Journal of Infrastructure Systems

The failure of critical components at navigation locks can cause unscheduled outages that impose high costs on shippers. Presently, the USACE requires that each hydraulic steel structure be inspected at least once every 25 years. At navigation locks, this requires dewatering because several critical components are located under water. Where budgets are limited, this may devolve into the practice of dewatering once every 25 years unless the failure of a critical component causes an unscheduled outage. Nine alternatives to the current practice were evaluated by comparing their costs over a 50-year planning horizon. An empirical transition matrix was used to simulate the degradation and failure of lock components, their repair or replacement, and the occurrence of scheduled and unscheduled outages. The optimal alternative minimizes the present value of lock dewatering cost, component repair and replacement cost, and shipper carrier cost. Results show that, in waterways where the ratio of scheduled to unscheduled shipper carrier cost is low, probabilistic scheduling criteria and structural health monitoring are optimal. These results demonstrate that preemptive maintenance strategies are cost effective.

Ensuring that railways provide excellent service requires the execution of maintenance interventions. As railway use intensifies, this becomes increasingly difficult due to the conflict between track possession used for train operation and the execution of interventions. One way to improve the consistency of train schedules, and thus user comfort, is to impose closure-free periods, in which no interventions are planned. The imposition of closure-free periods forces asset managers to group their interventions either before or after the closure-free periods. This encourages asset managers to exploit synergies between interventions which can reduce costs and the negative effects on users. However, it also means that maintenance interventions may need to be executed earlier or later than the times suggested when considering only their optimal life cycles. To deal with this issue, this paper investigated the effect of imposing closure-free periods on the development of intervention programs in terms of intervention costs and disruptions to service. We used a network-flow optimization model to determine the optimal intervention programs for a 5-year planning period without and with the imposition of a minimal 2-year closure-free period on a railway line in Switzerland. The effects of the closure-free period on intervention costs and service were discussed, along with the losses of executing interventions at different points in time from those suggested using optimal asset life cycles.

Network-based modeling and simulation of interdependent infrastructure systems has traditionally modeled 1 to 2 interdependency subtypes within a given model. Typically, different interdependency subtypes are modeled with special sets, variables, and parameters rather than integrated into the model as a standard feature. The most thorough current modeling efforts have modeled only four of the nine interdependency subtypes identified throughout academic literature. This paper presents the first model to incorporate all nine of the identified interdependency subtypes in a multiobjective mixed-integer program. The model is tested using a simulated flood event and a modified interdependent infrastructure dataset representing a medium- to large-scale military base. Model results identified a satisfactory short-term recovery level avoiding an additional $6 million of temporary repairs for a minimal increase in system operability. Inclusion of all nine interdependency subtypes developed accurate repair and cost schedules, whereas exclusion of any of the nine interdependency subtypes resulted in over or underestimations in cost and repair time. This model provides a more accurate repair schema when recovering from a system disruption.

In public–private partnership (PPP) projects, the government delegates the design, construction, and maintenance of an infrastructure system to the private sector. The concessionaire is responsible for the operation over a long duration before the ownership is transferred back to the government. A key factor affecting the success of PPP projects is that concessionaires often engage in opportunistic behavior to gain extra benefits at the expense of the public interest. Corresponding regulations to curb such opportunistic behavior are necessary by governments. In the PPP context, the regulations involve life-cycle activities covering all the stages. To overcome opportunistic behavior and establish regulations, the interaction between the government and the concessionaire in various stages of a PPP project is analyzed extensively in the current study based on the principal–agent theory. Overall, this study contributes a whole-life governance model to analyze the concessionaire’s behavior at every step, and the effect on the social welfare of a PPP project investigates the interaction effects between different PPP stages and helps the government to determine a suitable incentive mechanism to decrease the occurrence of opportunistic behavior. Subsequently, a decision support system is proposed for policy implementation based on the developed model.

This study presents and discusses a comprehensive work of nondestructive evaluation of a concrete bridge deck using impact echo for delamination evaluation, including data collection, processing, and presentation. Among the typical process of most nondestructive evaluation methods, this paper focuses on data visualization through augmented reality (AR). The test was conducted on the concrete bridge deck constructed by the Korea Expressway Corporation Research Institute in South Korea. The tested bridge deck was made of latex modified concrete and presented several artificial defects. A two-dimensional delamination condition map was generated and displayed along with delamination depth on a smartphone screen through AR. The entire process of the developed system was run fully automated in real time. The developed system successfully demonstrated the potential of the AR visualization approach, which is an effective and intuitive aid for human inspection for infrastructure condition monitoring with respect to the delamination of a concrete bridge deck. The intuitive interaction with the outdoor road environment by AR-based nondestructive evaluation data visualization would contribute to efficient inspection and smooth communication between participated infrastructure maintenance teams.

One of the essential components for developing a plan to manage pavements is the maintenance selection step, which is done using decision trees (DTs). In most cases, DTs are developed based on experts’ judgment. These trees are subjective and result in inconsistent decisions over time. A more objective approach is to use the data-driven trees which was addressed in multiple research studies. However, the resulting trees were limited in terms of the prediction accuracy levels achieved, the types of pavements covered, and the predicted maintenance action types. The goal of this paper is to use pavement management system (PMS) data to improve the consistency of the decision-making process in PMS to make more accountable decisions. To achieve this goal, this paper’s main objectives are to utilize the classification and regression trees (CART) algorithm to create a data-driven maintenance selection DT model and to develop a data-driven model impact evaluation approach to assess the benefit and cost of using the data-driven DT. An extensive dataset covering the three pavement types in the state of Iowa is used to achieve this objective. Also, a possible approach to evaluate the impact of using data-driven DTs to select maintenance actions instead of the subjective DTs is investigated. With 10 internal nodes and 52.5% overall prediction accuracy of five maintenance action types, the final DT model developed in this paper showed an improvement in the prediction accuracy and model complexity compared with the trees presented in the literature. This model is also expected to save the agency an average of $3.9 million compared with using the PMS output.

An overview of applying global climate model (GCM) projections of temperature and precipitation in infrastructure engineering is provided, with summarized general procedures, a review of commonly used projection products and processing techniques for different engineering applications, and sensitivity and uncertainty analyses with regard to different decisions via two case studies. Applying climate model projections in engineering requires multiple stages of decisions, which can be summarized as three steps: determining the requirements for climate variables, obtaining climate projections, and postprocessing of obtained climate projections. From the review of engineering applications in which climate projections were used, some important differences were identified during these three steps of decisions. The case studies suggested that the selections of projection products and processing techniques are subject to different kinds of uncertainty and challenges and that the sequence of decisions can lead to great variations in the resulting regional climate change projections. Based on the two case studies and the review of applications and uncertainties reported in the literature, several general observations and recommendations for applying GCM projections in engineering are offered.

This paper presents a framework to evaluate the regional and local resilience of infrastructure networks following disruptions from natural hazards. Herein, the regional resilience of a network relates to the accessibility of a community within a larger network, whereas the local resilience concerns the ability of a network to provide its intended service within the boundaries of a community. Using this framework, a methodology is developed to demonstrate its application to a road and highway transportation network disrupted by ground shaking and inundation under a Cascadia Subduction Zone earthquake and tsunami scenario. The regional network extents encompass the entire coast of the US state of Oregon. Embedded within this regional network are 18 local networks associated with coastal communities. Regional and local connectivity indexes are defined to identify the initial damage and then track the postdisaster recovery of the transportation network, i.e., evaluate the network resilience. The study results identify the attributes that lead to a regionally or locally resilient network and highlight the importance of considering local infrastructure networks embedded within larger regional networks. It is shown that without regional considerations, the time to recover may be severely underpredicted. The methodology is further used as a decision support tool to demonstrate how mitigation options impact the transportation network’s resilience. The importance of strategically considering mitigation options is emphasized as some communities see significant reductions in time to recover, whereas others see little to no improvement.

One of the essential proceedings to save the lives of the occupants of a multifloor and large building in emergency situations (such as fire and chemical spills) is the proper, timely, and safe evacuation of the building. This paper addresses the building evacuation planning as an important predisaster task and presents two equivalent mixed-integer linear programming (MILP) formulations where the first model is based on arc flows, and the second one is based on path flows. For each model, some valid inequalities are introduced to make the linear programming (LP) relaxation space closer to the convex hull of the original problem and speed up the model resolution via MILP solvers. Further, to solve large instances of the problem, a heuristic algorithm is developed. The performance of the proposed models, the effect of valid inequalities, and the efficiency of the proposed algorithm were evaluated on some instances taken from the literature and some randomly generated instances. The results confirmed the efficiency of the proposed heuristic method in terms of both running time and solution quality.

Bridges deteriorate over time due to various environmental and mechanical stressors. Deterioration is a significant risk to bridge owners (asset risk) and the traveling public (network risk). To tackle this issue, transportation agencies carry out bridge management under limited resources to preserve bridge conditions and control the risks of bridge failure. Nonetheless, existing network-level analysis for bridge management cannot explicitly consider the effects of preservation actions on network risk, measured directly by functionality indicators such as network capacity. In this paper, a novel method based on deep reinforcement learning is proposed to devise network-level preservation policies that can reflect bridge importance to network functionality. The proposed method is based on the proximal policy optimization algorithm adapted for bridge management problems and improved via distributed computing and architecture. The method is applied to an illustrative bridge network. The results indicate that the proposed method can produce significantly better preservation policies in terms of minimizing long-term costs that include asset and network risks. The devised policies are also investigated in depth to allow for transparent interpretation and easy integration with existing bridge management systems.

The rapid proliferation of tunnel construction presents challenges to the safe operation of tunnels. Although Structural Health Monitoring Systems (SHMSs) have been widely used to prevent tunnel disasters, it is still impossible to record the mechanical behavior of the full profile of structures because of a limited number of monitoring points. Along this line, this study proposes a spatial deduction model based on a machine-learning algorithm to characterize the mechanical behavior of a tunnel structure profile driven by limited monitoring data. Strain variation is considered to reflect the mechanical behaviors of the structure, and the monitoring data obtained from the SHMS of Dinghuaimen Yangtze River tunnel are adopted for these experiments. First, the framework of the spatial deduction model, which uses a nonnegative matrix factorization (NMF) algorithm, is presented. Then, the model is formulated using the monitoring data. A numeric model is developed to reflect the geological conditions in the field to compare with the data-driven model, and the spatial deduction results are used to analyze the real-time and historical mechanical behaviors of the structure. The results indicate that the sensitive positions of the tunnel structure are the arch crown, hance, and inverted arch. The correlation between the deduction result and actual data is more than 85%, and the error is less than 2.7 με, so the presented model is reasonable.

Due to the fundamental role of transportation infrastructure in the functioning of societies, the speed and cost of restoring it following a disruptive event are of utmost importance. Restoring damaged infrastructure using existing prioritization rules is time-efficient but seldom optimal. However, determination of the optimal restoration program from combinations of various interventions in time is computationally intensive and time-consuming. This paper introduces a novel approach to identify near optimal restoration programs that reduce the time between the occurrence of the disruptive event and the time the restoration work starts, using a double-stage optimization model. Moreover, the efficiency of commonly used heuristic algorithms is investigated in the proposed model, which minimizes the overall costs from the time the disruptive event occurs to the time the restoration work is complete. The results of the case study suggest that simulated annealing and particle swarm optimization are efficient algorithms for this model.

In response to bankruptcies and undesirable business performance of construction companies in Sub-Saharan African, this paper seeks to identify factors that mitigate financial distress. Findings presented will assist project stakeholders in mitigating the effects of financial distress. A quantitative research design was adopted using primary data gathered from Ghanaian contractors. A literature review was first conducted to synthesise factors that mitigate financial distress. A closed ended questionnaire was developed to collect perceptional data from contractors on the significance of the mitigation factors to financial distress factors identified within the literature review. The main analytical tools used for this study were the relative importance index (RII), Cronbach’s Alpha Coefficient, and Kendall’s Coefficient of Concordance. A total of 22 factors were identified and using the RII, the top three rated mitigation factors were delineated, namely, proper financial planning; practice of prompt payment by client; and proper estimation of project cost. This work constitutes pioneering research in the developing country of Ghana that must concentrate on mitigating financial distress in its construction industry.

The CustomizabLe ARtificial Community (CLARC) database represents a region-sized database for modeling and simulation of interdependent infrastructure systems. Close examination of the data revealed inconsistencies that are corrected for use of the database in its present form. Additionally, two underlying concerns are identified in the telecommunications and transportation infrastructure systems. Suggested improvements to these infrastructure systems are explained.