Journal of Infrastructure Systems

This study attempts to untangle the association and causation between pavement potholes and vehicle flat tire frequency. To this end, the study collected pothole data from crowdsourced user-generated reports and extracted flat tire incidents from official operation notes. To account for spatiotemporal variations in potholes and flat tire incidents, a geographically and temporally weighted negative binomial regression (GTWNBR) model was created to explore their spatiotemporal association, alongside a negative binomial fixed-effects regression model (NBFER) for causal inference of flat tire incidents. The GTWNBR model revealed that potholes are positively correlated with flat tire frequency, with a significant spatiotemporal variation. The potholes in the central city and interchanges of highways were found to have a higher association with flat tire frequency than in other areas. Besides, the flat tire frequency was most strongly correlated with potholes that outbreak during the winter months. Further, the NBFER model substantiated that increments in potholes can significantly cause more flat tire incidents. The NBFER and GTWNBR models differ in their strengths: the NBFER model is better suited for making causal inferences, whereas the GTWNBR model can provide a more comprehensive understanding of covariates and achieve higher predictive performance. The study findings recognize tangible connections of how pavement conditions trigger flat tire incidents, and they can pave the way toward prioritizing infrastructural investment decisions related to roadway maintenance.

Opportunistic behaviors are common in public–private partnership (PPP) projects. The contractor involvement in a whole project lifecycle is essential to ensure successful project planning, delivery, and operation, namely, through whole-process engineering consulting. Grounded with prospect theory, this study developed a tripartite evolutionary model among government, social capital, and whole-process engineering consulting enterprise for their opportunistic behavior in the delivery process of PPP projects. The model analyzed the risk attitude and loss avoidance of game subjects for strategic decision-making. MATLAB simulation was employed to validate the model. The results show that the risk attitude coefficient and loss aversion coefficient significantly affect the strategy choices of game subjects including reputation loss. The stability of the PPP project regulation system depends on several factors, including the severity of government punishment, the cost of rent-seeking, and the frequency of regulatory failure. The findings contribute to the current literature of risk assessment in PPP projects as well as the development and practice of whole-process engineering consulting in PPP projects.

Abstract

Globally, the current stock of infrastructure is not sufficient to meet the rising demand for infrastructure services. Billions of people around the world lack access to essential infrastructure services, especially in developing countries. Infrastructure project development is an inherently complex endeavor. The ability of governments to prepare and deliver quality infrastructure projects that meet societal need hinges on the strength of their infrastructure enabling environments: the actors, resources, and conditions that facilitate the development of quality infrastructure projects. Governments in developing countries often are burdened by weak enabling environments, but it is not clear how this can be remedied to improve project development. The complexity of project development necessitates a systems perspective. This paper advocates for a systems approach to address project development challenges and strengthen the enabling environment. A systems analysis was conducted to map the infrastructure enabling environment, identify key relationships and causal loops within this system, and uncover leverage points to improve project development. A systems stakeholder map and causal loop diagram were employed for this investigation, using the INABLE Framework for the infrastructure enabling environment as a reference. The systems stakeholder map highlights the central role of dedicated government institutions, the need for trust between stakeholders, and the role of power dynamics within these relationships. Similarly, the causal loop diagram demonstrates the limits of debt financing, the potential of quick-win projects and success stories to drive investor confidence, the trade-offs between affordability and bankability, and the power of data and information to drive decision-making. Using a case study application in Saint Lucia, the systems analysis is grounded in practical examples of system dynamics within the enabling environment. The case study highlights the role of legislation and institutional structures, and the impact of external technical assistance from development financiers and civil society in supporting the enabling environment.

Practical Applications

Sustainable development is rooted in meeting present needs without compromising the ability to do so in the future. It is rooted in preserving the natural systems that enable human life, while balancing the needs of society and economy, and ensuring quality of life for all. Infrastructure is essential to achieve sustainable development. It is foundational to human life. However, the infrastructure currently in place is not sufficient to meet the needs of growing populations. This paper investigated the systems that enable infrastructure development in developing countries to understand the challenges that prevent infrastructure from being built and to uncover potential solutions to address these challenges. The primary audience of this research is developing-country government agencies that are responsible for infrastructure development, and the organizations that support them. In North America, these are engineering consulting firms, federal government agencies, international organizations, and not-for-profit organizations that support infrastructure project development in developing countries. This work seeks solutions to the challenges these organizations and practitioners face in developing infrastructure projects. The paper presents a novel framework which provides common language that can be used to understand the enabling environment for infrastructure, a term which is not well defined or understood in this field. By applying the framework to a systems analysis and case study in Saint Lucia, the paper produced several findings, insights, and recommendations for government and development organizations to strengthen infrastructure development in their countries.

Interdependent infrastructure systems play a critical role in the normal operation and postdisaster emergency response in modern cities. However, they are vulnerable to earthquakes throughout their service life, resulting in substantial economic losses and public safety concerns. The threat posed by earthquakes underscores the necessity of implementing risk mitigation strategies. In this paper, an emergency resilience-driven decision model is proposed to address the optimization problem of seismic retrofitting of infrastructure systems. Two types of interdependencies are introduced to reflect the interaction within the infrastructure systems. Then, a resilience metric based on weighted emergency connectivity efficiency is defined to evaluate the ability of a community to provide emergency services under earthquake risks. In addition, a mathematical model and its solution algorithm are developed to maximize emergency resilience benefits while minimizing retrofitting costs, considering resource and probabilistic performance constraints during the emergency response phase. To demonstrate the applicability of the decision model, a case study is conducted on the Centerville community under seismic hazard. The results demonstrate the effectiveness of the decision model in evaluating community emergency resilience and identifying retrofit strategies that align with both resilience benefits and cost optimization criteria. Additionally, the sensitivity analysis results indicate that the intensity of interdependency may significantly affect the optimal seismic retrofit strategies and associated resilience benefits.

The process of restoring water supply after service is interrupted is critical for determining the durations and spatial distribution of outages and thus the impacts that households, businesses, and others ultimately experience. Nevertheless, the restoration period is difficult to predict because it involves complex, dynamic interactions among the system hydraulics, operator restoration actions, and consumer adaptations to service interruptions. In this paper, we introduce a new computer model called Restoration of Water after an Event Tool (REWET) that (1) allows detailed representations of both the hydraulic operations of the system and the restoration process, (2) is flexible enough to apply to any system or disruptive event, enable varying levels of complexity, and allow deterministic or probabilistic analysis, and (3) is available as free, easy-to-use, open-source code. It uses pressure-demand driven hydraulic analysis and allows detailed discrete event simulation representation of the restoration process. We present case study applications of REWET for the Los Angeles water system and for a small, simple network to illustrate the tool’s functionality, flexibility, and key features.

Top-down maintenance, rehabilitation, and reconstruction (MR&R) policies typically are modeled using Markov decision processes (MDPs). Within this framework, the total agency costs are assumed to increase linearly with the number of MR&R activities undertaken. However, there is empirical evidence to suggest that economies of scale (EoS) are present when agencies implement MR&R actions. EoS in infrastructure management refer to marginal savings in unit costs gained by increasing the scale of similar activities. This paper introduces EoS within a well-established system-level, top-down MR&R optimization framework for pavement management. In particular, given the concave nature of the proposed EoS-based objective function, two solution frameworks were implemented: a piecewise linear (PWL) approximation technique, and a branch-and-bound–based sigmoidal programming algorithm. Using a synthetic case study, the solution quality and computational efficiencies of the EoS-based problem formulations were compared with those of the fixed-unit-cost model. The resulting changes to the state–action distributions induced by the economies of scale are highlighted and discussed.

The transportation hub complex (THC) is an urban landmark building integrating the functions of transportation, commerce, office, and residence. Due to its convenient transportation and diverse service functions, the THC has a certain agglomeration effect in its surrounding areas. Therefore, earthquake damage to the THC will not only affect its own functionality but also impact the connectivity of the regional transportation system, leading to socioeconomic losses in the agglomeration area. To assess the comprehensive impact of THC under earthquake effects, this article considers three aspects: transportation, society, and economy. In terms of the transportation aspect, the impact of the loss of hub function in the THC on the regional transportation system is assessed from three aspects: resource integration capability, radiation scale, and connection efficiency. Subsequently, the agglomeration area generated by the THC is determined based on the kernel density analysis method. The social impact of the THC on its agglomeration area involves three aspects: the reduction of population flow, number of closed shops, and loss of infrastructure resources. The economic impact is measured as the sum of direct and indirect economic losses. Finally, a case study is conducted on a city’s THC. The research results indicate that the discontinuation of a subway station in the THC has a greater impact on transportation, society, and the economy. Furthermore, compared with the social service function of the THC, ensuring the hub function after an earthquake can more effectively reduce indirect economic losses. This study can provide a basis for improving the seismic resilience of the THC from the perspectives of regional transportation and socioeconomy.

Road travel cost can be defined as a function of condition and volume-capacity factors. Asset managers intervene on heavily trafficked and poor condition roads based on criteria to optimize network travel and intervention (social) costs. These criteria may involve a trade-off between improving the road condition or capacity. Road performance is known through periodic inspection and stochastic modeling to estimate a deteriorated future condition. The predicted future condition and traffic growth rates change pavement section intervention (capacity or condition improvement) priority over time. The optimal road intervention choice can be determined using algorithms, including the greedy algorithm and Monte Carlo simulations. Greedy algorithms search through the entire sample space locally and stepwise to approximate global optima, whereas Monte Carlo simulations randomly sample candidate sections to generate more globally optimum interventions. This study proposes a road asset management model using Monte Carlo methods to optimally choose road network interventions considering condition and traffic changes over a planning horizon. The study includes an empirical application using real world data and compares the proposed Monte Carlo simulations approach to the greedy algorithm.

As climate change persists, the frequency and intensity of natural disasters has increased, emphasizing the need for resilient critical infrastructure. This study analyzes sustainability impacts of resilient and recovery infrastructure for water resource recovery facilities (WRRFs) affected by flooding, considering potential operational and structural failures. A detailed inventory was collected for 10 reflective case studies to analyze life cycle environmental impacts of initial construction, recovery construction following a natural hazard, and resilience infrastructure that can help prepare for future natural hazards. Economic and additional impacts of flooding were evaluated using generalized WRRF cost models and anecdotes from relevant stakeholders, respectively. Generally, initial construction has a higher environmental and economic impact compared to recovery and resilience construction. Recovery construction tends to have higher environmental and economic impacts compared to resilience construction when major equipment replacements and repairs are necessary. Conversely, resilience construction tends to have higher environmental and economic impacts compared to recovery infrastructure when major construction activities and resources are required. If multiple flooding events occur during a facility’s design life, recovery construction impacts increase based on the number of flood events and can become larger compared to initial and resilience construction impacts, highlighting the uncertainty associated with recovery needs compared to the stability of resilience. Additional impacts of flood recovery were identified and discussed, including treatment, workers, community life, and facility impacts, which were often driving factors considered by decision-makers if resilient infrastructure was or will be implemented. This study aims to provide justification for communities, engineers, and funding agencies as they seek to protect critical wastewater infrastructure via resilient investments.

Rapid reliability assessment of transportation networks can enhance preparedness, risk mitigation, and response management procedures related to these systems. Network reliability analysis commonly considers network-level performance and does not consider the more detailed node-level responses due to computational cost. In this paper, we propose a rapid seismic reliability assessment approach for bridge networks based on graph neural networks, where node-level connectivities, between points of interest and other nodes, are evaluated under probabilistic seismic scenarios. Via numerical experiments on transportation systems in California, we demonstrate the accuracy, computational efficiency, and robustness of the proposed approach compared to the Monte Carlo approach.

Public sector infrastructure construction has been adversely impacted by recent shocks and stresses associated with global change, conflicts, and supply chain breakdowns. To address these challenges, this article presents novel methodologies to quantify past project delays and anticipate potential future disruptions from global shocks. These methodologies are then applied to over 2,000 projects executed by the US Army Corps of Engineers between 2011 and 2021. The results show that cost and time overruns in 2021 are double the baseline amount observed in the last 10 years. In some districts, a greater than 50% reduction in the number of contractors is also observed, despite an increased volume of work. Further, this article finds that many projects significantly rely on Russian-sourced minerals, suggesting that these challenges will likely accelerate as commodity scarcities from the Russia-Ukraine War unfold. This early-stage intelligence could significantly improve supply chain resilience in an increasingly unstable world.

In broad terms, a digital twin is a continuously updated virtual representation of some targeted physical reality. The objective of this technical note is to contribute to the current/ongoing efforts of delineating the digital twin concept within the civil engineering domain. An adult digital twin concept is described and exemplified for this purpose–applicable to mature civil infrastructure and with the primary aim of monitoring changes in state over time. The analysis advocates for clearly separating the real and virtual spaces by focusing on objective physical entities as state descriptors, i.e., entities that are not inherently coupled to any specific modeling framework. Also, the analysis emphasizes that model updating consists of two distinct activities, namely parameter updating (Type I) and formulation updating (Type II)—with the latter type requiring research attention.