Journal of Infrastructure Systems

Public–private partnerships (PPP) are considered to be one of the main models for the provision and management of public infrastructure all over the world aiming to meet the social needs of many countries. Several attempts have been made to thoroughly analyze the literature in this field to identify the existing trends in relevant research. However, they all failed to consider research based on different parameters that allows covering a wider spectrum of literature mapping. This paper provides a detailed literature map based on different attributes that have to be considered in PPP research and sets a strong foundation to clarify the main directions and authors according to a certain specialization in this sphere. The results highlight the main tendencies in the infrastructure management literature according to certain parameters which can be considered as a point of departure for the identification of new research topics in the future.

This paper studies urbanization in China using composite indices of urban infrastructure. It has two objectives. First, it computes a multidimensional composite index of urban infrastructure for ranking 31 provinces and six regions in China by their level of urbanization and infrastructure development during the period 2005–2014. The infrastructure index is composed of 15 components: consumption, culture, economic, education, employment, environment, finance, human development, health, housing, social security, social services, technology, transport, and utilities. Second, the paper estimates the effects of the aggregate urban infrastructure index and its underlying components on urbanization levels. Our empirical results suggest that provincial and regional disparities are significant and allocations for urban infrastructure are not balanced between the different provinces and regions. Guangdong and Tibet have the highest and lowest values of urban infrastructure respectively while the Eastern and Southeastern regions have the highest and lowest urbanized populations, respectively. One policy implication of these results is that each province should implement a different urbanization plan based on its own characteristics and the resources available. The central government should improve the allocation of resources and the location of key industries between poor and rich provinces. Our estimation results indicate that the economics, employment, human development, health, housing, security, utilities, and technology components of urban infrastructure had positive and significant effects on China’s urbanization. We suggest that the government should guide investments to more efficient transportation systems that include all age cohorts. After discussing the findings and how they are reached, this paper concludes by providing policy recommendations for city planners and policymakers.

Pitting corrosion of buried cast iron water pipelines causes damage that cannot be easily detected without advanced monitoring tools. Structural reliability analysis can be used to account for the inherent environmental and mechanical uncertainties of these infrastructures and accurate prediction of their life-cycle performance. In this study, the probabilistic performance of corrosion-affected water pipeline, in terms of the extent of damage, is forecasted using a reliability-based power law model. This performance indicator follows a lognormal process that in turn can be modeled as the underlying geometric Brownian motion with drift for the valuation of maximum expense limit and minimum revenue guarantee clauses of an operation & maintenance (O&M) contract. Binomial trees have been constructed to evaluate the corresponding American real options. This quantitative performance indicator can be used instead of condition state to determine subjectively and qualitatively the severity and extent of damage. Therefore, the merit of this novel integrated analysis is that the valuation of contractual flexibilities is based on accurate reliability-based analysis. It is hoped that the proposed methodology sheds new light onto the development of advanced asset management systems that enable O&M contractual parties a risk-informed negotiation of the contract terms.

Freight transportation infrastructure systems facilitate commodity flows across multiple industries. The closure of key infrastructures leads to an interruption of economic productivity that propagates through a system of interconnected industries. Investing in infrastructure and key industries can reduce the vulnerability of many industries by improving their ability to maintain functionality when shocked. This work investigates how a limited budget could be allocated to multiple industries to fortify them prior to a disruption to ultimately enhance the economic resilience across all industries by reducing the vulnerability of the underlying infrastructure. A risk-based economic interdependency model is used to implement a new measure of absorptive capacity to examine the propagation of a failure throughout the economy given the fortification of industry sectors. Sources of uncertainty in this data-driven model are considered, and a soft-robust optimization model is proposed to devise budget allocation under uncertainty. The approach is illustrated with an inland waterway port case study. The results can provide decision makers with managerial insights about how the economic interdependency affects the industries’ share of a budget to enhance absorptive capacity and how the level of budget affects the decision-making process for allocating resources.

This paper investigates the impacts of alternative structural concretes on the life-cycle sustainability and resilience of a reinforced concrete building in a high seismic region. To do so, we conduct a life-cycle environmental impact assessment, quantifying greenhouse gas emissions associated with building construction and seismic performance, accounting for potential earthquake damage and subsequent repairs. The unit of analysis is a modern code-designed reinforced concrete frame building, designed with seven different types of concrete: a conventional concrete mix, 3 concrete mixes in which varying quantities of fly ash replace cement, and 3 concrete mixes in which varying quantities of recycled concrete replace virgin coarse aggregate. The findings suggest that replacing cement with fly ash can be an effective tool to improve building sustainability over the entire life cycle, reducing greenhouse gas emissions during both construction and service life (seismic) life-cycle stages, without compromising seismic performance (i.e., resilience). However, replacing virgin coarse aggregate with recycled concrete aggregate can have unintended consequences in terms of worsening seismic performance and hence making the overall life-cycle sustainability calculus unfavorable in terms of greenhouse gas emissions. This paper is one of the first to link life-cycle seismic and environmental performance of buildings designed with alternative (and potentially green) concretes and to demonstrate the importance of considering tradeoffs between resilience and sustainability in design and selection of alternative structural materials. In addition, this study illustrates a framework that can be applied for life-cycle assessment and evaluation of structural concrete material choices.

Highly interconnected critical infrastructure systems (CISs) are the backbone of the well-being of the modern societies. It is crucial to account for the interdependencies among CISs in assessing the community resilience. This study presents a framework for resilience assessment of an interdependent traffic-electric power system subject to hurricanes. The traffic and electric power systems are both characterized using the flow-based mathematical representation, and three types of interdependency are captured. A modeling procedure coupled with the Monte Carlo simulation is presented to assess the resilience of both systems and to integrate different improvement strategies into corresponding stages of resilience. The traffic and electric power systems in Centerville, considering three improvement strategies, are studied to demonstrate the proposed framework. The results show that interdependencies can significantly affect the resilience of both systems, leading to different and the most effective strategies for each system. While hardening hurricane-vulnerable components and adopting a better repair schedule, both improve resilience of the electric power system significantly; adopting a better repair schedule works best for the resilience of the traffic system. The equal repair mode can achieve the resilience improvement in a balanced fashion. The results also highlight the importance of repair scheduling to the resilience improvement of these two interdependent systems when the available recovery resources are limited. Due to the interdependencies, the faster the functionality of traffic system can recover, the more efficiently the repair activities can be conducted, and thus, the greater resilience of both systems can be achieved.

Making communities safer requires better tools to identify, quantify, and manage risks. Among the most important tools are stress tests, originally designed to test the risk posed by nuclear power plants. A complementary harmonized multilevel stress test for nonnuclear civil infrastructure systems against natural hazards is proposed. Each stress test level is characterized by a different scope and a different level of risk analysis complexity to suit different civil infrastructure systems, different hazards, and different risks. The stress test consists of the following phases. First, the goals and the methods for the risk analysis are defined. The test is then performed at the component and system levels, followed by a verification of the findings. A penalty system is defined to adjust the output of the risk assessment according to the limitations of the risk analysis methods used. The adjusted risk assessment results are then passed to a grading system to determine the outcome of the stress test. Finally, the risk assessment results are reported, and the stress test outcomes are communicated to stakeholders and authorities.

Understanding the deterioration of roads is an important part of road asset management. In this study, the long-term pavement performance (LTPP) data and machine learning algorithms were used to predict the deterioration in the pavement condition index (PCI) over 2, 3, 5, and 6 years. In selecting the attributes for conducting the analysis, we targeted ones that are freely available. This approach can help smaller municipalities, which could be short on money or required expertise. For larger ones and transportation agencies, this can save the increasingly significant costs for collecting field data and any associated safety or traffic implications. In addition, we used this category of attributes to better examine the role of data analytics in asset management. Without considering a causal model, can trends in data help assess deterioration in the PCI? Several models using combinations of 15 attributes were learned and tested. The algorithms used in this study were two types of decision trees and their boosted models based on gradient boosted trees. The accuracy of the ensemble of boosted classifiers was considerably higher than their base learners, with some reaching over 80% in predicting unseen data. We also found that dividing data into different climatic zones can change the relative importance of attributes and the overall accuracy of the models. Increasing the prediction span reduces accuracy, while reducing the number of prediction classes (levels of deterioration) increases the accuracy. In addition to automating the calculation and prediction of PCI, this study presented informative or important attributes for prediction. Such analyses could help municipalities and departments of transportations with forming a more effective policy for data collection and management.

Transportation networks, as an essential ingredient of civil infrastructure, are subjected to various natural hazards over their service life. Failure of bridges may severely disrupt the serviceability of transportation networks, causing considerable economic and social losses. Recently, climate change has been found to be liable for the increasing bridge vulnerability due to intensification of extreme hydrologic events (e.g., floods, sea level rise, and hurricanes). With the limited resources for infrastructure management and the growing risk compounded by climate change, effective risk-informed management for bridge adaptation actions is imperative for the safety and serviceability of transportation networks. This paper formulates a network-level, risk-based framework that determines the optimal adaptation schedules for bridges in a transportation network considering climate change effects. Bridge scour, one of the most common failure modes for bridges under floods, is investigated. The proposed framework (1) evaluates the consequences of bridge failure at the transportation network level, (2) considers climate change effects related to bridge scour using hydrologic modeling under different climate change scenarios, and (3) investigates the effects of budget availability and risk perception on the decision-making process. The proposed framework is demonstrated using an existing highway bridge network located in the Lehigh River watershed in Pennsylvania.

This study developed a model to select the optimum sections of unpaved roads for chemical treatments as dust suppressants within a limited budget. In the state of Wyoming, more than 19,312 km (12,000 mi) of roads are unpaved. Because Wyoming is a leading producer of coal, oil, and natural gas, its unpaved roads have more traffic that results in significant dust emissions. To reduce these emissions, the state currently uses CaCl₂ and MgCl₂ as dust suppressants. Because its budget is limited, all 19,312 km (12,000 mi) of unpaved roads cannot be treated every year. A previous study concluded that traffic volumes, the percentage of fines passing through a #200 sieve, and annual rainfall make significant contributions to the generation of dust. Considering these factors, this study developed a model that identifies which unpaved roads should be treated given the limited budget. Because the resulting model is nonconvex, an evolutionary solving method was used to identify the best set of projects for chemical treatment. As a case study, the proposed model was implemented on a small network of 23 unpaved roadway segments.

Current leak detection practice in a water distribution system consists of monitoring the distributed volume in a district metering area (DMA) and the consumption measured with automated meter reading (AMR) at the building connections. The detection of the occurrence of a potential leak in a DMA is established through a systematic continuous comparison of the real-time distributed volume and the consumption for this DMA and/or, in the absence of AMR, the comparison of the monitored distributed volume and a reference curve based upon past monitoring records of the distributed volume under similar operational conditions. The purpose of this research was to develop, test, validate, and illustrate the application of the machine-learning–based risk assessment method for early detection of high likelihood leaks, their geolocation, and the detection accuracy assessment in the water distribution system of the SUNRISE demonstration site at the University of Lille, France. It illustrates that the proposed algorithm, integrated with a GIS-based spatial flow data analysis, efficiently supports early detection, likelihood severity assessment, and geolocation of leak sources.

A simulation of the resilience of lifeline systems in a test bed subjected to a series of seismic events is presented in this paper. The simulation framework is comprised of a group of independent simulators that interact through a publish–subscribe pattern for data management. The framework addresses the spatial and time-dependent interactions that arise between lifeline systems as a hazard and subsequent restoration processes unfold. The simulation results quantify how operability loss and recovery time may be underestimated if the interdependencies between lifeline systems are not properly taken into account. The effect of insufficient resources on recovery was investigated, and it was demonstrated that among the six resource allocation strategies studied, the time-varying strategies that are responsive to actual conditions on the ground had a better effect on resilience. This paper demonstrates the power of connecting simulators using the publish–subscribe method in order to account for multiscale interdependency and time-dependent effects on community resilience.

During the development of a suite of computer-aided decision support tools for the restoration of interdependent infrastructures impacted by an extreme natural hazard event, it became apparent that the release of vulnerability data on actual infrastructure systems could raise security concerns. As a result, an artificial and customizable infrastructure dataset was developed based on experiences with creating real, but not public, data that can be used for modeling, analysis, and decision making. The purpose of this paper is to describe the rationale and methodology for assembling the database for customizable artificial community (CLARC), an artificial coastal community of approximately 500,000 people and 1,065 square miles, that includes five civil infrastructure networks (electricity, water, wastewater, transportation, and communications) and social infrastructures, such as public safety, healthcare, and critical commercial services including fuel and banking that communities rely upon during an emergency as well as population and other socio-economic factors. The interdependencies that exist between the civil infrastructure systems and between civil and social infrastructures are included in the dataset and are attributes to assist with modeling and analysis. This paper discusses the dataset in detail, describes why and how it was assembled, and includes a virtual case study that uses the CLARC dataset to simulate the impacts on the civil and social infrastructure of a hypothetical Category 3 hurricane and the community’s response and recovery from that event. The dataset is openly available for use by the research and practitioner communities.

A leading factor in structural decline of highway bridges is the deterioration of concrete decks. Thus, a method to predict bridge deck performance is vital for transportation agencies to allocate future repair and rehabilitation funds. While service-life prediction tools are available, they rely on input parameters that are often difficult to obtain or estimate. This study estimates the relationships between concrete highway bridge deck performance and information readily available from the National Bridge Inventory (NBI), perhaps the single most comprehensive nationwide source of bridge information. As such, this paper takes full advantage of the NBI data using a scale of analysis exceeding that of previous studies. Using recent computational advances in Bayesian survival analysis, this paper models the factors affecting time-in-condition ratings (TICR)—defined as the time duration a bridge deck is assigned the same condition rating (CR) before it decreases—using over 150,000 bridge decks observed over 23 years. Because the dataset only spans 23 years of elapsed time and bridge deck deterioration takes place over years and sometimes decades, many observations of bridge deck CR only provide a censored view of how long a bridge deck may have been assigned a certain CR. Reasons for censorship include the following: (1) data is censored as its CR prior to 1992 is unknown; (2) data is censored as its rating after 2014 is unknown; (3) data is censored due to missing observations; and (4) data is censored due to an increase in CR from 1 year to the next, which is considered maintenance. Fortunately, the Bayesian approach provides a coherent method for handling censored observations while simultaneously providing meaningful estimates of parameter uncertainty. The results provide insight into the parameters driving concrete bridge deck deterioration and may help agencies with maintenance repair prioritization.

The increasing adoption of small-scale electricity generation technologies represents a transition from a hub-and-spoke system of centralized electricity generation to distributed energy resources (DER). Many utilities understand the benefits of DER but are unsure of how to mitigate the associated challenges, including intermittent generation, stranded assets, inability to dispatch, and an inability to control when generation is produced (“must take” energy). Using a bottom-up agent-based computational economics approach, this paper studies the evolving dynamic behaviors that influence the DER investment decisions facing the associated stakeholders. Building off the existing agent-based modeling of electricity systems (AMES) model and using a hypothetical case study in evolving actual data from Tennessee, the New England region, and some other reasonably assumed and verified parameters from the literature; the authors developed a proof-of-concept framework to account for the different system components associated with adoption of distributed solar generation (DSG). The interdependent steps included (1) incorporating different transmission and generation features from that of AMES, (2) allowing for customer electricity demand sensitivity to electricity rates, and (3) creating a new end-use customer agent type that allows for investigation of DSG investment behavior. Pursuant to a successful model testing processes including local sensitivity analysis, the model’s architectural design was able to represent the generic characteristics of the DSG domain problem and its potential future application to actual utility data. To this end, the authors present a benchmark simulation that was compared with two counterfactual scenario simulations. Although the results presented in this paper are illustrative and not prescriptive (i.e., the observations and discussions from the hypothetical case study are expected to—and should—change when applied to specific utility companies), they do still highlight a number of interesting trends. First, the declining cost of solar has not yet had a statistically significant impact on customer demand in six of the model’s largest utilities. Second, decreased load demand due to adoption of DSG technologies will have a large impact on high-cost generating units in the model but may have little impact on the lowest-cost generating units. Third, transmission line constraints matter. Despite the promising results, the model still needs more refinements to completely capture the feedback between customer adoption of DSG and utility investments in new generation resources and transmission assets. When complete, this research will result in a decision-support tool that will identify least-cost strategies that utility companies can use to respond to increasing penetration of DER.

During recent years, the rapid growth of shale gas exploration nationwide has resulted in a large number of overweight truck trips on roadways in areas with shale gas development. The Louisiana Department of Transportation and Development (DOTD) has seen a rapid decline in roadway serviceability within the Haynesville Shale area, presumably due to shale-gas-related traffic. The objective of this case study was to estimate roadway damage costs resulting from shale gas-related overweight trucks in Louisiana. In this study, the permit data for shale-gas-related overweight truck trips from 2006 to 2016 were extracted from DOTD’s oversize/overweight database using R language. The extracted overweight truck trips were subsequently assigned to the roadway network based on the shortest path method using ArcGIS. In total, it was found out that there were approximately 9.7 million shale-gas-related overweight vehicle miles traveled on Louisiana roadways between 2008 and 2016. This can be translated into a roadway damage cost of $17 million because of the shortening of roadway serviceable lives. On average, the estimated damage cost due to a single shale gas well was approximately $5,242. The damage costs per overweight mile and per equivalent single axis load were $1.74 and $0.27, respectively. However, it was estimated that only $14.5 million was recovered through permit fees. Therefore, it may be concluded that the current permit fee schedule for overweight truck trips in Louisiana cannot fully recover the damage costs resulting from overweight truck trips related to shale gas development. In general, this case study quantified the damage costs of one specific traffic type (i.e., shale-gas-related overweight truck traffic) on the roadway network using a combination of R Language, ArcGIS version 10.41, and pavement damage estimation analysis. The adopted method can also be applied to quantify the impact of other traffic types (e.g., that related to seasonal agricultural activities) on roadways or bridges. The results of the average damage costs estimated in this study may serve as a reference for future damage cost recovery due to shale gas exploration in other regions.

This paper describes a case study performed with the US passenger rail service provider Amtrak that developed a metrics framework to measure resilience to climate risk within their Northeast Corridor operations systems and to track changes in resilience over time. Amtrak representatives reviewed metrics and selected those most relevant and also most feasible to measure and implement at Amtrak. Twenty-one metrics, termed resilience activities, focused on technical and organizational (leadership and readiness) resilience; key outcomes to measure following a disruption were identified as cost, safety, customer satisfaction, organizational development, and on-time performance. Resilience activities were scored to serve as a baseline for assessing resilience in the future. While many researchers have generated frameworks or resilience metrics, few published examples of metrics implementation in infrastructure systems are available. Amtrak appears to be progressive in their use of resilience metrics and participated in this process to improve resilience awareness within their organization and identify areas for improvement. Analysis of baseline scoring of resilience activities indicates that Amtrak assesses itself as being relatively more resilient toward climate change risk from an organizational readiness capacity than from an organizational leadership capacity or a technical capacity. Future research may include the tracking of metrics following a disruption and a periodic reassessment of resilience activity scores.