Construction Research Congress 2024: Sustainability, Resilience, Infrastructure Systems, and Materials Design in Construction

Housing marginalized communities in resilient homes is an important aspect of building a climate-resilient population. In this study, the daily temperatures in a semi-permanent home in a densely populated slum have been compared to those in the homes in a consciously designed in situ slum rehabilitation project built using vernacular technologies. On monitoring the indoor and outdoor temperatures over nine months under both conditions, results clearly indicated that the permanent homes are significantly cooler than the semi-permanent homes or slums by an average of 10%−15% due to the higher thermal mass, insulation, and ventilation. The temperatures are also more stable, with lesser variation between seasons. The paper demonstrates that through minimal additional investments and good design prioritizing thermal comfort and ventilation, low-cost slum rehabilitation projects could lead to a healthier and more productive society. Such field investigation studies support robust modeling and design of rehabilitation homes.

With the anticipated growth in construction activities, minimizing value chain emissions, including those attributed to activities beyond an organization’s direct control—aka “Scope 3” emissions, and adopting circular economy (CE) practices have become critical to attaining the 2050 climate goals. Extant literature lacks a value-chain-comprehensive set of CE strategies on one hand, and an analysis of their actual project applications on the other. This research aims to build a holistic portfolio of CE actionable strategies that span across the entire built environment value chain, analyzing their extent of implementation and interlinkages within the literature versus actual projects. To this end, the authors conducted a systematic literature and practice review to extract and collate CE strategies and project case studies, and quantified the implementation frequency for each of the strategies and their interlinkages through a network analysis. Results revealed a broad yet fragmented implementation, with the most frequently implemented strategies including requiring sustainability ratings, passive design measures, and landfill diversion, whereas strategies targeting logistics and property market phases were rather scarce. Ultimately, the findings herein can serve as a foundation for a multifunctional database of value chain-specific strategies and case studies that can guide the industry on its track to decarbonization.

Distributed solar generation (DSG) systems are small-scale units that are located at or near end-consumers such as residential rooftop photo-voltaic (PV) systems. DSG has driven a plethora of multidisciplinary research with various interrelated topics and perspectives. A broad understanding of research directions and potential gaps in knowledge can be elusive. Accordingly, the goal of this paper is to investigate the research trends related to DSG and explore research opportunities. This is achieved by performing a data-driven keyword search and network analysis on a large dataset of publications related to DSG to quantify topics related to DSG and the interconnectivity between them. The findings show that social equity in the context of DSG may be understudied as it has few publications contrasted with high citation impact and weak interconnectivity to other topics. In addition, the findings show a recent sharp growth in publications related to machine learning (ML) and artificial intelligence (AI) in the context of DSG. Ultimately, the findings in this paper should guide future multidisciplinary research in DSG.

Climate change has led to an unprecedented increase in the frequency and intensity of heat waves around the world. Excessive heat can result in life-threatening situations for citizens, especially seniors, children, and people with chronic illnesses. Therefore, it is vital that all citizens, especially vulnerable populations, have access to air conditioning or other cooling mechanisms during heat waves. This emphasizes the criticality of electrical infrastructure to save lives during these extreme events. Previous studies indicated that the existing infrastructure systems are not equitably serving all citizens due to unjust urban development. The first step to fundamentally transform the existing processes is to better understand the problem through evidence-based and data-driven methods. Therefore, this study aims to understand inequity issues related to citizens’ access to electricity during heat waves. More specifically, this study aims to empirically examine (1) whether there is a statistically significant association between citizens’ socioeconomic status and their exposure to excessive heat, and (2) if there is a statistically significant association between citizens’ socioeconomic status and the reliability of their access to electricity during heat waves. The outcomes of this study set the stage for equitable urban development and just infrastructure systems.

Flood in the Great Lakes area is one of the most common natural hazards, causing significant damage to infrastructure. In pursuit of sustainable and resilient communities against increasingly frequent inclement climate, protecting the lifeline safety of coastal infrastructure becomes imperative. In this paper, we propose a computational framework, which consists of computational fluid dynamics (CFD), finite element analysis (FEA), and Gaussian process regression (GPR) algorithm, to quantitatively assess the responses and risks of bridge, the most vulnerable infrastructure subjected to floods. Based on the simulation results, an integrated tool to enhance the flood resilience of the bridge infrastructure is presented. The outcomes of the study pave the way for coastal communities to estimate the impacts and potential risks confronting the bridge infrastructure during flood hazards. The framework empowers stakeholders in the Great Lakes area to make optimal decisions, implement necessary protective measures, and enhance the overall resilience of the coastal infrastructure vulnerable to flood events.

Flood hazards have affected millions of people through damage to infrastructure. Flood hazards in the Great Lakes area in recent years have caused a large area of the infrastructure system in Indiana and Illinois to be paralyzed due to the impact of the high flood level on infrastructure. To assess the risk of floods for certain types of infrastructure, we develop a machine learning-based risk analysis model to generate predictions of risk scores based on several indicators from a public dataset. Different infrastructure types such as bridges, culverts, pump stations, and roads were considered. We predicted risk scores to represent the flood risk from multiple components, such as exposure and criticality to flood hazards. The correlation between certain indicators and risk scores was examined to determine the critical variables used as input for the risk analysis model. The Gaussian process regression (GPR) algorithm was applied to train and test the dataset. From the analysis results, we find the GPR model provides accurate prediction. Therefore, the machine learning-based risk analysis is a valid approach to identify the degree to which infrastructure assets are at risk and provide stakeholders and decision-makers with the suggestions on minimizing the effects of future flood events.

Construction contributes to around half of global material consumption and solid waste generation. Transitioning to a circular economy is a potential solution to mitigate negative environmental externalities. Nonetheless, this transition is challenged by various technical and socio-economic factors. To identify a viable path for a circular economy transition, the overarching objective of this research was to build on transition theory and identify drivers, motivations, challenges, and efforts in shifting to a circular economy in the construction industry. For this purpose, semi-structured interviews were conducted with representatives from 13 construction companies in North America and Europe, including owners, contractors, and manufacturers, who are actively implementing circular economy principles. Interview findings were summarized, and a four-step transition management cycle framework for the transition to a circular economy in the construction sector was proposed. Findings are useful in managing the transition from a linear to a circular model in the construction sector.

In a linear economy, products such as construction materials are disposed of at the end of life of the built asset and are considered waste. The principles of a circular economy call for the reuse and recycling of products, thereby extending their lifespan. Construction and demolition waste (CDW) exceed a third of the total waste generated worldwide. Developed countries have created master plans to manage waste; however, these plans do not necessarily focus on CDW. Also, they fall behind in presenting a clear methodology for duplication in other parts of the world. This study proposes a master plan development framework targeted towards effective CDW management. The methods used consist of thematic and comparative analyses on data collected from literature, an analytic hierarchy process (AHP) expert supported analysis, followed by a case study application of the developed framework to the context of a developing country. Finally, a microeconomic assessment is carried out.

To support electrification and decarbonization, building codes now mandate the inclusion of clean energy technologies in new commercial and residential constructions. However, there is a lack of comprehensive research on how “early adopters” and their “followers” differ in their adoption of various technologies at the city and community levels. By utilizing the Bass model, this study emphasizes the importance of recognizing community and city adoption patterns, understanding the social dynamics of diverse communities at different spatial scales, and considering the traits of early adopters and their followers to effectively promote technology adoption. The study reveals that higher adoption rates of electric vehicles (EVs) and heat pumps are linked to a greater presence of early adopters. Early EV adopters tend to have higher educational attainment and lower home values, while heat pump followers are associated with higher population density. Moreover, the study identifies an inverse correlation between early adopters and followers, as well as with market potential. Interestingly, communities with higher early adopters of rooftop solar tend to have lower followers of heat pumps. This underscores the significance of finding a balance between encouraging innovation and imitation behaviors, alongside promoting the dual adoption of technologies when shaping policies.

Corporate sustainability practices fueled by environmental, social, and governance (ESG) reporting have been gaining popularity in corporate settings. Driven by environmentally conscious investors, communities, and other various stakeholders, critical investments are being made to set and achieve ambitious sustainability goals. The construction industry is not an exception to this trend. While ESG frameworks and standards suggest general road maps about ESG metrics and various disclosures, the implementation and reported topics vary widely among construction companies. Similarly, the research on the landscape and status of ESG in the construction industry has been limited and infrequent. This exploratory research paper seeks to take a step toward laying out the status of sustainability within the industry. Through semi-structured interviews with corporate leaders in large construction companies, common sustainability practices, challenges, and opportunities were identified. These interviews were also used to elucidate key disclosure topics and best practices in the construction industry. This study will be beneficial to industry professionals to navigate the ESG landscape and benchmark against the exploratory baseline presented. Academics could develop this exploratory research further to suggest standardizations to ESG practices for industry-wide consistency, benchmarking, and improvement.

The architecture, engineering, and construction (AEC) industry comprises 30% of the extraction of resources and 25% of the solid waste. Studies have indicated that the adoption of sustainable techniques to lessen this environmental impact is being inhibited by demolition waste, the lack of standards, and software homogenization. This relates to the linear way of using resources, and practices such as circular economy (CE) could be a key driver to achieving sustainability. This paper presents the material passports (MPs) tool for implementing CE in the AEC industry. MPs can provide information about how to assess materials at different life-cycle phases and their reuse at their end-of-life (EOL). Thus, this paper explores frameworks for deconstruction information model (DIM)-based MPs within building information modeling (BIM). DIM-based MPs provide information to enhance efficient demolition while recognizing valuable materials at the EOL. To achieve this goal, a literature review was conducted to understand how BIM could enable the use of MPs and what attributes should be addressed. This study aims to contribute to the implementation of CE in the AEC industry by developing tools that will allow us to understand how to benefit from materials by lowering the exploitation of resources for future construction.

Residential buildings account for over 20% of total energy consumption in the US, with heating, ventilation, and air conditioning (HVAC) consuming the most energy. However, there is little research that comprehensively explores how different influential factors affect energy consumption of space heating (SPH) and space cooling (SPC) separately. Therefore, based on RECS 2015, this study investigates the influencing factors of HVAC energy usage in single-family detached houses, differentiating SPH and SPC. Random forest regression and multiple linear regression are employed to model energy consumption, and their performance is compared. The results illustrate that nonlinear models fit HVAC energy usage better, with higher goodness of fit and fewer predictors. The determinants identified by both methods are similar, emphasizing housing size and climate over demographics, and vary by SPH and SPC. The order of influencing factors for SPH is climate > fuel type > housing size > number of windows; however, that for SPC is climate > housing size > equipment type > AC vintage. Finally, the study confirms that electrified SPH leads to less energy use. The findings contribute to understanding the differences in HVAC energy consumption and their influencing factors, facilitating the design of energy-efficiency programs.

High-intensity storms expose pervasive deficiencies in aging stormwater management infrastructure, which poses a significant threat to the growing population of Americans living in unsheltered conditions. Unhoused communities are particularly vulnerable to flooding hazards, as their makeshift residences lack structural stability and property insurance protections, resulting in excessive damages and casualties. This literature review investigates how failures in stormwater infrastructure systems during high-intensity hazards disproportionately endanger unhoused individuals throughout the United States. Direct content analysis methods were used to converge on empirical consistencies in literature at the intersection of civil infrastructure systems, socially vulnerable communities, and resilience against natural hazards. Results identify limitations in the existing literature, as well as descriptive emergent themes to inform future research. This project aims to reevaluate current engineering best practices to more equitably protect unhoused communities from flooding hazards.

Public-private partnerships (PPPs) have been implemented as a suitable delivery method for infrastructure projects. Because of their size, those projects imply disrupting significantly the natural environment when creating complex built environments that fulfill human needs. In this context, PPPs require instruments to evaluate and mitigate environmental impacts through the infrastructure life cycle such as the environmental impact assessment (EIA). In PPPs, the EIA is carried by concessionaires composed of equity providers (EPs), which are embedded in network structures. Despite the relevance of EPs and project complexity for the outcomes of the EIA, previous research in this field has mainly focused on understanding EIA processes rather than its outcomes and drivers for effectiveness. To overcome this limitation, this paper analyzes the influence of temporal network metrics (e.g., temporal degree centrality, temporal closeness centrality, and temporal betweenness centrality), defined by EP relationships, and project complexity in delivering effective EIA. This research gathers information on 28 road PPPs in Colombia between 2014 and 2023. Findings show that actors’ positioning computed through temporal metrics, especially temporal closeness centrality, influences the capacity of actors to carry EIA effectively.

Cleanroom environments demand specialized ventilation systems to uphold stringent air quality standards. This study aims to comprehensively investigate the research landscape of cleanroom ventilation and air distribution systems using a robust network analysis approach, complementing a systematic literature review. A systematic review identified relevant literature on cleanroom ventilation, forming the basis for a citation network comprising 133 papers. By employing three centrality measures (degree, betweenness, and closeness), the network analysis approach facilitates the identification of significant papers based on network characteristics. Content analysis of these influential works provided a deeper understanding of the research domain, revealing research gaps, trends, and promising opportunities for future investigations. The network analysis approach enriches our comprehension of cleanroom ventilation research by providing valuable insights into the structure and dynamics of the research community. Future researchers and practitioners can leverage these insights to make informed decisions in designing and implementing effective ventilation systems, ensuring optimal cleanroom performance. The study highlights the importance and usefulness of reference network analysis as a powerful supplement to review studies, offering a multifaceted view of the research landscape and advancing knowledge in the critical domain of cleanroom ventilation.

Construction firms play an important role in the recovery of communities after a disaster. In order to participate in the recovery of a community, it is important for construction firms to be prepared for disasters well in advance. It is also necessary for the construction firms to have capacity to withstand the disaster first before helping the community. There are several frameworks to assess resilience of communities, but there is a lack of literature and tool to assess the performance of construction firms. This paper aims to fulfill the gap by focusing on the assessment of preparedness of construction firms. A set of previously identified readiness indicators is used to build an assessment tool. This tool consists of a series of questions to be answered by the personnel of construction firms to understand the level of preparedness. The results of this tool are given as a score ranging from 0 to 100 with 0 indicating that the construction firm is not prepared for the upcoming disasters and 100 indicating that the firm is well prepared. The results indicate the strength of the firms and the areas that has scope for improvement.

Communities and cities are becoming vulnerable due to climate change-induced disasters such as heat waves, flooding, landslides, and droughts. The severity and increased frequency of these extreme events have demanded a resilience plan in the major cities to combat extreme weather events, which requires a systematic community vulnerability assessment. However, the specific impact of extreme events such as high heat waves on community vulnerability has been difficult to measure due to the unpredictability of weather patterns and events. Further, compounding the effects of building and built environment characteristics, the social and behavioral characteristics of households can result in differing levels of vulnerability to extreme temperature events. Even though many studies have discussed social vulnerability based on community demographics, the compounding effect has not been fully explored. When it comes to thermal resilience against extreme weather events, socially vulnerable communities are more likely to be affected by extreme heat due to a lack of thermal-resilient houses. In this research, Kolmogorov-Smirnov (KS-2 test) test was used to extend the relationship between building features data and the social vulnerability index of the city of Philadelphia. The outcome of this research strengthens our understanding of how social vulnerability and building resilience are correlated, also in the future to build community prototypes that integrate building features and social vulnerability to simulate community response against extreme weather events.

Construction product manufacturing is a contributor to global climate change through its carbon emissions, known as embodied carbon (EC). Previous studies have found that utilizing a mass timber structural system can contribute to a reduction of EC. A barrier that prevents the use of mass timber is its higher cost than other available alternatives, which should be justified by stakeholders that wish to use mass timber. However, there is a dearth of studies to support their efficient analysis and decision making. To fill this knowledge gap, the objective of the present study is to develop a model which streamlines a decision-making process based on cost and EC data. To test the model, a case study was performed to compare the costs and EC of multiple structures. Based on the case study, the model proved to be an efficient way of providing the desired data. Additionally, the study found that alternatives utilizing mass timber resulted in a larger percentage reduction in EC than increase in cost. The model is expected to support stakeholders that wish to lower EC by selecting a mass timber structure.

Building resilient communities requires effective collaboration among multisector stakeholders. However, stakeholders may hold different value priorities in implementing resilience strategies. Heterogeneous collections of value priorities form distinct value systems. Differences rooted in value systems may cause conflicts that impede stakeholder collaboration. Despite extensive studies on stakeholder collaboration, there is a lack of systematic understanding on how the value systems of various stakeholders influence their collaboration. To bridge this gap, this study focused on examining the impact of stakeholders’ value systems on their collaboration patterns through network and exponential random graph model (ERGM) analyses using data collected from a survey. Our results show that stakeholders’ value systems have significant impacts on their collaboration patterns (i.e., presence or absence of collaboration, frequency of communication) in resilience planning. The findings of this study offer insight to enhance stakeholder collaboration in community resilience planning by considering stakeholder value systems.

Enhancing housing resilience requires us to choose a path that can systematically assess the value of resilience to the housing stakeholders. However, there is a lack of methods that assess housing resilience value by considering stakeholders’ perspectives. To address this gap, this paper proposes a human-centered resilience evaluation framework that integrates stakeholder value systems and dynamics with resilience evaluation. The model mathematically incorporates (1) the degree that a resilience asset fulfills stakeholder values (i.e., resilience fulfillment degree), (2) stakeholder value systems and dynamics in a disaster, and (3) the integration and alignment between resilience fulfillment degree and stakeholder value systems. The use of the model was illustrated through an experimental case study that assesses the resilience value of two alternative housing projects in the context of a hurricane disaster. This framework offers a unique approach that integrates human perspectives with resilience assessment and facilitates human-centered resilience designs or strategies.

Resilient materials and technologies (RMTs) can absorb external stresses imposed during a natural disaster and enhance a building’s resilience against natural disasters. Therefore, this study identifies RMTs available for implementation in a single-family home to be resilient against hurricanes and floods. The study also determined RMTs’ performance from the perspective of variables: multi-functionality, recyclability, smartness, and market maturity. The study used three-phased exploratory research involving establishing research parameters, data collection using the forward snowballing method, and then descriptive statistical analysis for the identified RMTs. The research identified 54 RMTs that could be adopted onto a single-family home’s substructure (17 RMTs) and shell (49 RMTs). A significant majority of the RMTs were mapped onto the homes’ shell assemblies. Further, most RMTs were identified as recyclable, and very few were as smart, thereby indicating the need for smart RMTs that can transform in response to external stresses.

Flooding is one of the major natural disasters. Predicting river water levels and flooding is an effective way of enabling proactive flooding response measures. Although machine learning-based prediction models in prior studies have obtained a low error rate, they do not perform well during the rapid and significant water level rising (i.e., flooding). To provide a better flooding prediction tool, the present study first evaluates the commonly used long short-term memory model and points out the limitation of prior studies. Then, a novel model named physics-informed (PI) LSTM is proposed. The PI-LSTM integrates hydrological knowledge into the neural network as well as extends the current physics-informed river water level prediction neural networks to a recurrent one. Compared with LSTM, PI-LSTM has a better performance in predicting rapid and significant water level rising. The study is expected to increase the accuracy of flooding prediction and provide better decision-making support to agencies responsible for flood forecasting and warning.

The quality of life and societal outcomes, such as health, education, employment, and wealth, depend on infrastructure, but not all communities benefit equally from infrastructure developments. Some communities experience disadvantages like limited transportation access, unmet infrastructure needs, or outdated electrical distribution infrastructure. Public agencies at different levels have made efforts to advance equity, but approaches to equity-based policies vary within the United States and between agencies. Peer-to-peer learning is one of the most effective ways to identify promising strategies for achieving infrastructure fairness. However, little research has focused on how local agencies incorporate equity into their planning processes. This study conducted a content analysis of local capital improvement plans to propose a conceptual life-cycle framework for incorporating equity into the capital planning process. The framework covers various stages, from formulating a capital budget request to evaluating the project’s effectiveness in improving equity if the request is accepted and implemented.

Due to the frequent occurrences and damages of flooding disasters, there has been an increasing interest in developing proper methods to help mitigate their consequences. Although previous research was directed to help in managing disasters, more data-driven methods are still needed. To this end, this paper developed a novel approach to enhance the decision-making process related to prioritizing the flooding mitigation, management, control, and/or recovery plans. First, data was collected for multiple flooding events and was cleansed to reach a total of 7,152 observations. Second, exploratory data analysis was conducted to examine and uncover trends and relationships in the data. Third, unsupervised machine learning was used to categorize the management priority of the disaster events using clustering analysis. Fourth, geospatial analysis was conducted on both the flood event level and on the county level. The results showed that flood disaster events could be categorized—based on their duration and frequency—into two management priority levels: low priority and high priority. The conducted research in this paper contributes to the body of knowledge by equipping agencies and disaster decision-makers with a decision-support system to prioritize short to long-term risk reduction and management interventions to better address flood disaster events.

The construction of projects and buildings in urban areas leads to major transformation in the urban surfaces, where natural, pervious surfaces are transformed into rough, impermeable surfaces. This transformation leads to different environmental risks including the urban heat island (UHI) effect. Not all urban areas are equally affected by the UHI effect, which makes some areas more vulnerable to extreme heat waves. Thus, this paper develops a heat vulnerability index based on demographic, land use/land cover (LULC), meteorological, and geographic factors to assess heat vulnerability of communities. First, data for multiple meteorological, demographic, geographic, and LULC factors was collected. Second, four vulnerability sub-indices were developed. Third, the heat vulnerability index was developed as a weighted average of the four sub-indices, where the principal component analysis (PCA) method was implemented to calculate the weights of the factors as well as the sub-indices. The results reflected that LULC factors have the greatest impact on assessing heat vulnerability of communities. This research adds to the body of knowledge by helping authorities and decision-makers identify heat-vulnerable communities and ultimately developing immediate adaptation and mitigation plans for areas identified as highly vulnerable to heat events to address future harm from high temperatures.

Historic and recent flood events show how susceptible communities are to floods. In the future, it is anticipated that areas prone to flooding will see a substantial increase in population density, which increases the damages of flooding events. In addition, climate change and rising sea levels may increase the frequency and severity of large-scale floods. This reflects that flood mitigation projects need to be properly planned and implemented. Hence, this paper aims to identify cost-effective flood mitigation measures for possible implementation in Essex County, New Jersey. First, the benefits were quantified for three potential flood mitigation measures/projects: elevation, wet floodproofing, and dry floodproofing under different scenarios. Second, the costs were estimated for the different alternative flood mitigating measures. Third, benefit-cost analysis and Monte Carlo simulation were conducted to select the most cost-effective measure(s). The results showed that while wet floodproofing and dry floodproofing might be more cost-effective than elevation, they do not provide the same protection level as elevation measures. This paper contributes to the body of knowledge by providing a framework that helps communities make informed decisions about their flood-related risks and money and prioritize projects to decrease flood losses by increasing the level of protection against flood hazards.

The increase in frequency and intensity of flooding has become a global challenge. Increased population, rapid urbanization, and climate change all aggravate flood frequency and losses. Flooding events often lead to ripple effects which are a series of interconnected events that are triggered by flood hazards and aggravated by their recurrence. Ripple effects are often hard to predict and assess due to the high uncertainties inherent in their nature and causes. Hence, this paper aims to model the ripple effects of floods using data mining algorithms. This research mainly focuses on transportation infrastructure rather than other critical systems. First, data were collected for multiple flood events in the states of New York and New Jersey and their associated ripple events. Second, the data was cleansed and preprocessed. Third, association rule analysis was conducted to identify the critical dependencies or key combinations between the occurrence of flooding events and the associated ripple effects. The results illustrate that the following events are the most critical ripple effects resulting from flooding events: obstruction on the roadway, accidents, and single-line traffic alternating directions. The result of this study can provide helpful information for decision-makers to model infrastructure dependence and interdependence, which is an important consideration in the development of resilience-based performance standards to reduce flood-related losses.

Power grid infrastructure systems are vulnerable to natural hazards. Many studies have focused on the use of sensing technologies to detect natural hazard-induced power grid faults. However, a massive sensor network to collect data for such studies is costly and may not capture complex grid conditions. Therefore, this paper develops an automated grid fault detection system. First, a smart solar-enabled microgrid was developed to simulate small grid operation, which can also dynamically sense the voltage and current for capturing the grid conditions. Second, three types of faults (i.e., partial shading fault, three phase fault, and tripping fault) were introduced into the microgrid to represent the potential faults caused by natural hazards. Third, a one-day operation was simulated. Fourth, a dataset with 864,000 samples was collected, denoised, labeled, and used to develop three different machine learning classifiers. These classifiers were evaluated using four metrics, including accuracy (i.e., the proportion of correct predictions made by a classifier), precision, recall, and F-1 score. Model evaluation results showed that (1) the K-nearest neighbor was the optimal classifier to detect a partial shading fault with an accuracy of 99.19%, and (2) decision tree was the most performant model for detecting three phase fault and tripping fault with accuracies of 100% and 99.90%, respectively. Ultimately, this paper contributes to the body of knowledge by integrating power grid simulation and machine learning for improving the resilience of power grids against natural hazards.

Flooding is a destructive natural hazard that causes significant economic and social impacts. Newark, a city located near the Passaic River in New Jersey (NJ), has a history of flooding and a high risk of future floods due to its location near rivers. However, there is limited research that was conducted to perform a comprehensive flood risk analysis in Newark. Hence, this paper fills this gap. First, the study region and different flood hazard scenarios (10-, 25-, 50-, 100-, and 500-year return periods) were defined. Second, the flood depth grids were generated using H&H modeling. Third, the potential losses from flooding in terms of both economic and social impacts were estimated and quantified. The results provided insights into the most vulnerable areas to flooding in Newark. The findings also recommended potential mitigation measures that need to be implemented to reduce future economic and social damages and losses. This study adds to the body of knowledge by providing valuable insights for disaster management professionals and community leaders in developing effective flood risk management plans and making informed decisions on mitigation investments. Moreover, this study helps to increase the resilience of Newark to potential future flooding events.

There is a growing body of literature exploring the viability of mass timber as a building material. Research and development in the use of mass timber will expand now that the International Code Council has approved the construction of high-rise buildings using mass timber. While several studies on mass timber with differing goals and methods have been published, a systematic examination of the current status, development, trends, and practical implementation associated with mass timber is limited. In the US construction market, a significant portion of industry practitioners is still reluctant to accept mass timber as a feasible load-bearing material. The purpose of this paper is to review and discuss the latest research trends and identify research gaps in the use of mass timber in the construction industry. The study conducts a systematic literature review using descriptive and content analysis of relevant publications in prominent journals from 2010 to 2022. This review study provides an improved understanding of the state-of-the-art research of mass timber and contributes to the existing body of knowledge by opening new directions for researchers. Practitioners can use the results of the study to make informed investment and implementation decisions.

Climate change is a significant issue that may impact many aspects of our lives. The increase in global carbon emissions is one of the main reasons for such environmental consequences, and immediate action should be taken to manage the carbon footprint from human activities. Carbon releases from the construction sector contribute to a significant fraction of greenhouse gases being released into our atmosphere. Hence, various efforts have been made to manage the amount of carbon emissions in construction projects, such as developing standards, methods, and tools. While many earlier studies were associated with conventional analytical methods for estimating carbon discharges, recent studies have focused on monitoring the actual status of carbon emissions in a project, enabling project teams to take timely responses to minimize releases. This paper reviews carbon emissions monitoring practices that can be applied in construction projects and provides a descriptive analysis. The review explains the type of technology adopted and indicates the project life-cycle stage that each application was designed to be used. The findings from this paper will provide an overview of the state-of-the-art practices for carbon emissions monitoring and identify future directions for research.

Buildings account for 40% of total energy demand in the US. Consequently, there is a pressing need for a dataset that provides comprehensive information on the energy consumption of household units in the US. The current practice on large-scale energy simulations may not reflect the actual energy consumption patterns. Additionally, the existing national building energy datasets, such as the RECS, have a limited number of datapoint and do not reflect the social aspects of the households. This study aimed to create a large-scale national residential building energy dataset using a two-stage machine learning approach, combining two national datasets of the RECS and the AHS. The outcome of this study is a large-scale and comprehensive national dataset that contains information about energy consumption in household units as well as their detailed building features. Three machine learning algorithms, including artificial neural networks (ANN), random forest (RF), and gradient boosting regression (GBR), were used to develop a data-integration framework. The results showed that RF had the best performance in predicting the end-use energy consumption. Additionally, the predicted energy consumption in the generated large-scale dataset had an accuracy of over 80%. These findings have significant implications for energy-efficient building design and operation.

The tradeoffs between energy efficiency and thermal comfort in office buildings have been studied for decades. To date, researchers have proposed various strategies, such as occupant-based control strategies and personal comfort systems. Notably, operations of office buildings are embracing flexible work arrangements which allow occupant mobility, especially in the post-pandemic era. Research efforts on space recommendation strategies that match the needs of personal thermal preferences with different space conditions have been explored. However, existing literature lacks simultaneous quantification of energy consumption and thermal comfort at the whole building level when implementing flexible space usage. This paper quantitatively evaluates the influence of space match strategies on energy performance and thermal satisfaction in three typical office buildings of different sizes using the summer season. Our results demonstrate up to 22% cooling electricity savings and 15.7% increase in comfort probability across all simulated scenarios. These findings indicate that implementing space match strategies could improve building performance in both energy efficiency and thermal comfort to varying degrees.

As extreme weather events (e.g., heat/cold waves) are likely to become more frequent and more intense with climate change, it is vital to take action to mitigate their impact on the environment, society, and infrastructure. Therefore, identifying the thermal resilience of building envelopes under extreme weather conditions is important to adapt the buildings for long-term changes in climate. A majority of previous studies evaluated thermal performance of building envelopes under steady-state conditions. In contrast, this study introduces an experimental framework to evaluate the dynamic thermal performance of building envelopes (e.g., wall panels) under non-steady-state condition, by utilizing the measurement of their dynamic thermal properties. The analysis is carried out through the use of two key metrics, decrement factor (DF) and time lag (TL), which are employed to quantify the dynamic thermal resilience of building envelopes. This framework consists of five steps, namely weather data collection, experimental setup preparation, tuning phase, extreme weather simulation, and TL and DF calculation. The developed framework will aid in developing energy-efficient and resilient buildings to adapt to climate changes. Moreover, decision makers (e.g., designers, architects, manufacturers) will be able to better understand how building envelope design can impact energy efficiency and indoor thermal comfort.

In response to the industry’s significant contribution to global greenhouse gas (GHG) emissions, many architecture, engineering, and construction (AEC) companies have developed climate pledges. Climate pledges are commitments made by entities (e.g., companies and governments) to lower their contributions to climate change by reducing their GHG emissions. Approximately 30% of the top 100 design firms and 100 contractors ranked by revenue by Engineering News Record have made formal climate pledges. The researchers hypothesize that adopting corporate climate pledges may be associated with various corporate and geographic characteristics, including headquarter location, revenue, and ownership type. Thus, this study describes statistical associations between firm characteristics and climate pledges in the AEC industry, using a dataset that focuses on the top 100 contractors by revenue. This work finds that corporate factors are more likely than geographic factors to be associated with climate pledge adoption and contributes to the literature to guide strategies for climate action.

Over the last two decades, natural disasters have caused over $2.97 trillion in economic losses, 1.23 million deaths, and affected more than 4 billion people through injury, loss of housing, displacement, and/or requiring emergency aid. Data have shown that physical, social, and economic inequities play a significant role in the vulnerability of communities particularly post-disasters. Low-income communities have more than four times as many deaths per disaster, experience more severe and long-lasting infrastructure damage, and often receive delayed disaster recovery. Therefore, this causes prolonged and widespread homelessness as well as an inevitable temporary housing crisis. This research proposes an innovative short-term Origami temporary emergency housing solution, which offers an equitable and affordable solution ensuring potential victims are offered quick shelter post-disasters. This study aims to address the challenges of post-disaster homelessness through (1) understanding existing natural disaster housing solutions, (2) identifying post-disaster vulnerabilities pertaining to temporary housing needs, and (3) proposing Origami shelter solution and validating its feasibility and applicability through surveying engineering and architecture experts. The results of this study show the urgency for quick-assembly temporary emergency housing that allows victims to safely live and return to routine activities while infrastructure systems and homes are being repaired or rebuilt.

Green building design and construction are important in mitigating the health and environmental effects of indoor air pollution and climate change. The Leadership in Energy and Environmental Design (LEED) rating system becomes essential for ensuring cost-effective, healthy, and environmentally friendly buildings. While the list of LEED certification credits can collectively contribute to sustainability in buildings, the comprehensive compliance of LEED-certified buildings with the entire list is still questionable. In this respect, this study investigates the key categories contributing to the highest LEED scorecard points and explores the relationship between different LEED rating parameters. This study compares LEED ratings of 40 buildings certified by LEED v4 with the highest point totals and analyzes their scorecards in various categories. The findings suggest how different LEED categories can be synergized with each other for an effective certification process. The patterns of current LEED certified buildings can be used to advance green building practices and can guide future research directions, ultimately contributing to sustainability in built environments.

Post-occupancy evaluation (POE) is crucial for assessing indoor environmental quality (IEQ) and identifying areas for improvements. Traditional methods such as paper-, web-, and polling-based surveys face challenges of discontinuity, high costs, low response rates, and participation bias. To address these issues, a mobile robot platform with IEQ sensors and a touchscreen interface was developed to gather real-time feedback while monitoring built environments. A seven-day experiment in three test bed offices involving 34 participants showed that (1) compared to the email survey, the robot remarkably increased the response rate from 12.5% to 90.18%, and (2) participants found the robot useful with an average usability score of 72.8 (out of 100). Besides, among 23 valid exit survey responses, 78% preferred responding to surveys via the robot, and 87% expressed willingness to interact with it again. This study demonstrates the feasibility and usefulness of using mobile robots for continuous POE in real-world scenarios.

Envision rating system is a globally used infrastructure sustainability rating system that assesses all types of infrastructure projects and fosters the necessary improvements to achieve sustainable performance. The Envision framework consists of 64 credits, and based on the achieved points under these credits, infrastructure projects are awarded one of four levels: verified, silver, gold, and platinum, which in turn indicate how well the project addresses the sustainability indicators. This study explores the Envision-certified projects to date (2013−2023) and reviews the project trends, and project types as well as investigates possible correlations between the Envision award levels and various project factors including the project sector, type of infrastructure, project location/country, and year of the award. The findings of this study provide an overview of current trends in the sustainable infrastructure industry, identify and motivate various project sector to pursue sustainability certification, and provide direction for future research to exacerbate developing sustainable infrastructure.

To mitigate the impacts of climate change on infrastructure, there has been a growing trend toward adopting resilience strategies in infrastructure planning. However, research highlights historical discriminatory practices and biases in policies and investments, resulting in disproportionate disaster impacts on communities. To effectively evaluate infrastructure resilience, it is crucial to consider these disparities. To address this need, this study focuses on assessing infrastructure resilience while incorporating disaster inequality and disaster vulnerability using a proposed equity-integrated resilience evaluation model. The resilience of infrastructure in Florida counties with different characteristics (e.g., spatial, demographic, and socioeconomic statuses) was evaluated and compared. The findings reveal that (1) the more socially vulnerable counties experienced greater disaster inequality, (2) there is a higher percentage of disaster vulnerable counties in the rural group in high-intensity hurricanes, and (3) the infrastructure of the inland counties, collectively, has weaker resilience compared to coastal ones.

Natural hazards can disrupt various aspects of the community members’ lives such as satisfaction with the solid waste management and debris removal system. These impacts can be disproportionate across communities with different socio-demographic backgrounds. This study is aimed at identifying such inequalities. To that end, a case study of the Houston area in Texas under post-Harvey situation is conducted. Satisfaction with waste and debris management services is taken as a sample metric for post-disaster well-being of community members. To quantify this metric, the 311 phone call data, which pertains to municipal service requests, is used. The dataset comprises the topic, location, and the date of municipal service requests. The number of active service requests related to waste management is taken as the metric for the related community well-being domain. Socio-demographic disparities in post-disaster community well-being of the affected zip codes are then explored. The results showed that zip codes with lower income level, more single-parent families, and multi-unit houses were affected the most by Harvey. The study outcomes can inform policymakers in identifying the zip codes that sustained disparate impacts caused by Hurricane Harvey. The proposed step-by-step method can be expanded to identify disparities with respect to other well-being domains, and for other locations.

This paper presents a regression-based approach for identifying the impact of county-level capacity building strategies on post-hurricane recovery pattern of cellular networks considering the impact of infrastructure interdependencies. The proposed approach models the recovery rate of the cellular network in a county, based on hurricane damage to cellular network, electricity grid, recovery of electricity grid, and the Baseline Resilience Index for Communities (BRIC). To demonstrate the applicability of the proposed approach, the recovery rate of the cellular network after Hurricane Ida was regressed on the set of predictors using a Tweedie regression. Historical data on the damage and recovery of the cellular network after Hurricane Ida was collected from the US Federal Communications Commission’s reports. The recovery rate of the cellular network and the grid was calculated by fitting an exponential distribution to their recovery curves. The regression coefficients indicate statistically significant relationships between the recovery rate of the cellular network and the level of damage of the cellular network and grid, recovery rate of grid, and BRIC index. The coefficients could be used to estimate the effect of the capacity building strategies to maximize the rate of recovery of the hurricane-damaged cellular network.

Early-warnings and situational awareness during disasters are critical for protecting our built environments and communities. In recent years, the increasing prevalence of social media platforms has presented an unprecedented opportunity for gathering real-time information, facilitating rapid hazard detection, evacuation plan propagation, and damage assessment and recovery. However, underdeveloped countries have been inadequately explored compared with developed countries across different spatiotemporal scales. To address this knowledge gap, this study investigated online public responses before, during, and after two recent hurricanes, aiming to identify the different patterns between underdeveloped and developed countries at different stages of disasters. Twitter data of Hurricane Eta in Honduras and Nicaragua and Hurricane Ian in the US was collected and analyzed using sentiment analysis and topic modeling. The findings revealed that individuals in Honduras and Nicaragua tended to express more negative attitudes and had more concerns about disaster damage than those in the US. Sentiment differences were also found between moderately and mostly affected regions in the US. Recommendations for future research directions and disaster response practices were proposed based on the findings.

A shift to the circular economy is inevitable in the construction sector for sustainable development, as it promises solutions to two main problems of the industry—dealing with the waste generated and overcoming the shortage of raw materials. This study tries to identify circular practices in the construction industry, constraints to their wide acceptance, and recommendations to promote them. At the end-of-life of a building when it is demolished, a large amount of waste is generated, and its handling is a major problem. In this study, life-cycle analysis methodology is adopted to analyze various scenarios after useful life of a model building. The environmental impacts are calculated in terms of carbon emissions, taking into account two life cycles of building, considering options of deconstruction and demolition. Total carbon emissions are calculated for different scenarios, and the results show a significant reduction in the impacts for circular strategies.

Water insecurity is a condition that arises when individuals lack access to a sufficient, dependable, and safe supply of water to meet their fundamental needs for sanitation, hygiene, and drinking. In 2017, Hurricane Maria devastated Puerto Rico’s water infrastructure, resulting in increased water insecurity, potentially contributing to mental health problems such as depression. This study examines the correlation between household water insecurity measured using the Household Water Insecurity Experiences (HWISE) scale and the depression levels measured using the Center for Epidemiological Studies-Depression (CES-D) scale of individuals residing in Puerto Rico. To achieve this goal, we conducted 154 surveys with respondents in Puerto Rico from May to July 2022. Preliminary findings from multiple linear regression analyses indicate a potential association between depressive symptoms among residents and water insecurity. The findings of this study highlight the importance of increasing access to clean piped water within residential homes. To achieve this, future research could explore potential options such as revising water distribution system requirements and enforcing federal water quality standards in Puerto Rico. By implementing these measures, policymakers and relevant authorities can work towards mitigating water insecurity and ensuring a safer and more reliable water supply for the population.

Lightweight cellular concrete (LCC) can substitute the thick aggregate layers within permeable parking (PP) lots. Nevertheless, LCC’s lightweight may cause buoyancy forces that damage the PP lots. Increasing LCC density reduces buoyancy forces, adversely affecting PP lots’ permeability and runoff storage capacity. Thus, this study assesses LCC feasibility in PPs and its influencing factors. First, the study conducted lab testing to develop various porous concrete and LCC materials with adequate strength while complementing permeability. Later, this study developed an optimization model that maximizes using LCC materials while avoiding buoyancy and meeting the required runoff storage capacity. Finally, this study demonstrated designing permeable parking lots for various rainfall intensities utilizing the lab-developed LCC materials and optimization model. Results indicate that combining aggregate and LCC layers is beneficial, with LCC reducing the thickness of aggregate layers by 75%−85%. The key factors affecting PP lots’ hydraulic performance are the LCC density and void ratio.

Millions of tons of construction and demolition waste are sent to landfill sites each year. The circular economy (CE) production and consumption model aims to reduce waste and extend the usage lives of products and infrastructure. Implementing CE in construction enhances sustainability by reducing resource use. In the past decade, barriers and drivers for CE implementation in construction have been identified. However, their applicability may vary in the US due to cultural differences. There is currently a lack of studies examining the perception of specific barriers and drivers related to CE practices in the US. This study assesses stakeholders’ views on the barriers and drivers within the US construction industry. Data were collected through an online survey completed by 63 CE and sustainability, architecture, engineering, and construction stakeholders. Information was analyzed using the relative importance index and the Kruskal-Wallis test. The study findings highlight the top three barriers hindering CE implementation, which include a lack of experience, unfamiliarity with design and construction techniques, and insufficient performance information on reclaimed construction materials. While the top three drivers that promote CE implementation are environmental sustainability, image enhancement, and gaining a competitive advantage.

Energy burden, that is, the percentage of household income spent on energy utilities including heat and electricity per year, remains a major concern for low-income households in the US. High energy burden is estimated as more than 6% of household income. Energy efficiency programs are major strategies used to address energy burden for low-income communities. Despite their potential to reduce energy cost, the adoption of energy efficiency programs remains low. Also, anecdotal evidence suggests that some energy efficiency measures are not being used by homeowners as expected. This study systematically reviews literature on the social determinants of the use of energy efficiency programs using national reports, journal articles, and conference proceedings. Also, the paper applies a framework from the literature review to an Alaskan community using interview data (n = 40). Preliminary findings from the literature review showed five major themes: communication of logistics, social behaviors, financial benefits, indoor environmental quality, and pro-environmental attitudes. However, the qualitative study indicated that majority of people in Alaska communities have limited experience with energy efficiency programs. Poor communication of logistics was another key barrier to participation. Energy efficiency organizations should therefore improve advertising strategies and simplify application processes, if possible, to encourage participation in Alaska communities.

The COVID-19 pandemic has urged airports to seek opportunities for innovation and to redefine a sustainable and resilient path to recovery. Yet, there is a lack of information on how airports of different sizes in the US have used technology to recover from COVID-19. This paper addresses this shortcoming by exploring resilience management practices among airports of different sizes located in Eastern USA. The results presented in this paper stem for a larger effort which featured interviews with consultants and solution providers along with a nationwide survey of airport authorities. Here, results obtained from 20 airports in Eastern USA are provided in depth. The results highlight preparedness and recovery opportunities among airports of different sizes and provide important categorical insights on the state of practice in terms of resilience management practices and the use of advanced technologies that can advance resilience. The overarching insights can benefit authorities by unearthing the challenges the industry faces and by providing them with guidance while adopting new resilience initiatives or solutions.

In 2017, Hurricanes Irma and Maria caused catastrophic damage in Puerto Rico. This study explored the effects these hurricanes had on residential construction practices during the first year of reconstruction. Semi-structured interviews were conducted with residential construction stakeholders impacted by reconstruction efforts. Residential structures built using current building codes experienced minimal storm damage. Conversely, significantly damaged residential structures were commonly built using informal construction practices, utilizing light wood framing methods. These homes were commonly not insured nor eligible for government assistance. Without assistance, the damaged structures were again being rebuilt using informal practices, thus setting the stage for further damage during future hurricane events. The cyclical nature of these informal construction practices is contrary to the nature of sustainable construction, in terms of resource consumption and social impacts. This research can be used to help prioritize reconstruction efforts and ultimately make changes to better prepare for other similar disasters.

Leadership in Energy and Environmental Design (LEED) provides valuable criteria for evaluating the sustainability of construction projects. However, it is not clear if in this point-based system, the evaluation credits are realistically and practically developed and weighted. To understand this, all the LEED-NC (v4) certified projects in the US were investigated (n = 797). The data was collected from the US Green Building Council (USGBC) website. A model was developed based on the predefined relationships between credit categories and the overall LEED scores. After validating the model through confirmatory factor analysis (CFA), the actual contribution of each credit category to the overall sustainability level was evaluated through structural equation modeling (SEM). The expected and actual contributions were then compared. The results showed that the expected contributions of the credit categories are not consistent with the actual influences thus demonstrating the need for redefining some of the credits and their corresponding weights.

Buildings are responsible for the largest portion of energy consumption on US electric grids. The wide participation of buildings in demand side management (DSM) through modulating or shifting electricity end uses, particularly in homes, can support decarbonization goals and increase reliability of electric power supply. The awareness and willingness of households to adjust internal loads, housing occupancy, and household energy consumption patterns all play an important role to support the potential for DSM. A particularly challenging type of housing to reach in DSM is rental housing. Historically, this type of housing has been plagued by split incentives that limit the motivation of homeowners (landlords) to improve the energy performance of these buildings since they often do not pay the utility bills. DSM presents an opportunity to support reducing the utility bills of renters through control adjustments rather than requiring the landlord to invest in energy efficient technologies. This study aims to identify household occupancy schedules and potential factors that may influence willingness to participate in DSM among renters, in particular college students. A survey-based method was conducted among 55 college students majoring in civil, environmental, and applied engineering, and showed that, while the targeted population has low awareness about DSM strategies, they would be willing to participate in a DSM in the future. The factors that appear to drive willingness to participate in DSM for this population were related to the potential reduction of electricity costs and energy savings.

Sustainability performance indicators (SPIs) are flexible tools developed based on specific frameworks to provide integrative strategies to assess sustainability. Indicators represent the pressing concerns and reflect the interests of project stakeholders. The residents’ satisfaction level and the perspective of experts provide a solid base for identifying SPIs. Several frameworks and sets of indicators have been used. The triple bottom line +1 (TBL+1), which indicates social, environmental, economic, and governance aspects, was used here as an integrative framework to assess sustainability. This paper identifies and summarizes the significant potential leading sustainability performance indicators (PLSPIs) for low-income housing (LIH). A systemic literature review adapting three research design stages was used to identify and summarize the significant PLSPIs. Due to the length limitation, the paper presents two sets of PLSPIs, the PLSPIs related to social and environmental aspects. These sets include 10 social PLSPIs (SPLSPIs) and 14 environmental PLSPIs (EPLSPIs).

The world has been under the thumb of the global coronavirus (COVID-19) pandemic since 2019 and is still recovering from its effects on society and industries. Data centers provide critical information technology supporting infrastructure for organizations and businesses. Although designated as essential construction that remained operational, data centers were not immune to the pandemic’s adverse influences. This study identifies the unique challenges faced in the construction of data center projects through an in-depth qualitative case study analysis of five large data center projects built during the height of the pandemic. Insights were gained through interviews with 14 project managers and clients overseeing these projects. Thematic analysis revealed an overall emphasis on health and safety. Supply chain disruptions, schedule delays, financial implications, reduced productivity, and contractual changes were also highlighted. Data centers are critical facilities and are in increasing demand as such should be adequately prepared to address future disruptions.

Sustainable transportation infrastructure has profound social, environmental, and economic benefits for both individuals and societies. Recent years have witnessed a growing interest in this domain, and multiple studies have been conducted over the past decade. However, the existing body of knowledge concerning the present status of research within the domain of sustainable transportation infrastructure is limited. This study intends to identify the most significant articles categorically, track the recent trends of publications, examine the most common keywords, highlight their interconnections, and determine the most influential authors in the field of sustainable transportation infrastructure. The visualization analysis method based on bibliometrics, including performance analysis and scientific mapping, was performed. The performance analysis method involved studying the reviewed publications based on their types and topics and exploring the year-wise trend of publications. The scientific mapping method involved the keyword co-occurrence analysis and citation analysis. The outcomes of this study provide a comprehensive and structured understanding of the existing research landscape; highlight influential articles, authors, and trends; and offer insights that can guide future research efforts, policy-making, and industry development within the realm of sustainable transportation infrastructure.

This study conducts a systematic literature review to evaluate existent studies on the use of multi-criterion decision-making (MCDM) methods for selecting building envelope materials and identifies the various criteria and weight distribution techniques employed in this process. The results and discussion section focuses on the environmental impact, cost, technical performance, and social impact criteria, as they were identified as the predominant criteria among the reviewed studies. The results demonstrate that environmental impact and cost criteria consistently carry higher weights in MCDM studies in comparison to the social impact criteria. It is also found that, while numerous MCDM methods have been employed in the building envelope material selection process, the use of a hybrid MCDM approach is found to be the most recommended course. Overall, the findings of this study assist construction specialists in understanding the current practices and limitations in material selection processes for building envelopes and provide insights for future research in this area.

The construction industry largely contributes to the increase of greenhouse gas (GHG) emissions, accounting for 37% of the total energy-related emissions worldwide. Research efforts have focused on alternatives to decrease GHG emissions levels in the operational phase of buildings, while limited studies focused on their construction phase. In this context, this research reviews the latest technologies and strategies to reduce GHG emissions during the construction phase by considering the application of different building materials, construction alternatives, and GHG emissions predictive tools in the residential sector. The systematic review is organized based on the PRISMA 2020 guideline, and the results are analyzed using VOSviewer. The contribution of this study is the consolidation of state-of-the-art approaches to reduce GHG emissions during the construction phase of residential buildings, which can assist construction practitioners in taking effective measures to address and mitigate GHG emissions in this particular phase.

Demolition waste is a significant issue in the world. Over the years, different methods for extending the lifespan of construction materials and reducing demolition waste through reuse and recycling have been investigated. Specifically, concrete bridges have significant potential for material recovery due to not only the substantial amount of materials used but also the possibility of their retaining nearly the same or similar properties across bridge life cycles. Most bridges are demolished not due to their structural deficiency but because they are functionally obsolete. In the literature on circularity and waste management, however, bridges are rarely considered. This paper quantitatively assesses the reusability and recyclability of different components of a bridge in Florida. Moreover, the reuse and recycling costs for bridge components and their resulting material savings are calculated and compared with their non-sustainable demolition options to decide the most sustainable but still economically feasible options based on their benefit-cost ratios. This study specifically examines how parameters like component uniqueness, time, and transportation affect the feasibility of bridge components for reuse or recycling and suggests the most sustainable demolition plan.

This paper explores the variations in community resilience factors between rural and urban counties across the US, focusing on regions at relatively high risk of riverine flooding. Leveraging data from the FEMA National Risk Index, US Census Bureau, and BRIC Community Resilience Index, the study explores the relationship between community resilience and the degree of rurality. The analysis identifies statistically significant differences between urban and rural counties and identifies the extent of Spearman’s correlation between each factor and percent rural. Rural counties facing elevated riverine flood risks exhibit comparatively lower community resilience in social, economic, and infrastructure/housing. Conversely, urban counties have a lower resilience in community and environmental aspects. These are more pronounced than seen nationally; while the directions of the trends nationally are similar, the correlations differ in magnitude. Notably, these higher-risk rural counties have a greater need for infrastructure/housing resilience (ρ_ih = –0.78) and have a greater level of community and environmental resilience than their higher-risk urban counterparts (ρ_c = 0.47; ρ_e = 0.52) as compared generally in the US (ρ_ih = –0.49; ρ_c = 0.30; ρ_e = 0.34). This study contributes to resilience literature by identifying aspects of community resilience within high-risk riverine flood-prone areas that are unique compared with the general urban/rural dynamics in the US.

This review article focuses on the use of artificial intelligence (AI) in sustainable construction management. By employing keywords and filtering through the Scopus database, 95 relevant articles were analyzed using VOSviewer for quantitative analysis. The researchers also conducted a qualitative analysis to identify core research themes. The article provides an overview of the current state of AI development in the construction industry, with a focus on sustainable construction. It aims to inform industry professionals, academics, and policymakers about past research and future trends in using AI tools to enhance the synergy between the construction industry and sustainability. By presenting the findings of this study, the article contributes to the body of knowledge and assists stakeholders in making informed decisions regarding the integration of AI in sustainable construction management.

According to statistics, the majority of Louisiana’s urban coastal area is low-income, out of which a significant percentage is susceptible to issues dealing with hazards and the relevant consequences. On the other hand, the increasing number of hazards along the Mexican Gulf Coast has become a severe concern for governors. In this study, social vulnerability to extreme floods for New Orleans urban area is evaluated through four phases. The indicators of social vulnerability are identified, and using a statistical analysis process the most important indicators are determined. Also, Social Vulnerability Index (SVI) is estimated and mapped, and the flood risk maps are generated. Finally, by merging the SVI map and the flood map, the social vulnerability exposure map is generated. The results revealed that four key factors that account for 78.60% of the variation in the overall dataset include age (31.3%), urbanization (21.5%), employment status (13.7%), and education level (12.1%). The output of this research can help stakeholders and decision-makers lessen impact of extreme flooding on the social life of vulnerable communities living in coastal areas.

Traditional energy production has inadvertently led to climate change and energy scarcity. In response, renewable energy has emerged and is continuously expanding to meet our demand for clean, sustainable, and renewable power. One promising avenue is the utilization of piezoelectric harvesters to extract energy from roadways. However, the initial embedding costs of these harvesters can be high. Therefore, conducting an economic analysis is crucial to determine the viability of investing in this new renewable energy source. This study aims to assess the economic feasibility of implementing this technology through the application of life-cycle cost (LCCA) and comprehensive economic evaluations. By examining a real-life hypothetical scenario, this study demonstrated that this system would incur higher costs over the study period. However, sensitivity analysis reveals that modifying certain factors, such as reducing the prototype cost to below $6, would result in a positive net saving.

Water insecurity, a global issue, particularly impacts Alaska Native communities due to poverty and social disadvantages. Historically, poor water access and infrastructure affect over 30 Alaska Native communities. Despite some having piped water access, mistrust of tap water prevails, leading to reliance on potentially unsafe alternatives like river and rainwater. To bridge the gap in understanding tap water mistrust among Alaska Natives, we conducted interviews with a Norton Sound Health Region community. Findings reveal tap water quality perceptions, knowledge of pipe issues, health concerns, and supply disruptions drive mistrust. This research emphasizes the link between mistrust and infrastructural problems, aiding policymakers in prioritizing resources for water infrastructure development and maintenance.

Contaminants, such as lead, in water systems pose significant public health risks. Despite growing public awareness, the presence of lead service lines remains a concern in water systems. Consequently, there is a need to ascertain spatial and demographic trends in lead contamination to identify regional areas to monitor and to inform outreach campaigns. Leveraging lead water quality data collected in Chicago, we use Poisson regression analysis to identify the zip code-level demographic drivers of high lead levels. Our model showed that the percentage of Black residents and the percentage of buildings built before 1980 were significantly associated with the percentage of samples with more than 15 ppb of lead, the EPA standard. Such trends shed light on underlying issues that may contribute to lead contamination, such as historic inequities in the built environment. These findings will assist decision-makers in prioritizing capital projects and educational campaigns, ultimately improving water services.

The evolving concept of smart infrastructure is redefining operational performance toward optimizing the performance of infrastructure systems by leveraging the asset value instead of expanding the existing systems. This can be achieved by leveraging the value of asset data, which is vital for efficient infrastructure management practices. However, state departments of transportation (DOTs) face several challenges in managing transportation asset data throughout the asset life cycle. As such, this paper investigated the current practices in implementing asset management systems (AMS) to manage ancillary transportation asset data, the maintenance management systems’ (MMS) integration with the overall maintenance framework, and the nature of the adopted systems. Furthermore, this study explored the status of adopting AMS to manage the data for 25 ancillary asset classes. A web-based survey was developed and distributed. The results are encouraging and illustrate an increasing interest of state DOTs in implementing AMS to manage ancillary transportation asset data.

Although technologies benefit the horizontal construction sector, investing in them doesn’t guarantee that state departments of transportation (DOTs) can reap the technologies’ fullest potential. Thus, it is important to provide DOTs with proper guidance to match the capabilities of the technology to the requirements of the DOTs’ tasks. As such, this study presents a tool to evaluate the fitness of transportation technologies for construction highway and asset management tasks (CHAMFiT). This tool focuses on wireless and sensing technologies, including radio frequency identification, e-ticketing, ground penetrating radar, unmanned aerial systems, light detection and ranging, geographic information system, and global positioning system. Drawing on previous research, surveys, interviews, and a workshop, CHAMFiT maps common activities performed by state DOTs during construction and asset management phases to the characteristics of the investigated wireless and sensing technologies. The developed tool will help state DOTs make insightful decisions to implement wireless and sensing technologies successfully.

The resilience of pipeline infrastructure is pivotal to the safety of the industries and residents. Aging issues of pipelines, such as time-induced deterioration, pose a significant potential for leaks or even explosions, with devastating consequences for lives and properties. To address the challenges, epoxy resin liner emerges as a practical trenchless solution to rehabilitate pipeline with minimal interference to surrounding environment. In addition to pure epoxy, we introduce epoxy/carbon nanotube (CNT) composite as an alternative structural lining material to assess the improvement brought about by nano additives. The research investigates the effectiveness of structural liners in underground pipeline maintenance and rehabilitation through a three-dimensional finite element analysis (FEA) model. Computational models are constructed to simulate steel gas pipes and structural liners under various pressurized conditions. Through a comprehensive parametric study, we explored the effect of various factors, including liner thickness and the addition of CNTs, on the mechanical performance of both the pipeline and the structural liner. The findings guide trenchless construction and maintenance procedure, ultimately enhancing the safety and reliability of pipeline infrastructure.

Weigh-in-motion (WIM) and automatic traffic recorder (ATR) devices, particularly the former, serve as indispensable sources of traffic data for mechanistic-empirical (ME) pavement design. However, the sparsely distributed WIM and ATR devices are unable to cover the entire roadway network in Indiana. The current truck weight road groups (TWRG) were developed using WIM traffic data of 20 years ago to reflect regional traffic conditions in Indiana. Because of the outdated traffic data, the existing TWRG classes fail to accurately reflect the actual traffic conditions, especially in the cases of extremely low and high truck traffic volumes. This study was performed to derive new TWRG classes using the newly collected WIM traffic data to provide accurate traffic information for pavement design. Consequently, employing the new TWRG values can significantly improve the quality of pavement design, resulting in considerable cost reduction of highway projects.

The Black Belt region of rural Alabama has struggled from a lack of access to managed wastewater infrastructure, resulting in the presence of raw wastewater on the ground and significant risks to public and environmental health. In addition to soil conditions that cause hydraulic failure in conventional septic systems, the wastewater crisis in the Black Belt is further exacerbated by several constraints, such as high poverty and low-population density. It is imperative to holistically understand the nature of challenges underlying the Black Belt’s wastewater crisis, bringing in-depth insights into the complexity of the operating environment. To do so, this study identifies wastewater challenges, spanning technical, financial, social, institutional, and environmental dimensions. We applied hybrid deductive-inductive qualitative content analysis on eight semi-structured interviews with 11 stakeholders involved in the wastewater sector in Alabama to better understand wastewater issues and barriers to improvements. Results show that inflexible funding processes—such as requirements for communities to match funds and have technical capacity to manage them—strain Black Belt communities’ competitiveness to secure wastewater infrastructure funding. Findings highlight the need to audit funding systems to consider rural, underserved communities’ financial constraints, thereby ensuring more equitable allocations and potentially improving wastewater access in these communities.

State and local highway agencies spend substantial budgets repairing infrastructure damages caused by snow and ice every winter season. These infrastructure damages can be prevented by establishing effective winter maintenance strategies which require monitoring road surface temperatures (RST). Road weather information systems (RWIS) are a common source for obtaining data on both current and predicted RST. However, RWIS does not exist in many locations, making it challenging to monitor road surface temperature in locations where RWIS is not available. The objective of this research is to assess the applicability of deep learning models in estimating RST using weather forecasts from the National Weather Service which are provided in grid cells over the continental United States. The deep learning models were developed based on collected data on actual RST and associated weather forecasts from roadways in North Texas for two winter seasons. The accuracy metrics of the deep learning models were computed using mean absolute error, root mean square error, and R-squared. According to the results, the developed deep learning model can predict RST with a root mean square error of 1.71°C, mean absolute error of 1.35°C, and an R-squared value of 0.92. The proposed models can be incorporated into a winter maintenance decision support system, enabling the monitoring of the RST without dependence on RWIS.

Third-party damages to the subsurface infrastructure are a persistent issue that negatively impacts the integrity of the underground infrastructure and its vital services. Hundreds of thousands of third-party damages occur every year in the United States. These damages are more likely to reoccur when ignoring the value of learning from them. Therefore, this study investigates 16,937 damage reports that occurred in North Carolina in 2020 and were reported to North Carolina 811. The findings suggest that undesirable excavator practices contribute the most to third-party damages, followed by locator practices, general industry practices, and utility owner practices. In addition, the finding shows that most of the reported causes represent the direct causes of damages, not the root causes. Identifying the root causes is critical for a sustainable approach to significantly reducing damages to underground utilities. Overall, this study identifies the current direct causes of damages and weaknesses that hinder the industry from acquiring the needed knowledge to prevent damages to subsurface utilities.

State departments of transportation (DOTs) have been transitioning from manual to automated pavement condition surveys with the advantages such as safe and speedy data collection and consistency and repeatability in data collected. The manual and automated data collection methods have inherently different capabilities for measuring pavement distress. When state DOTs use pavement performance indicators developed based on manually collected data and have already introduced an automated data collection process, the different capabilities can be problematic by either overestimating or underestimating actual pavement conditions. This study investigated the effect of the condition data of automated pavement surveys on a manual-based pavement performance indicator through a case study. The case study was a structural cracking index (SCI) used for the West Virginia Division of Highways. This study investigated the effect of automated pavement condition surveys on manual-based pavement performance indicators. The study found that automated surveys can lead to changes in distress data compared to manual inspections, suggesting non-random change patterns. These changes can lead to significant budget losses for state agencies. The study also demonstrated the applicability of the approach used for a case study for other state agencies to evaluate their current pavement performance indexes.

Insufficient subsurface information and soil mischaracterization contribute to significant delays and cost overruns in up to 50% of all infrastructure projects. Adequate and accurate determination of soil-index properties such as plasticity index and fine/clay fraction which affect the shear strength of clayey soils help assess infrastructure stability, especially in locations with extreme wetting-drying cycles such as North Texas. Due to the inherent limitations of geotechnical site investigation methods, extensive research has been conducted to establish relationships between various soil-index and geoelectrical properties of clayey soils. However, little to no efforts have been made to quantify the effects of plasticity index, fine fraction, and clay fraction on the electrical resistivities. This paper investigates the relationship between these variables using correlation and linear regression analyses. Other influencing variables such as water content and dry unit weight were also used in the analyses. Conventional laboratory electrical resistivity tests and physical property tests were performed on 56 soil samples taken from some locations across Texas, US (842 observations). The results show statistically significant relationships between clay and fine fractions, plasticity index, and electrical resistivity. The results also show that the electrical resistivities are more influenced by the plasticity index than the fine and clay fractions. The proposed model in this paper provides practical tools to continuously determine the soil-index properties for assessing site conditions and infrastructure stability.

Increasing water shortages, droughts, and global warming demand methods to rapidly detect underground water leaks. Conventional techniques are costly, time-consuming, and error-prone. However, remote sensing techniques can offer innovative solutions. Previous studies mostly used optical sensors. However, optical data has limitations, including noise interference, limited null subsurface penetration, and weather dependency. Therefore, the study in this paper aims at exploring the combination of radar satellite data and machine learning to automatically identify underground water leakages. Radar data offers sensitivity to soil moisture below the surface. Moreover, image texture features were leveraged from dual-polarized radar data to enhance prediction. Gray-level co-occurrence matrix texture features were combined with backscattering coefficients to create a feature space that could better train the random forest. Results indicate the ability to automatically detect 69% of underground leaks with subsurface moisture alone, which lists, tables, figures, display equations, footnotes, or references.

Due to needs of maintaining pavements located in statewide, the “one size fits all” approach for estimating pavement conditions no longer works as pavement deterioration depends on roadways’ characteristics. Understanding how pavement deterioration probabilities vary based on diverse factors is critical to improve accuracy of pavement condition estimations. The objectives of this paper are to build empirical Markov Chain pavement deterioration models and analyze how transition probabilities of pavement condition differ depending on multiple factors. The data of the Georgia Department of Transportation pavement inspection records from 2017 to 2021 is used to develop Markov Chain models and evaluate pavement condition transition probabilities. The major finding of this research is that pavement condition transition probabilities significantly vary depending on highway system types, traffic volume, and annual average temperature. It is anticipated that findings from this research will help highway agencies to improve accuracy of long-term forecasting of pavement performance.

Infrastructure systems are known to be interdependent. Interdependencies among infrastructure systems are often revealed during disruptive events, such as natural disasters. Healthcare infrastructure plays a fundamental role in society after the occurrence of disasters facilitating the response and recovery of communities. This study aims to explore the role of interdependencies in the management of healthcare infrastructure in response to natural disasters such as earthquakes. The authors carried out 15 semi-structured interviews during 2022 and 2023 with stakeholders involved in managing healthcare infrastructure in Chile that were qualitatively analyzed. Our results show that protocols exist to manage healthcare infrastructure during disasters; although the interdependencies between healthcare infrastructure and other urban infrastructure systems (e.g., water infrastructure) are recognized, existing protocols have given limited attention to them. Our results provide healthcare infrastructure agencies with insights to respond to future natural disasters understanding the importance of interdependencies with multiple infrastructure systems.

Prediction of bridge elements’ deterioration is an essential task in maintenance planning to preserve bridges in a functioning and safe state. Several stochastic and data-driven approaches are used in the literature to estimate deterioration of bridge elements. However, these approaches rely on expert judgments of deterioration parameters or subjective selection of deterioration factors. The objective of this paper is to predict health index (HI) of concrete bridge elements using machine learning methods based on National Bridge Inventory (NBI) and the National Bridge Elements (NBE) databases. To this end, entropy-based mutual information analysis is applied to evaluate the influence of different deterioration factors, such as daily traffic, location, and age on bridge elements deterioration. Moreover, several machine learning models are developed to identify the best method to predict bridge elements’ HI. Based on predictive performance metrics results, random forest method had the best performance in terms of mean absolute error, root mean square error, mean absolute percentage error, and coefficient of determination metrics for all the elements. The primary contributions that this research adds to the body of knowledge are (1) application of entropy-based mutual information to evaluate linear and nonlinear impact of different factors on bridge elements deterioration, and (2) development of new machine learning models to predict deterioration of bridges with various characteristics including age, daily traffic, and location. The models developed and presented herein are expected to support decision makers in identifying optimal time of interventions to minimize maintenance costs while maximizing bridge performance.

This study investigates the accuracy of utility records by comparing available utility records derived from traditional records research such as One Call systems and Subsurface Utility Engineering (SUE) Utility Quality Level B (QLB) investigations conducted as defined by the ASCE 38-22 standards. Focusing on a case study in South Dakota, a “linear foot” metric was developed to quantify discrepancies and assess missing utility records. The findings revealed significant disparities in documented utility linear footage between the two methods. SUE QLB investigations consistently identified a more significant extent of utilities. This underscores the necessity of incorporating SUE practices early in the project development stages to ensure accurate utility data. Hence, this study emphasizes the potential of SUE investigations in enhancing the efficiency and risk management of construction projects.

The site selection of public electric vehicle charging stations (EVCS) will have a long-lasting impact on people’s access to and use of EV, and thus long-term social equity. Since it is hardly possible to reinstall a public EVCS once it is built, site selections for EVCS should consider a fair share of benefits. In this respect, this research explores the evaluation criteria of social equity for guiding public EVCS installations through a comprehensive systematic review. This study will provide a comprehensive social aspect which synthesizes evaluation indicators and socioeconomic and demographic variables regarding EVCS installations toward fair infrastructure investment. The proposed complete social equity criteria can be utilized to investigate the patterns of community and social features so that socially acceptable, preferable, and equitable sites for EVCS can be suggested. This study will advance the body of knowledge on planning, design, and installation decisions of equitable public infrastructure.

Disaster reconstruction is costly, and given limited resources, prioritization of the most critical infrastructure elements is necessary. This paper summarizes a literature review exploring how post-disaster reconstruction is performed on a macro-level, meaning the integrated nature of multiple infrastructure systems. This research specifically focused on determining how decision analysis is carried out in real-time application and how those decisions may be influenced by the growing body of knowledge on reconstruction decision making analysis and optimization techniques. The literature reports many different processes that have been developed for prioritizing infrastructure reconstruction, including a number of academic models designed to aid prioritization efforts. For their part, governments and managers of infrastructure systems are making prioritization decisions as disasters strike, but it is unknown whether or not real-world decision-making routinely incorporates these academic prioritization strategies. These results will help inform further research in developing macro-level interdisciplinary decision-making models for effectively prioritizing infrastructure reconstruction.

Increased EV adoption leads to reduced revenue from gasoline sales taxes used for road maintenance, while placing additional strain on power infrastructure designed for predominately smaller loads. This paper analyzes the adoption of electric vehicles (EVs) and infrastructure health in the Colorado Front Range and San Francisco Bay Area. The study analyzes demographic factors affecting EV adoption and assesses potential adverse impacts on pavement and power infrastructure quality. As expected from the literature, we find income to be the strongest predictor for EV adoption. However, we find noticeable differences in other trends between these two areas that demonstrate the importance of a regional approach for modelling the impact of EV adoption on infrastructure. Regarding the impact of EVs on power and transport infrastructure, it is unlikely that EV adoption has reached a high enough level to significantly affect infrastructure in either areas. The results suggest, however, that in the Bay Area, people living in counties with high EV adoption and high levels of income are more likely to experience power outages. Overall, this study provides insights into the broader impacts increased EV adoption may have on pavement and power infrastructure.

The selection between rigid and flexible pavements is a critical decision in infrastructure development. Though studies have highlighted the potential benefits of rigid pavements, practical applications and substantial evidence to aid governmental decision-making remain scarce. Furthermore, there is a discernible disconnect between the design and life-cycle analysis processes in current practices. Addressing these challenges, this paper introduces PaveDecision.com, an online application that combines both rigid and flexible pavement design and life-cycle cost analysis in a single platform. With its user-friendly interface and capacity to process a multitude of input variables, this platform serves as a versatile tool for professionals and educators alike. Its computational accuracy has been confirmed through tests aligning with manual calculations in design standards. Ultimately, this research illuminates the potential of consolidated platforms like PaveDecision.com in enhancing pavement design processes, facilitating cost-effective decisions, and advancing sustainable infrastructure development.

Errata identify incorrectly published items and technical errors. This file contains errata that shall be used from their effective dates; the most recent effective date is 04 19 2024.