Journal of Infrastructure Systems

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Infrastructure systems such as water, electricity, gas, and so on are interdependent complex sociotechnical systems. As such, understanding the human or social component of these systems is critical for increasing resilience. Qualitative analyses, including cognitive task analyses conducted with utility systems personnel, suggested that the human or social part of these systems can be modeled as a multiteam system (MTS). The interdependent critical infrastructure systems (ICIs) that we examined were found to have all of the essential features of multiteam systems. Using the National Academy of Sciences resilience framework (plan, absorb, recover, adapt), we applied findings from the MTS literature to generate recommendations, cautions, and suggestions for future research in infrastructure systems design and practice.

Abstract

Performance forecasts of highway bridge networks are informative inputs for stakeholders for cost-effective decision-making of funds allocation and asset management. Developing performance forecast models requires establishing a target performance measure (PM) and developing deterioration models accordingly. In the United States, the Federal Highway Administration (FHWA) and state departments of transportation use FHWA’s national PMs to assess highway bridge performance. Incorporating the national PMs for forecast modeling is the focus of this paper. Existing approaches commonly utilize stochastic processes for deterioration modeling, introducing uncertainty into forecasts and creating challenges for decision makers to capture the complete information. Numerical estimation through Monte Carlo simulations is achievable but inefficient. This paper presents an application of the generalized Poisson–binomial distribution as an efficient statistical tool with closed-form solutions to characterize uncertain network PMs based on stochastic forecasts of individual bridges within a network. The study demonstrated and validated the modeling tool with a case study on highway bridges in the United States using historical data (1993–2019). The results showed that this modeling tool achieved a robust and low prediction error. Further, the study demonstrated the feasibility to infer maintenance, repair, and rehabilitation (MR&R) effects when specific MR&R data were not available.

Practical Applications

This study demonstrated and validated an application of the generalized Poisson–binomial distribution for bridge network performance forecasts in terms of FHWA bridge performance measures. The study presented how to develop probabilistic forecasts of a network or a group of bridges. The primary advantage of this study is the theoretical formulations that characterize the network forecasts’ statistics. For practical applications, selections of bridges in a network can vary among multiple users. The theoretical formulation features near-real-time computations. For applications that are sensitive to computations, e.g., online analysis tools, the presented study can be a well-suited candidate. The study also showed the capability of modeling bridge MR&R effects in network performances through a calibration process. The capability can advocate potential applications for developing bridge asset management systems that consider bridge MR&R plaining. The third potential use of the study is to develop a fast or rough estimation of network forecasts with budget constraints. However, readers should refer to the “Limitation” section before starting their own applications.

Considering the magnitude, lifespan, and environmental impacts of physical infrastructures, integration of sustainability with development policies has proved to be indispensable; accordingly, several rating systems were nationally developed to enhance implementing sustainability into physical infrastructures. Lack of automation and strategic outlook of the conventional approach to infrastructures’ sustainability assessment, exacerbated by the lengthy and costly processes involved, highlights the necessity of adopting comprehensive and innovative measures. This paper principally aims at extending the scope of sustainability rating systems such as Envision by proposing a framework for large-scale and automated assessment of infrastructures. Based on the proposed framework, a single model was developed incorporating remote sensing and GIS techniques alongside the support vector machine (SVM) algorithm into the Envision rating system. The proposed model adds a certain degree of automation in assessment process regarding the criterion N.W.1.2 of Envision rating system (i.e., provide wetland and surface water buffers) as a starting point toward entire automation of the Envision system. Given the quantitative scale of the criterion N.W.1.2, our model automatically extracts (1) wetlands, (2) waterbodies, and (3) roadways through Optical Satellite_Sentinel-2A, Synthetic Aperture Radar (SAR) Satellite_ALOS-1 imagery, and shapefile from Florida Department of Transportation (FDOT). The image-based model then examines whether certain applicable specifications of Envision scoring system are met. The level of achievement is determined, and the final score in the criteria N.W.1.2 is calculated afterward. The results indicate that more than half of the existing road segments in the study area failed to obtain the minimum required score, regulated by Envision. This emphasizes the criticality of considering sustainability indicators in future infrastructure planning. In addition, the validated results confirm the feasibility of automation of other indicators of the Envision system that will help authorities see the bigger picture and make more sustainable decisions for future practices and policies.

Abstract

An effective infrastructure asset management (AM) system is crucial for utilities, city officers, government agencies, and other asset-owning organizations to facilitate navigating the numerous challenges associated with operating and managing infrastructure assets. In this paper, the AM system itself is represented as a complex network (comprised of nodes and links) that describes the major components necessary for its operation within an organization and the information connections between such components. ISO 55001, a widely accepted international standard, specifies the requirements for an effective AM system and outlines the criticality levels of different system components—reflected in the corresponding network by the link weights. The main challenges facing managing an AM system (i.e., metamanagement) pertain to (1) information asymmetry (i.e., not relying on consistent information for decision making) between AM system components; and (2) information overload (i.e., excessive information undermining decision making) within the AM system components. These challenges cause systemic risks (possibility of dependence-induced disruptions) within the system network due to the connectedness of system components. Systemic risks can be mitigated through built-in network resilience by restructuring the system component connections. Such network reconfiguration presents a complex nonconvex optimization problem with multiple potential solutions depending on the number of new connections to be added to the AM system, the length of those connections, and the target risk mitigation level. Through this metamanagement (managing the management system) lens, a genetic algorithm approach was employed to explore the optimal AM network configurations considering different objective functions. These objective functions were based on different complex network measures including the betweenness-, closeness-, and eigenvector-centrality, as well as the vulnerability index. The devised objective functions were employed to the cases of adding 1 to 15 links only to limit network overconnectedness (i.e., information overload). Considering all objective functions evaluated, adding a small number of links (fewer than five) provided a significant reduction in systemic risk (18% to 49%). Finally, managerial insights are presented to explain how to employ the developed approach to mitigate the systemic risks within an organization’s AM system based on different metrics valuations and stakeholder inputs.

Practical Applications

The effective implementation of infrastructure asset management systems within organizations that own, operate, and manage infrastructure assets is critical to address the pressing challenges facing the infrastructure decision makers (e.g., infrastructure ageing and deterioration, maintenance backlogs, strict regulatory operating conditions, limited financial resources, and losing valuable experience through retirements). Infrastructure asset management systems contain connectivity between major operational components that can lead to systemic risks (i.e., dependence-induced possible disruptions). Such systemic risks are typically caused by information asymmetry, when (some) asset management stakeholders operate in silos, and/or information overload, when asset management stakeholders are overwhelmed by too much/irrelevant information thus, hindering their decision-making effectiveness. The approach described in this paper empowers infrastructure asset managers with the necessary means to reduce their organizations’ systemic risk exposure, and subsequently enhance their organization resilience to information asymmetry and/or overload. This study is meant to be applied by organizations that align their asset management system with an accepted system that includes 39 separate asset management subject areas.

Municipal drainage systems play a key role in public health and are considered one of the main components of every modern city’s infrastructure. However, as a drainage system ages, its pipes gradually deteriorate at rates that vary based on the conditions of utilization. To prevent unexpected failures, municipalities have adopted a proactive approach that relies on regular condition assessments of their assets. At this juncture, a question that needs to be answered is how these assessment data can be used to optimize the frequency of inspections of the drainage pipes. In fact, when the assessment information is used in conjunction with deterioration models, city managers are able to develop data-driven maintenance, rehabilitation, and replacement plans based on the current condition of the assets and their risk of failure. As a result, understanding the rate at which defects evolve over time provides valuable information in terms of understanding the relationship between the various factors affecting defect development and pipe deterioration. This research presents an image registration framework for extracting crack development information from closed-circuit television (CCTV) videos of sewer pipes. Image processing techniques are used to estimate the relative change for a given defect from images taken at two different times. Because the parameters of cameras (e.g., model, location, angle of view) are generally not expected to be identical for consecutive inspection campaigns, the images to be compared were first scaled using a technique referred to as the area scaler (AS) to ensure all images have the same frame of reference. This scaling procedure is illustrated in a case study, containing 49 pairs of images that led to a relative error (with respect to the mean) generally not exceeding 5% when frames of the same defects contained a sufficient number of matching points.

Condition of drainage asset systems can have substantial impact on the structural and operational integrity of railway tracks. It is therefore important to ensure that the various components of the drainage system are well-maintained. To this end, decision makers in the railway industry have been moving toward predictive, risk-informed drainage asset management. The approach aims to optimize the allocation of the limited time and financial resources for maintenance works. To achieve this more research is required to develop predictive condition models for railway drainage assets. This paper describes the development of data-driven condition prediction models using drainage pipe asset records. The models were tested for both structural and service condition prediction. Nine input factors were considered in the prediction models. Significance of the factors was evaluated using connection weight analysis. Four machine learning (ML) algorithms, namely neural networks, decision trees, bagged trees, and $k$-nearest neighbor, were compared based on their condition prediction performance for pipe drainage assets. The models were developed and tested using field data collected from the UK owner of rail assets, Network Rail. The results demonstrated that bagged trees performed best on a balanced data set with 87% overall accuracy for structural condition prediction and 72% accuracy for service condition prediction. It was found that pipe length, previous condition, years since previous condition, and maintenance were the most significant factors in predicting condition.

Existing models have failed to capture the stochastic nature of input variables for the value-for-money (VfM) analysis in build–operate–transfer (BOT) infrastructure projects. The present study aims to address this knowledge gap by offering a VfM-at-risk analysis model and developing a stochastic-based model that takes account of the risk allocation scheme and cost efficiency in the valuation. The proposed model can deal with the net-cost and net-revenue projects. This study introduces weighted risk reduction factors to accommodate the interaction between public and private agencies in sharing risk outcomes and a metric to measure the optimality of a risk allocation scheme. A case study based on Indonesia’s BOT water supply project was used to demonstrate the model application. It is shown that cost efficiency and an optimal risk allocation scheme enhance VfM. Moreover, the wisdom of using the BOT option can be challenged when the public agency is at least as good as the private agency unless other reasons exist, and allocating project risk to an agency that cannot best deal with it can reduce VfM. This study concludes with model limitations and directions for future research.

Maintaining roadway pavements and bridge decks is key to providing high levels of service for road users. However, improvement actions incur downtime. These actions are typically scheduled by asset class, yet implemented on any asset type, they have network-wide impacts on traffic performance. This paper presents a bilevel program wherein the upper level involves a Markov decision process (MDP) through which potential roadway improvement actions across asset classes are prioritized and scheduled. The MDP approach considers uncertainty in component deterioration effects, while incorporating the benefits of implemented improvement actions. The upper level takes as input traffic flow estimates obtained from a lower-level user equilibrium traffic formulation that recognizes changes in capacities determined by decisions taken in the upper level. Because an exact solution of this bilevel, stochastic, dynamic program is formidable, a deep reinforcement learning (DRL) method is developed. The model and solution methodology were tested on a hypothetical problem from the literature. The importance of obtaining optimal activity plans that account for downtime effects, traffic congestion impacts, uncertainty in deterioration processes, and multiasset classes is demonstrated.

Distributed solar generation (DSG) has grown in popularity in the last decades and is attracting a growing number of adopters. With the continuously decreasing cost of photovoltaic (PV) cells and battery storage technologies, DSG systems are often cost-effective, in addition to being reliable and sustainable. DSG can offer many benefits to the electric power infrastructure by improving the resilience of the electric grid against power disruptions; improving sustainability using renewable solar energy; and reducing carbon emissions. Accordingly, several federal and local policy incentives are available to motivate DSG adoption. However, the growing penetration of DSG represents a shift from central decision-making by independent system operators (ISOs) to distributed generation expansion decisions by consumers, which creates uncertainty in estimating future demand and market trends. If not planned well, this problem may be exacerbated by policy incentives. As such, the goal of this paper is to investigate the effect of policy incentives on the adoption of DSG, and the electric power infrastructure and market. This was achieved by developing a system of systems (SoS) framework that can simulate electric power networks affected by incentivized adoption of DSG. A case study using real data and a modified IEEE six-bus system was used to test the model under different conditions of rebates or tax credits and loans with reduced interest. The results show that incentives at one location can have widely varying effects on other locations on the grid. In many cases, introducing incentives may discourage DSG adoption in other locations due to the decreasing prices caused by lower demand from the grid. In other cases, incentives may have the opposite effect, as electricity prices increase and encourage adoption of DSG. The outcomes of this paper support the need for careful consideration of the effect of DSG incentives to fully capitalize on their capabilities and improve the electric power infrastructure.

The International Roughness Index (IRI) is widely used in evaluating pavement condition, making repair decisions, assessing ride comfort, and estimating vehicle operating costs. However, it is generally costly to measure IRI, and for this reason, certain road classes are excluded from IRI measurements at the network level. It is hypothesized that it is feasible to estimate the IRI of a pavement section given its distress types and their respective densities and severities. To investigate this hypothesis, this paper uses data from in-service pavements in a Midwestern US state and multiple statistical and machine learning techniques, namely least absolute shrinkage and selection operator (Lasso) and Ridge regression, support vector regression (SVR), regression tree, and random forests methods. These techniques were used to ascertain the extent to which IRI can be predicted given a set of pavement attributes. The data set contains comprehensive disaggregate data on pavement performance (IRI) and distress variables (rutting, faulting, texture, and cracking) collected by automated equipment. The analysis results suggest that it is feasible to estimate reliable IRI at a pavement section based on the distress types, densities, and severities at that section. The results also suggest that such estimated IRI is influenced by the pavement type and functional class. The paper also includes an exploratory section that uses Gaussian techniques to address the reverse situation, that is, estimating the distribution of extant pavement distress types, severity, and extent based on the roughness value of that section.

Abstract

When critical infrastructure system services are disrupted, households typically respond by reducing, delaying, or relocating their demand (e.g., delaying laundry), or augmenting supply (e.g., using a generator). While this phenomenon is well known, there has been little systematic empirical investigation of it. Focusing on electric power and water service interruptions and using revealed and stated preference survey data from Los Angeles County, California, we develop 24 mixed logit models, one each to predict the probability an individual undertakes a specified adaptation as a function of outage duration and characteristics of the individual. The analysis aims to determine: (1) how common different household adaptations are; (2) how adaptation implementation varies with infrastructure type, outage duration, and uses of the service; (3) what household characteristics are associated with implementation of different adaptations; and (4) how adaptations tend to occur together. The percentage of individuals who report doing an adaptation varies greatly across adaptations and outage durations, from 2% to 88%. In general, adaptations that require moving out of the home are the least common of those investigated. For electric power outages, adaptations that could be done at home are less likely as the outage duration increases, while those that require going somewhere are more likely as the duration increases. For water outages, all adaptations (except delaying consumption) are more likely as an outage lasts longer. Using electric power or water for medical devices and/or work and business has a large effect on the likelihood of implementing many adaptations. Preevent conservation habits are also associated with an increased likelihood of implementing adaptations. The influence of household characteristics varies greatly across adaptations. There is evidence that some adaptations tend to occur together (e.g., using water from lakes and the government) and others tend not to (e.g., delaying electricity use and going to a hotel).

Abstract

Knowing what kinds of adaptations are found among different segments of the population, how common they are, and when they are likely to be implemented can help policymakers know what sorts of crisis-coping behaviors to expect in their locality, and can help them to know the timeframe in which those adaptations would be implemented. This in turn can provide needed knowledge for how to support those adaptations. Such support could take the form of enhanced contacts and outreach in specific populations (e.g., medically fragile), advance planning (having materials on hand, or including adaptations in drills or exercises), or timely improvising as an event unfolds.

Governments typically provide some form of guarantee to ensure the financial viability of public–private partnership (PPP) projects confronted with traffic demand shortage. Although a number of studies have analyzed the problems associated with the optimal level of guarantee and the pricing mechanism of early termination initiated by private companies or governments, most have ignored the mechanism of early termination of PPP projects with a government guarantee. This paper proposes a government decision-making framework that enables the evaluation of optimal circumstances for the early termination of PPP transportation projects where the actual revenue falls below the minimum revenue guaranteed by the government. In particular, this study analyzes how the government can determine ex-ante whether and when to grant a private company the right to early termination as well as determine ex-post whether to voluntarily terminate the project by considering the tradeoff between social benefit and cost resulting from such a termination. This study sheds light on how to develop an early termination mechanism that can be used in conjunction with the government guarantee to deal with demand risk in PPP transportation projects.

Critical facilities such as hospitals rely heavily on the functioning of complex infrastructure systems to perform efficiently. After a disaster, the functionality of these facilities is impaired because the infrastructure they rely on is impacted, and the facilities will recover as the supporting infrastructure is repaired. Infrastructure recovery planning often depends on political and economic interests, which are often conflicting and can result in a delayed or inefficient recovery. When priorities are set in the planning stages, actions can be more organized and directed, resulting in a more effective recovery, especially for critical facilities. However, existing infrastructure recovery planning approaches do not provide a systematic method for prioritizing the recovery of the functionality of critical facilities. This paper presents a framework for the postdisaster recovery planning of interdependent infrastructure systems, emphasizing the recovery of healthcare facilities. In the framework, the operation of the emergency departments of the healthcare facilities is modeled using discrete-event simulation, and a new functionality metric for the facilities is developed. The performance of the supporting infrastructure systems is quantified using a connectivity-based metric considering their interdependency using a hierarchical approach. A virtual city with healthcare facilities comprising three hospitals is used to demonstrate the proposed framework focusing on the road, power, and water infrastructure of the city. Two approaches are considered to plan the recovery of the infrastructure systems. In the first approach, recovery is planned without prioritizing any of the city’s buildings or facilities. In the second approach, the hospital network is prioritized in the recovery planning. The virtual city is used to demonstrate the proposed framework considering an earthquake scenario.

One of the main challenges in the postdisaster management of large transportation networks involves the determination of the priority and the level of service recovery for each damaged asset in the network. Presently, the application of metaheuristic algorithms in developing restoration programs is receiving increasing attention. These algorithms determine a good solution to minimize the consequences of extreme events on the network of study in a relatively short period of time. This paper investigates the suitability of a discrete particle swarm optimization (DPSO) algorithm in finding a good solution to a restoration model developed for minimizing the overall direct and indirect costs of postdisaster restorative interventions. This model can consider constraints and limitations on the available budget, work groups and equipment, as well as different levels and speeds of service recovery for assets per damage state, and the changes in the traffic flow as the restorative interventions are executed. Moreover, the model has the capacity to process complex networks; hence, it can be implemented in real-world postdisaster decision making related to the development of restoration programs. The results suggest that the DPSO algorithm is a suitable choice of optimization algorithm in situations where the number of damaged objects is medium to large.

Abstract

An important prerequisite for economic growth is the efficient and safe delivery of goods and services. In many developing countries, the most popular delivery mode for goods is road transportation. Because of the poor condition and congestion on some roads, a significant amount of productive time is lost during travel, a huge cost that hinders economic development. For instance, in Kampala, Uganda, an estimated 40% of rush-hour journeys are spent at a standstill due to narrow, dilapidated roads. In addition, pavement management has been fragmented with uncoordinated decisions taken for interlinked road aspects; for example, the inadequacy in road capacity (congestion) has been managed separately by planners, whereas durability (structural failure) has been handled by engineers despite the close links between these aspects. This study looks at three fundamental considerations for road travel, i.e., safety, condition, and capacity. The study builds a social cost model to evaluate intervention (condition improvement and capacity increase) choice for multiple road sections simultaneously by optimizing social costs after setting safety limits. The social cost model contains a travel time function that incorporates a condition term in the original Bureau of Public Roads function. The model defines social cost as a summation of travel and intervention costs incurred by the society and proposes an evaluation framework that combines both capacity and durability aspects. To show the applicability of the model, an empirical study was carried out on Ugandan road pavements.

Practical Applications

This study uses road data to build an asset management model applied to determine the intervention choice for multiple road sections concurrently by optimizing social costs incurred by society. Social cost is defined as the summation of the cost of using roads (travel time) and the cost of improving road infrastructure (intervention). The model innovatively combines condition improvement (e.g., patching) and capacity increase (e.g., increasing number of lanes) in a joint decision framework to facilitate socially optimum intervention choice after minimum safety levels have been met. The model was empirically applied to Ugandan roads. The empirical result showed the contribution to the decrease in travel time on account of condition improvement and capacity increase. Optimal intervention strategies were proposed with consideration given to the impact of change in the safety limit and the total budget to the social cost; which suggested that there may be no need to arbitrarily increase intervention budgets. This study can be applied to other infrastructure such as bridges and tunnels and can also be adapted to optimally decide effective and efficient management choices for other facilities, such as pipelines and building and road furniture, that have component parts and many possible interventions during their life time.

Road infrastructure, an essential support system providing mobility and accessibility within residential layouts, has become a serious concern in several countries. In some places, residential layouts (subdivisions) have been developed through private ownership. If the norms laid down by central development authorities are not followed by local developers, land use, including road infrastructure, is affected. This study presents a quantitative assessment of the impact of improper development of urban residential layouts on road infrastructure. The study developed an index for assessing the road infrastructure within residential layouts based on identifying the deficiencies which affect mobility and accessibility. A road infrastructure development index (RIDI) was conceptualized using an analytical hierarchy process (AHP) and parameters that affect accessibility and commuter comfort in residential layouts derived from high-resolution satellite data. The parameters used to generate this RIDI were the number of approach roads, spatial road serviceability, road density, bottlenecks, population density, dead ends or cul-de-sacs, and road width. Sample layouts were selected from varying built-up land-use classes to extract the index parameters to develop the RIDI. The RIDI values estimated for sample layouts indicate the level of development of road infrastructure in the residential layouts. A case study of the Nagpur urban area in central India, showing estimated RIDI values, is discussed. The RIDI concept can be used for proper planning, monitoring, approvals, and improvement of road infrastructure in residential layouts in urban areas, especially in the developing countries, after its calibration for varying urban scenarios.