crucial for the functionality of urban communities during routine situations and extreme events. Diversity is recognized as a key property of a resilient transportation system since it determines the abundance and distribution of transportation modes in an urban area. Further, transportation diversity influences how a transportation system responds to and recovers from extreme events like natural hazards. In this study, the transportation diversity—based on modal availability and distribution—of zip codes in New York City is measured; subsequently, the socio-economic characteristics of zip codes with varying levels of transportation diversity are analyzed using descriptive statistics. The results show that zip codes with similar transportation diversity generally have comparable socio-economic factors. For example, residents in low transportation diversity zip codes have lower average income and higher vehicle ownership; further, these zip codes have higher commute time than other zip codes. The findings can support more comprehensive transportation and community resilience planning. For instance, the results pinpoint low transportation diversity zip codes with more disadvantaged residents, which can improve evacuation, shelter planning, and post-disaster recovery actions. In addition, improving transportation diversity will likely decrease car dependency of residents in low diversity zip codes and enhance their ability to reduce commute time.

Due to the increase in greenhouse gas emissions (GHG), several countries have developed programs to counteract climate change. Since 2011, the Colombian government has implemented a “Low Carbon Development Strategy” to reduce emissions production and promote sustainable economic growth. As a result, the government has incentivized the implementation of carbon mitigation measures in different economic sectors, including the office-building sector. This study aims to analyze dynamics between the implementation of carbon mitigation measures and the electric energy consumption in the office-building sector by identifying influential factors and examining how they are related. Through a system dynamics (SD) approach, a causal loop diagram (CLD) was built in order to understand key variables and feedback processes observed in the system. Results suggest that energy consumption is directly related to both the growth of the office-building sector and to external influences, such as government incentives. Conclusions point to the importance of analyzing the electric consumption problem with an integrated approach, in order to identify requirements to develop successful sustainable policies and foresee possible consequences. The proposed diagram can be used as a support tool in decision-making processes for public policy formulation.

Pedestrians’ physiological signals [e.g., electrodermal activity (EDA) and gait patterns] offer a unique opportunity for assessing the urban built environment, by deepening our understanding on human-environment interactions. Leveraging physiological signals collected in outdoor ambulatory settings has thus been attempted in several studies. Yet a question still remains about whether and which physiological signals could present direct associations with negative environmental stimuli observable in the urban built environment. Motivated by this, the paper examines the association between physiological responses and negative environmental stimulus, through the design of an experiment that exposes subjects to a strong negative environmental stimulus (i.e., replica of a dead animal body) in outdoor ambulatory settings. The experimental results (n=31) confirm significant associations between the presented environmental stimuli and physiological signals (i.e., EDA and gait patterns), and also indicate that physiological signals could portray individuals’ varying degree of sensitivity to the presented stimulus. These findings highlight that pedestrians’ physiological signals provide meaningful information on direct responses to the surrounding urban built environment.

The U.S. Environmental Protection Agency has estimated that approximately $334 billion investment through 2026 would be needed to simply maintain existing water utilities. When significant investments in renewal and rehabilitation of aging water system are needed, and when federal and state funding for infrastructure renewal is limited, the only source of funding for water utilities is increasing the water rates of the customers. However, the long-term effects of such changes in water rates on the water consumption by customers can be uncertain, i.e., the aggregate behavioral response of users can either mitigate or exacerbate the original issue. Therefore, understanding the behavior of consumers under different rate conditions is important as water utilities conduct short-term and long financial analysis of rate increases. The objective of this paper is to simulate, using agent-based modeling, the micro-behavior of water consumers under water rates increase events. The dynamic environment of the model allows consumers to update their decisions and behaviors based on their interactions with other consumers, and to determine what types of decisions are made based on word-of-mouth communication between consumers, and the level of water service and quality of the water supply. The expected contribution to the body of knowledge from this research study is the identification of emergent behavioral outcomes due to the interactions between the stakeholders stemming from increases in water rates as well as water quality and service reliability. The model outcomes will enhance water utilities’ assessment of the amount and frequency of water rate increases for long-range planning and management of urban water systems.

Automatic defect detection and classification from images is becoming increasingly important for bridge deterioration prediction and maintenance decision making. The majority of existing defect detection efforts have developed their datasets for training a machine-learning algorithm for detection/classification. However, the majority of these datasets suffer from two main limitations. First, most of the datasets are relatively small in size, which is not sufficient to build a well-trained, accurate image classifier. Second, most of the datasets lack the needed variety in scenes, angles, and backgrounds, which is not adaptable to different application contexts and environments. To address these limitations, this paper proposes a semantic image retrieval and clustering method to collect a large size of relevant images with various scenes, angles, and backgrounds from the Web and cluster these images for supporting domain-specific bridge component and defect detection. The proposed method includes three primary steps: query formation and image search and retrieval, image representation, and image clustering. First, a set of domain-specific words were extracted from bridge inspection documents and used as queries for retrieving a large number of images from the Web. Second, a transfer learning technique was used to transfer knowledge in a pre-trained model for general image classification to the bridge component and defect-related image clustering task. A deep convolutional neural network (CNN) with pre-trained weights was used to extract the visual features of the images for image representation. Third, a clustering technique was used to cluster the images based on the extracted features. The performance of the proposed method was evaluated using the silhouette coefficient. The evaluation results show that the proposed method is promising.

Animal-vehicle collisions (AVCs) are a significant issue for transportation agencies. This paper aims to identify the contributing factors to AVCs to reduce the number and severity of such crashes. The crash data is obtained from the Tennessee Department of Transportation (TDOT), which provides crucial information regarding crash severity, time of the crash, weather condition, driver age, vehicle speed, and type of animal. The logistic classifier is applied to identify the most important contributing factors to the severity of AVCs. The results of this study help transportation agencies develop various types of AVC mitigation measures to reduce the number of animal-related crashes based on the historical crash data and landscape structure.

Accurate information about the condition of sewers is necessary to make maintenance, repair, and replacement decisions. Much of this data is collected through video inspections using closed-circuit television (CCTV) cameras. Interpretation of the CCTV videos is typically conducted manually to identify defects in pipes. This process tends to be slow, labor-intensive, and subject to biases. Recent studies have attempted to use deep convolutional neural networks (CNNs) to automate the interpretation of CCTV videos. However, CNNs often have millions of parameters and are not easily interpretable to humans. As a result, previous studies on automated sewer defect interpretation have used off-the-shelf CNN architecture in an end-to-end manner, with little focus on the internal operations and behavior of these models. This study aids in the development of improved automated sewer defect interpretation systems by using a visualization technique known as class activation mapping (CAM), that facilitates CNN interpretations not feasible previously. By using CAM as a diagnostic tool, an improved CNN model is developed and trained on 14,400 images from 8-inch diameter vitrified clay pipes. Tests on this CNN model demonstrate improved accuracies over previous models in the identification of fractures, roots, and deposits. Hence, this study represents a crucial first step in using CNN interpretation techniques to develop improved models for sewer defect identification.

There are limited studies that empirically evaluate the interactions between households and infrastructure systems. As a result, the extent to which interruptions in infrastructure services influence different aspects of well-being for different subpopulations is still only vaguely understood. In order to address this knowledge gap, this study investigates multiple dimensions of well-being to derive an empirical relationship between sociodemographic factors of households and their subjective well-being impacts due to disruptions in various infrastructure services during and immediately after Hurricane Harvey. Statistical analysis driven by spearman-rank order correlations and fisher-z tests indicated significant disparities in well-being due to service disruptions among vulnerable population groups. The characterization of well-being is used to explain why and to what extent infrastructure service disruptions influence different subpopulations. The results show that disruptions in particular infrastructure systems are more likely to result in well-being impact disparities, in which racial minorities experience the greatest impact. Similarly, interruptions in services were more likely to evoke changes in social well-being and household’s connectivity to their communities. These findings present novel insights to understanding the role of infrastructure resilience in household well-being, as well as inequalities in well-being impacts across various subpopulations. The approach of the research and its findings enable a paradigm shift towards a more human-centric approach to infrastructure resilience.

Urban agencies are usually organized in divisions or departments that manage different infrastructure systems (i.e., pavements, utilities, etc.). Because of the high density of urban areas, these infrastructure systems are located close to each other and the intervention in one of these systems often impacts others. These interactions are often neglected because each of the divisions work with independent management systems. This paper presents a framework for the integrated management of municipal infrastructure. The proposed framework accounts for the savings derived from the integration of maintenance activities in co-located infrastructure. It comprises three modules: (1) an input module containing inventory data of the infrastructure systems; (2) a data processing module that analyzes infrastructure condition, maintenance needs, and costs, and identifies potential areas for integration; and (3) an evolutionary solving module that optimizes the maintenance program by accounting for the savings derived from integrated interventions. An urban network including pavement and sewer systems is analyzed as a proof of concept to estimate the order of magnitude of the savings that could be derived from an integrated management. In this case study, the integrated management of municipal infrastructure allows to reduce maintenance costs by 9%, when compared to a rehabilitation plan not including the savings derived from an integrated maintenance.

Natural disasters occur frequently in the U.S. and yield significant damages to communities. Decision-makers must understand the perspectives and inputs of the people of the community in order to facilitate resiliency and effectively serve and comfort them in a timely manner. Within recent years, a variety aspects of post-disaster recovery process have been studied, through which several indicators were determined. This research aimed to identify, validate, and analyze socioeconomic barriers that cause delays in the post-disaster recovery, based on public and expert input. To achieve these objectives, a comprehensive review of more than 300 scholarly papers and other relative documents was performed to identify the potential socioeconomic post-disaster recovery barriers (PDRBs). Two surveys, based on the potential barriers, were developed and distributed to subject matter experts (SMEs) and the public. Various statistical methods were used to analyze the collected data. The surveys were completed by 239 participants, 44 of which were SMEs. Such a comparative analysis has been rarely conducted, despite its importance to achieving an understanding of how socioeconomic barriers delay the post-disaster recovery processes. The findings indicated that, the biggest difference in the perspectives of SMEs and public was related to lack of family or friends who can help emotionally, while, lack of a community that looks out for each other was awarded the second highest difference level. In addition, testing the significance of the PDRBs using the public and SMEs sample input it was concluded that the expectations of the two groups were different and the extension of the significance was different comparing the two groups’ overview. The findings in this research would assist decision-makers in establishing effective post-disaster recovery practices and constructive relationships with the public that are aimed towards a more efficient recovery.

The characteristics and interrelations of each gender are constructed under geographic, cultural, social, and economic conditions. The differences in their understanding of and vulnerability to natural disasters cause them to evaluate the importance of probable challenges through post-disaster recovery process differently. Without considering gender-based requirements in post-disaster recovery planning, it is not possible to fully meet the needs of the community. In this study, the physical, social, and economic challenges after a disaster were evaluated according to both women’s and men’s opinions. To meet the objectives, (1) the potential challenges were identified through a thorough literature review, (2) a survey was developed and distributed to women and men who had been involved in at least one major disaster during the last decade, (3) the results were statistically analyzed, and (4) the challenges were weighted and prioritized. Twenty-seven challenges were identified from which 19 were determined statistically significant. In addition, the top-three weighted challenges for natural disaster management were “low revenue of the local government,” “low level of average household income,” and “lack of family/friends who can help emotionally.” Overall, the results indicated that women had a different perspective of disaster recovery challenges comparing to men. The results of this study establish a basis for better understanding the differences between women’s and men’s insights into disaster recovery challenges, and can be utilized to develop policies that enhance the resiliency of the communities and reduce their vulnerabilities to extreme natural events.

While working toward improved energy efficiency in the building sector, building systems need to be more resilient to cope with the dynamic environment. Climate change and the effects of fluctuations in the natural environment impact building systems. They can push buildings to operate at a higher limit than the initial designs leading to failure of the system. One of the most commonly addressed forms of resilience in buildings is the structural system. Other building services and systems should also be resilient to ensure robustness and adaptability. Increasing resilience for existing infrastructure and incorporating resilience into future facilities will be beneficial to achieving the social, environmental, and economic needs of occupants and other stakeholders. The literature was synthesized to identify opportunities for improved resilience in buildings beyond structural systems. This paper explores sustainability, resilience, and resilient building systems and provides recommendations that can advance sustainability and resilience in buildings.

Since the Kyoto Protocol was ratified in 1997, many countries have tried to reduce their greenhouse gas (GHG) emissions by implementing various reduction schemes. Carbon credit trading, one of these programs, has been used in many countries to comply with the protocol. In spite of the existence of carbon credit markets for over a decade, global GHG emissions recently reached their highest recorded level ever. The credits clearly have not achieved their promised results. Buildings have likewise hardly been affected, even though their energy use is among the largest sources of emissions. There is great potential for the construction sector to employ blockchain to support a significant reduction in fossil fuel consumption and improve building energy performance. In an attempt to revamp carbon credit trading, several non-profit foundations recently unveiled a blockchain trading scheme that is applicable to the carbon credit market. Financial activity which utilizes interconnected data “blocks” has many advantages. For example, this technology eliminates the need for time-consuming security to protect transactions. This goal of this paper is to present a new carbon trading paradigm that employs blockchain technology and that the construction industry can utilize to incentivize participation in the carbon market. This “smart” automation can simplify regulatory compliance.

Several researchers have conducted researches to measure characteristics of post-disaster reconstruction. The lessons learned from preceding post-disaster reconstruction processes help to prevent resource misallocation and assists to achieve of a reliable planning after a disaster. Hence, the potential barriers should be identified because it is significantly important to accurately predict cost and time of recovery. The barriers which lead to delays in the process of reconstruction are more prominent for this purpose. This study aimed to identify potential barriers to the reconstruction of critical infrastructure and transportation systems and assess their validation. The potential barriers to effective reconstruction were primarily identified by a comprehensive literature review. Next, interviews with experts in post-disaster recovery was conducted to collect their viewpoints and lessons learned. Eventually, qualitative data analysis was performed on the collected data. The results of current research demonstrated the agreement of interviewees on the impact of the majority of the potential barriers; however, their different experiences lead to discords in some of the barriers’ importance and priority. In addition, implementing quantitative method in this study showed the applicability of such approaches for improving post-disaster reconstruction and recovery process. The current research conveyed an efficient approach for evaluation of reconstruction characteristics in the disaster-affected communities with limited resources and offered knowledge for improving studies relative to disaster reconstruction by identifying and validating the important barriers to effective reconstruction.

A great deal expert knowledge is required to design complex sustainable infrastructure systems such as energy-efficient, affordable multifamily housing units. A more accessible understanding of how to optimize green building systems is needed to allow more builders and designers to engage in the delivery of sustainable construction projects. EarthCraft Multifamily (ECMF), a green building certification, was designed for traditional builders to engage in the process of delivering affordable sustainable multifamily units. To date, has certified more than 45,000 energy-efficient housing units. This study aims to improve the accessibility and usability of ECMF tools and resources such as the program manual, worksheet, and technical guidelines. User experience (UX) methodologies are being leveraged to enhance ECMF resources’ delivery of affordable sustainable housing. User interviews, stakeholder interviews, and heuristic evaluations provide detailed information on the usability strengths and weaknesses of ECMF. Preliminary findings show potential for EarthCraft resources to evolve in their layout, content, and delivery methods. It has also been found that ECMF tools have encouraged the selection of optimal sustainable design practices in new construction and renovation projects. While a base level of expertise will always be needed in infrastructure delivery for safety reasons, the delivery of sustainable designs can be more accessible to a larger range of stakeholders by improving user experiences of resources and tools. By making the process more approachable, non-expert stakeholders can better manage their role in the process. The procedures and findings from this study are transferable to other green infrastructure rating/certification programs, with similar resources, such as LEED and Envision.

The AEC industry has witnessed an increasing growth in the green building market in the past decade and continues to expect its promising future. While most of the prior research on the green building market was conducted based on the survey data at the project level, how the corporate dimension develops is still lacking. To fill the gap, this research attempted to explore the characteristics of the U.S. green building market from a corporate perspective both quantitatively and qualitatively. We first collected data of 223 top/representative green building contractors in the U.S. identified by the Engineering News-Record over the past decade, and explored the development trend. The 223 organizations were further categorized into three different groups with distinct characteristics and corporate strategy using k-means clustering. This research provided practitioners and academics with a holistic understanding of the U.S. green building market, as well as contributed to the development of green building corporation management strategy.

Building stakeholders must invest an increasing amount of resources in the process of delivering energy-efficient buildings as energy efficiency standards become more aggressive, and building systems become more complex. This paper documents the earlier stages of a larger project, which is a joint venture between Virginia Tech and an industry partner, to predict the benefits and drawbacks of energy investments in the residential housing sector. As such, existing processes for predicting the benefits and drawbacks of energy-efficient technologies in buildings can be improved in the areas of comprehensiveness, precision, accuracy, and elegance. This study presents an exploratory and descriptive field study of an affordable housing development in Virginia, which was recently renovated to improve and explore system performance of one-stage, two-stage, and variable speed HVAC systems. Utilizing this study design, the researchers performed energy analyses on nine duplexes, which include two households per unit, to evaluate the efficacy of the energy investments alongside an updated model of energy analysis. Building upon manufacturer specifications, the researchers had hypothesized that the variable speed HVAC systems would outperform the other two systems in terms of energy savings. Preliminary analyses utilizing pre-existing models of energy monitoring and energy analysis proved contrary to the hypothesis. Data collected and analyzed thus far are considered preliminary results, which support the researchers’ case for updating energy monitoring and energy analysis processes. The preliminary data analysis has shown great potential for updating previous processes to calibrate inaccurate perceptions of behavior-driven energy use; leverage cloud computing for data analytics and visualization to improve data accessibility; and the inclusion of socio-political impacts into decision-making tools. The findings of this study transfer beyond the context of HVAC systems to various building systems. The methodology of this study also provides guidance for better academic-industry-citizen engagement for the real-world testing and design of engineered infrastructure systems.

The objective of this study is to empirically assess household susceptibility to the power disruptions during disasters. In this study, a service gap model is utilized to characterize household susceptibility to infrastructure service disruptions. The empirical household survey data collected from Harris County, Texas, in the aftermath of Hurricane Harvey was employed in developing an appropriate empirical model to specify the significance of various factors influencing household susceptibility. Various factors influencing households’ susceptibility were implemented in developing the models. The step-wise algorithm was used to choose the best subset of variables, and availability of substitutes, previous hazards experience, level of need, access to reliable information, race, service expectations, social capital, and residence duration were selected to be included in the models. Among three classes of models, accelerated failure time (AFT)-loglogistic model yielded the best model fitness for estimating households’ susceptibility to disaster-induced power disruption. The model showed that having a substitute, households’ need for the service, race, and access to reliable information are the most significant factors influencing household susceptibility to the power disruptions. Understanding households’ susceptibility to infrastructure service disruptions provides useful insights for prioritizing infrastructure resilience improvements in order to reduce societal impacts.

Currently, visual technologies such as closed-circuit television (CCTV) are commonly utilized for sewer pipe inspection and condition assessment. Inspectors are required to interpret CCTV videos, evaluate the defect severity, and assess the pipe condition manually, which is time-consuming, and the assessment results are subject to the expertise and experience of the inspectors. Computer vision techniques are drawing the attention for interpreting inspection results automatically. Previous studies mainly focus on classifying defect types or detecting the relative location of defects in CCTV videos, but there is a lack of approaches for automated condition assessment of sewer pipes. This study proposed an approach for efficiently evaluating the severity level of sewer pipe operation and maintenance (O&M) defects, such as deposit and tree root, based on computer vision techniques. Firstly, the codes for the severity assessment are designed with references to existing standards. The measurement of the defects is performed by extracting related features from the images, such as the relative area of the pipe cross section and the area of the defects. The edge of the pipe joint and the vanishing point of the pipe ground surface are detected on the image. Then the joint shape model is fitted based on the detected edge and the vanishing point. On the other hand, the relative area of the defects on the image is obtained after performing defect segmentation using deep learning models. Combining the relative area of the pipe cross section and the area of the defects, the severity level of defects is computed based on the loss percentage of the pipe section and the designed severity assessment codes. In the end, illustrative examples are provided using images extracted from CCTV inspection videos to demonstrate the applicability of the proposed approach.