Journal of Infrastructure Systems

Smart city programs provide a range of technologies that can be applied to solve infrastructure problems associated with ageing infrastructure and increasing demands. The potential for infrastructure and urban improvement remains unrealized, however, due to technical, financial, and social constraints and criticisms that limit the implementation of smart cities concepts for infrastructure management. The discussion presented here provides a review of smart technologies including sensors, crowdsourcing and citizen science, actuators, data transmission, Internet of Things, big data analytics, data visualization, and blockchain, which can be used for infrastructure management. Smart infrastructure programs are reviewed to explore how enabling technologies have been applied across civil engineering domains, including transportation systems, water systems, air quality, energy infrastructure, solid waste management, construction engineering and management, structures, and geotechnical systems. Gaps in the application of smart technologies for infrastructure systems are identified, and we highlight how the civil engineering profession can adopt new roles toward the development of smart cities applications. These roles are: (1) master designer: civil engineers can identify ready applications of enabling technologies to improve the delivery of urban resources and services; (2) steward: civil engineers must account for both the environmental and societal impacts of smart infrastructure applications; (3) innovator and integrator: civil engineers should integrate across diverse sectors and groups of experts to develop smart infrastructure programs; (4) manager of risk: civil engineers should manage existing and growing risks of natural disasters, emergencies, and climate change; they should also manage new vulnerabilities in the privacy and security of individuals and households that are introduced through smart technologies; and (5) leader and decision maker: civil engineers can take a lead in smart infrastructure discussions and policy development.

The suitability of the project delivery system (PDS) selected for a project greatly influences the efficiency of project implementation. In Iran, traditional design-bid-build (DBB) is commonly used for highway construction projects; however, DBB leads to problems such as cost overruns, delays, and poor quality. This study aims to compare the performance of DBB with that of alternative contracting methods (ACM), including design-build (DB) and construction manager (CM) methods, for highway projects in Iran. A survey was conducted to determine the most important decision-making criteria for selecting the appropriate PDS to use in highway projects. The survey, conducted by postal questionnaire, aimed to empirically validate the reliability of the model. In the survey, all the conditions for imposing the effective criteria were considered, and ultimately a model using the analytical hierarchy process (AHP) was developed to rank the PDSs for highway construction projects in Iran. The results showed that DB is most often the optimal method. The research can be used to prepare a model that can help project owners make sound decisions about the best PDS.

This study examined the efficacy of anticorruption measures for extirpating the prevalence of corrupt practices in infrastructure procurement in developing countries. The study employed the fuzzy synthetic evaluation (FSE) technique to assess 62 responses from practitioners involved in the procurement and delivery of infrastructure projects. The probing measures were identified to be the highest-ranked construct, followed by the managerial measures. The remaining constructs with their variables were revealed to be moderately effective. The study has revealed the level of effectiveness of existing anticorruption measures that are widely applied to check, thwart, and extirpate corrupt practices in the procurement and delivery of infrastructure projects. This study theoretically contributes to the body of knowledge on corruption in infrastructure procurement by evaluating the effectiveness of anticorruption measures. Practically, this study provides useful information for project parties, policy makers, anticorruption institutions, and researchers toward the reinforcement of the existing measures for the expurgation of corrupt practices in infrastructure projects’ procurement, delivery, and management.

Determining the risk related to transportation networks due to the occurrence of (natural) hazard events often requires computer support. A simulation-based modeling environment can be useful when modeling a set of related events that lead up to the estimation of the probable consequences of hazard events, which affect network managers and society. Nonetheless, running such simulations can be computationally expensive because each type of event requires a model of its own, and proper interfaces are needed to link events. Therefore, only a limited number of simulations can often be conducted, with the expectation that their results are representative of those that could have been obtained if all simulations had been run. This article presents a simulation reduction technique to calculate the risk related to transportation networks due to extreme hydrometeorological hazard events by conducting statistical analysis on the risk estimated when simulating the impact of nonextreme events. The technique may be of interest to network managers seeking to make decisions based on possible future climate scenarios. An example road network in Switzerland is used to illustrate the technique.

Vulnerability assessment of interdependent infrastructure networks (IINs) must be conducted according to a specific situation such as the failure model, the scale of networks, interdependent relationships, infrastructure characteristics, and so on. This paper discusses the development of an interdependent network model for vulnerability assessment by combining the characteristics of community IINs and the design of the maximum flow comprehensive index (MFCI) to describe the performance of interdependent networks based on network flow theory. This study is based on the perspective of an infrastructure manager, and identifies the key nodes of interdependent networks by using the optimal attack strategy when dealing with potential external attacks (i.e., terrorism). It is assumed that attackers are sophisticated and will always choose the optimal attack strategy to maximize vulnerability and minimize the performance of interdependent networks. Because it is difficult to identify key nodes in interdependent networks, we proposed a probabilistic solution discovery algorithm (PSDA) that can accurately calculate the final failure state of interdependent networks regarding the interdependent relationships among networks and relationships inside a network. A case study was conducted to validate the proposed method: using MFCI and PSDA. Moreover, the assessment result was analyzed further with a view to improving infrastructure protection and identifying failure propagation paths. The main contributions of this paper are to propose a vulnerability assessment index system and construct a goal optimization with solving algorithm to provide decision support for the infrastructure managers.

Duration-based deterioration modeling approaches have been investigated over the past two decades with a view toward utilizing their prognostic capabilities in accurately identifying maintenance needs and improving asset management under constrained budgets. However, despite significant advances in asset management systems during this time, implementation of such approaches for probabilistic infrastructure deterioration modeling has been limited, resulting in underutilization of their predictive potential. In this paper, a comprehensive framework based on a combination of the Cox proportional hazards method and Markovian theory is presented to develop multivariable deterioration models that probabilistically incorporate the effects of explanatory factors on deterioration over the complete life cycle of a bridge component. Both stationary (time-independent) and nonstationary (time-dependent) transition probability approaches are introduced and compared. Sample results from implementation of this framework on North Carolina’s statewide bridge inspection database containing 35 years of data for more than 17,000 bridges are discussed. The predictive fidelity of the developed models is analyzed relative to the actually recorded condition ratings to demonstrate the effectiveness of these models in accurately forecasting deterioration.

Structural deterioration of buried pipeline due to adverse corrosion effect is among the leading causes of increasing possibility of pipe failure. As a result, maintenance intervention becomes a fundamental task for good engineering management program. However, in recent years, different probabilistic techniques have been used for the evaluation and estimation of cost-effective maintenance strategies of buried pipe based on performance indicators such as failure probability. Whereas the probabilistic approach is used to account for the variabilities associated with the structural parameters, the nonprobabilistic method has been utilized to deal with the limitations of the probabilistic approach, especially for cases in which the data for proper probability evaluation of the design parameters are not adequate. In this study, a new maintenance technique is developed to determine the optimal time for the maintenance of buried pipeline using a combination of fuzzy and subset simulation (probabilistic and nonprobabilistic) approaches for computing pipe reliability and risk, based on $\alpha$-level cut. The strategy aims at assessing the cost-efficiency required for the determination of the optimal time for maintenance using multiobjective optimization based on the fuzzy-subset annual reliability, risk, and total maintenance cost. The times for essential maintenance schedules are obtained based on a performance indicator (annual reliability or risk), and the optimization is performed using a genetic algorithm. The applicability is demonstrated with a numerical example, and the method provides engineering technicians with the needed tools for the determination of the optimal time interval required to carry out maintenance of the buried pipelines.

This paper presents a framework that integrates network theory methods into infrastructure network assessment in order to investigate transportation network robustness from a topological perspective. The objectives of this paper are threefold: (1) develop a framework that can effectively measure network performance under different stress levels (hazard scales); (2) determine the constraints of infrastructure networks’ spatially embedded nature in network robustness assessment; and (3) characterize the percolation transition in infrastructure networks and systematically evaluate cities’ robustness, and in doing so provide an evidence-driven evaluation framework for urban resilience planning. In this research, 13 city and 3 state transportation networks were investigated through both the proposed simulation and an analytical framework. The results show that (1) certain theoretical methods, such as generating functions, do not apply to transportation network analysis due to their unique spatially embedded features; (2) different city and state transportation networks’ degree distribution and percolation dynamics display similar patterns; (3) critical percolation threshold identifies the robustness feature in extreme cases and provides an early warning for urban traffic disruption, and the robustness index accurately measures the network robustness from a holistic point of view; and (4) reducing network spatial complexity by decreasing the node assortativity will make the empirical simulation comply with the theoretical results, which unveils the source of spatial essence inherent in infrastructure networks. Through the validation of different transportation networks, the proposed robustness assessment framework is verified to be able to extend the robustness analysis to a general network.

Past studies developed pavement performance models using data from all or multiple states across the United States. This study hypothesized that due to variation in agency practices and work activity profiles, individual pavement performance models should be estimated for each state, using data from its own roadway network, for use in its pavement management system. To test this hypothesis, this study used condition data of Interstate flexible pavements from eight Midwestern states to estimate three models: fixed-parameters regression, random-parameters regression, and artificial neural networks (ANNs). The ANNs model was found to statistically outperform the regression counterparts when estimating pavement roughness across all states. In contrast, the random-parameters model was statistically superior to the ANNs model in some cases when exploring the performance of these models for individual states. The statistical performance of models did not have a consistent trend across all states. Therefore, the application of models, based on data from multiple jurisdictions, could lead to erroneous/nonoptimal maintenance and rehabilitation decisions. Highway agencies are recommended to rely on their own jurisdictional data when developing their pavement performance models.

A large portion of public–private partnership (PPP) infrastructure projects in China have ceased without delivery, which has drawn attention to the need for effective risk management. Previous studies directly and/or independently correlated risks with consequences. This does not allow for an effective investigation of causal chain and propagation effects among risks. To address this research gap, this paper developed a risk network model to analyze risk interactions and their propagation mechanism in PPP infrastructure projects. A case study of a PPP water supply project in China highlighted the key risks and mitigation actions. The study showed that policy change is frequently a source of risk, government default is profoundly affected by other risks, and safety accidents are one of the critical risks leading to project failure, which previous studies have overlooked. Furthermore, PPP stakeholders should focus on decoupling the following risks to reduce the probability of PPP project failure: safety accidents caused by lack of managing/organizational ability, and safety accidents caused by insufficient/delayed financing. The results indicated that the risk network model can be applied in PPP risk analysis to identify unnoticed but pivotal risks with propagation effects. This paper contributes to the research on PPP project risk management in three ways. First, it encourages both theorists and practitioners to consider PPP risk management as a dynamic, nonlinear process. Second, it illuminates critical risks and propagation mechanisms that have been overlooked by previous studies, thereby supplementing the body of knowledge on PPP project governance/management. Additionally, it proposes effective mitigation actions within risk management practice, thus laying the foundation for management and governance strategies.

The dynamics and uncertainties of environmental, physical, and social demands (e.g., climate change, technological evolution, population growth, and market changes) have a significant effect on the performance of infrastructure systems. Dynamic external conditions can affect the performance of infrastructure systems because traditional designs are thought for long-term demands and assume stationarity in future behavior. This results in rigid and permanent features that may not perform optimally under variable conditions. Specifically, the system may be rendered inefficient from an operational point of view, might be more exposed to risks, and may even stop serving its purpose to society altogether. This situation can be reduced by implementing alternative design and management strategies that facilitate changes in the system as unplanned scenarios arise. One novel strategy is to incorporate the possibility of change by making systems more flexible from the outset. While some benefits of introducing flexibility have been identified and discussed in the literature, the concept is still far from being completely understood. The objective of this work is to address this knowledge gap by presenting an analysis of the concept of flexibility. The analysis includes a literature review concerning the most relevant aspects of flexibility: quantification, valuation, and management. Additionally, the adaptation process is deconstructed to identify the core components that must be present in any flexibility analysis. From this analysis, the main system attributes affected by flexibility are made explicit to formulate a novel flexibility metric. Finally, three numerical examples are presented and discussed to illustrate the application of the metric and highlight the relationship between flexibility and the value added to the system.

Establishing performance targets for marine maintenance and repair operations can be challenging for management due to the multitude of factors that can potentially influence productivity, efficiency, and manpower requirements. The aim of this study was to measure and evaluate the efficiencies of various maintenance and repair shipyard operations using data envelopment analysis (DEA). The use of DEA allows for multiple factors affecting maintenance productivity to be analyzed and allows the sources of inefficiencies in maintenance operations to be identified through examination of efficient or best practice decision making units, or DMUs. Results presented in this study were used to develop an overall strategic plan for enhanced decision-making with regard to labor and resource requirements and management strategies for the administration of the shipyards. Inefficient maintenance operations were identified through DEA evaluation and included the use of identified quantitative and qualitative factors. The use of quantified factors in assessing manpower needs was a beneficial approach for what is typically a subjective judgment in the determination of staffing and scheduling needs, organizational structure, and performance targets.

In gravel roads management systems (GRMS), the need for a holistic approach for detecting the dust amounts on gravel roads has enabled the development of a solution that works based on one of the subdisciplines of artificial intelligence (AI). Recently, machine learning is one of the most widely used algorithms to train data to optimize systems. The advances in machine learning has enabled us to develop a complex application. This paper demonstrates the ability of using one of the most popular machine learning frameworks TensorFlow to build an image classifier. This classifier has the ability to classify the dust amounts on gravel roads into four major levels (None, Low, Medium, and High). This classifier is based on the aspect of optimizing one of the deep neural networks models Inception-v3 model. This model contains a pretrained package used to extract and recognize dust patterns from dust images automatically. In this paper, a data set of 4,000 images of gravel roads were collected. For training, 80% of the data set was used, and 20% was used for testing. Furthermore, a prediction accuracy plot was generated, and it was found that this classifier achieves a prediction accuracy of 72%.

The vulnerability of critical infrastructure systems and their interdependencies have long-standing implications for community recovery and resilience. Extant research on critical infrastructure interdependencies has focused primarily on the infrastructure systems themselves and has paid less attention to the impact of infrastructure breakdowns on critical services in disasters. This study aims at analyzing the impact of infrastructure interdependencies on the emergency services sector (ESS) and critical support functions using an expert opinion survey of emergency managers, city planners, utility providers, and other stakeholders from two study regions: South Florida and parts of New York (NY), New Jersey (NJ), and Eastern Pennsylvania (PA). The majority of the survey respondents identified the loss of power as the most important factor affecting service delivery in their respective sector. Over 70% reported that loss of communications considerably impacted computer-aided dispatch (CAD) capabilities for the coordination of emergency services, dispatch of ambulances, and other medically equipped vehicles, as well as responders’ ability to continue their work. A comparison of individual service categories by type of infrastructure disruption and region revealed that there was a similar level of agreement among the respondents regarding the importance of backup power. There is a statistically significant difference in stakeholder responses regarding lack of adequate fuel supply with the stakeholders in the NY-NJ-PA study region more concerned about fuel availability than their counterparts in South Florida. The use of log-linear models to analyze key interdependencies between the ESS and critical infrastructures reveals conditional dependencies with statistically significant associations between critical infrastructure services and coordination of emergency response, access to health services, sheltering, and postdisaster evacuation. The findings from this research have important policy implications for prioritizing infrastructure restoration and improving disaster recovery outcomes.

Accurate prediction models for road structure deterioration increase the cost-effectiveness of road construction and the scheduling rehabilitation and maintenance of road structures. In this paper, a method to detect the minimum maintenance operation detection (MMOD) threshold and network-level pavement rutting prediction model are described. The MMOD threshold has the potential to filter network-level pavement rutting measurement data and improve prediction models. The model is a multilevel statistical time series model for rutting prediction without the need for measurement history. The model parameters used are pavement type and average daily traffic. The road maintenance planner estimates the need for a minimum sampling rate for future pavement performance measurements and predicts the pavement rut behavior. For asphalt concrete and soft asphalt concrete, the model gives realistic predictions for the first 10 years. For stone mastic asphalt, the realistic prediction window is the first six years.

Increasing temperatures due to global climate change and the urban heat island effect pose a rising threat to the reliability and security of energy systems. To support climate preparedness and resilience planning for the electric sector, this study (1) proposes a framework for assessing the vulnerability of electricity systems in the remainder of this century using the Washington metro area as a case study, (2) evaluates the cost-effectiveness of deploying rooftop photovoltaic (PV) technology, and (3) employs quantitative probabilistic risk analysis in order to enhance adaptation planning and decision making. The results indicate that by the end of the century, the probability of power outages could increase 70-fold in the Washington metro area under the business-as-usual scenario. If the peak load on the electrical grid is cut by three quarters, the probability of outages could rise 20-fold. This reduction is achievable by installing solar panels on building roofs. There is about $136.45{\mathrm{m}}^2$ of suitable roof area for solar PV in the Washington metro area, which could add a generation capacity of 13.02 GW to the existing power system. The benefits of using rooftop PV to reduce the peak load on the power grid by 25%, 50%, and 75% are significantly greater than the costs when environmental impacts are considered. The cost-effectiveness of PV strategies may increase in the future because of declining material and installation prices and because of increased social cost of carbon emission.

Worldwide, numerous economies have been subjected to the unnecessary economic and social impacts that materialize from the collusion that has taken place when public infrastructure has been procured. In an attempt to mitigate this collusion, the Brazilian government introduced laws (e.g., Law 8666/93) that require the procurement of public goods and services to be subjected to capped first-price auctions. For public infrastructure projects, bids are limited by median market prices that are recorded in specific databases and relied on during the selection of bidders. We examine 187 capped first-price auctions with eight or more bids. We reveal that full collusion can be detected if the collective behavior of any auction’s bidders is compared to a “but for” scenario that expresses the expected behavior of a group of honest competitors bidding randomly. This comparison makes it possible to flag, using different confidence levels, whether participants in a particular auction fully colluded at the time the bids are known. The contribution of our approach is twofold. It can be used independently or in conjunction with other existing methods to detect the presence of collusive behavior in a capped first-price auction.

Governments have used public–private partnership (PPP) units to ensure that PPP programs and projects are successfully delivered. These units frequently are understaffed and vulnerable to political and economic pressure to deliver. Few researchers have examined the organization, size, and transparency of these units. This paper undertakes a comparative analysis of 27 Brazilian federative units. The research has been focused more on identifying governance characteristics, and this study includes indicators of success, such as project implemented and transparency level. The research concluded the following: (1) Brazilian PPP units still have to significantly improve PPP public transparency; (2) a reduced number of PPP projects is actually implemented, but the more transparent PPP units are those with the ability to successfully implement more projects; and (3) size matters in terms of staff—the larger the units are, the more projects are implemented and the better the conversion ratio (projects appraised versus projects in operation).

Since the 2000s, the city of Santiago, Chile has experienced severe congestion problems in its urban area. To face them, the government decided to promote the first full network of electronic toll highway concessions ever implemented in a metropolitan area worldwide. The toll system was defined fully free-flow and interoperable across all concessions, requiring a complex enforcement mechanism to make it effective. Although previous research studied specific issues of the highway program, an overall analysis of its performance over time was missing. Using a case study methodology, information was gathered from literature review studies, statistical sources, and interviews with key stakeholders. This paper found that the concession model is a feasible alternative to finance and operate a network of urban toll highways using an interoperable electronic toll collection (ETC) system. However, future experiences should take into account crucial aspects such as the need to promote integration with public transportation means, to avoid separation among neighborhoods, and to adopt regulatory measures to prevent congestion in the future.