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.

Rubber-tired trains are powered by electricity through sliding contact between the conductor rail system and collector shoe. The sliding contact produces wear on the conductor rail and wear plate, which results in malfunctions of the electricity supply. A track management system (TMS) is usually adopted to facilitate track operation; one key component of such systems is the performance model. Generally, there are three types of performance models: empirical, mechanical, and mechanical-empirical (ME). However, there are no studies on ME-based wearing performance models for conductor rail systems at present. Therefore, there is an urgent need to develop an ME-based wearing deterioration model. In this study, relevant material properties such as hardness and tensile strength, train operating parameters such as train speed, electric current, and lateral acceleration of the collector shoe, and quarterly inspection data on the wear plate and conductor rail were collected. Two ME-based wearing models were proposed, one for the wear plate and one for the conductor rail. The results showed that the performance model for the conductor rail had higher accuracy than the model for the wear plate.

Models that simulate postearthquake functional loss and restoration of water distribution systems support the development and implementation of more informed resilience-enhancing strategies. This study uses pipe damage, repair data, and a discrete-event simulation (DES) model to hindcast the water system damage and functional loss and restoration, following the 2014 South Napa earthquake. The DES model represents the water system as a set of entities (e.g., pipes, tanks, pumps) that interact through a sequence of events (e.g., earthquake damage, inspection) of variable duration that may be triggered at different points in time depending on the entity attributes (e.g., pipe vulnerability), access to resources (e.g., inspection crews), and the completion of other events. In addition to pipe damage and time-dependent inspection and repair processes, the DES model incorporates a pressure-driven analysis (PDA) that captures the restoration of water availability following the event. The tuned DES model is then used to simulate the postearthquake disruption and restoration of the City of Napa’s water supply for a $M_{\mathrm{w}}$ 6.7 event on the West Napa fault. By propagating the uncertainties in the damage to the network components (e.g., pipe) conditioned on the shaking intensity and the temporal (e.g., repair time) and resource-related (e.g., the number of inspection crews) variables, probabilistic descriptions of network component repair and water service restoration are generated.

Infrastructure networks, such as those for energy, transportation, and telecommunications, perform key functions for society. Although such systems have largely been developed and managed in isolation, infrastructure now functions as a system of systems, exhibiting complex interdependencies that can leave critical functions vulnerable to cascade failure. Consequently, research efforts and management strategies have focused on risks and negative aspects of complexity. This paper explores how interdependencies can be seen positively, representing opportunities to increase organizational resilience and sustainability. A typology is presented for classifying positive interdependencies, drawing on fundamental principles in ecology and validated using case studies. Understanding opportunities that arise from interdependency will enable better understanding and management of infrastructure complexity, which in turn will allow the use of such complexity to the advantage of society. Integrative thinking is necessary not only for mitigating risk but also for identifying innovations to make systems and organizations more sustainable and resilient.

Bridges as a key component of road networks require periodic monitoring to detect structural degradation for early warning. Early detection of loci and extent of structural flaws is essential to maintain safe bridge functioning. The elegant properties of continuous wavelet transform (CWT) in analyzing the signal in both time and frequency domains was the impetus to extensively employ this technique in structural health monitoring applications. However, the faint signature of structural damages in the recorded bridge responses curtails the merits of employing this technique. Furthermore, the selection process for the optimal CWT parameters that could capture signal discontinuities due to structural damages is an arbitrary process, which adds another level of uncertainty to wavelet transforms. This paper investigates compiling Shannon entropy to CWT to infer the loci and extents of structural damages in bridges. Entropy is a measure used to evaluate the randomness of the data. The more stochastic the data, the higher the entropy. In this article, Shannon entropy is used to associate a proper probability density function for the used wavelet to measure the entropy of the wavelet function at different scales. Implementing this technique facilitates selecting the optimal CWT parameters to better depict the signal; hence, identifying signal discontinuities becomes viable. The paper numerically investigates the fidelity of the proposed approach to identify bridge damages using midspan bridge response as well as using indirect records from a vehicle passing over the bridge. An implicit vehicle–bridge interaction (VBI) algorithm is used to mimic the vehicle–bridge interaction dynamics for different scenarios.

The public-private partnership (PPP) scheme has been applied for many years as an effective approach in improving water infrastructure and services. However, the failure of many water PPP projects is eroding public confidence in this approach. This study first thoroughly examined 35 water PPP projects that had failed in different parts of the world and, consequently, identified 60 failure drivers, classifying them into three categories (feasibility and procurement, construction and operations, and sociopolitical and external). A questionnaire survey of international experts was then conducted for the purpose of evaluating the relative significance of these failure drivers. The top failure drivers in the different stages of the PPP life cycle and those associated with sociopolitical and external aspects were examined and discussed. Furthermore, key failure drivers that could possibly lead to different types of failure were identified. The outputs of this study can facilitate stakeholders in formulating better overall strategies and specific preventive and remedial measures toward successful water PPPs in the future.

Municipalities are experiencing high inefficiency and financial burden imposed by their underperforming aging infrastructure. One-third of Canada’s municipal infrastructure is in fair, poor, and failing condition states, increasing the risk of service disruption and leaving decision-makers with no choice but to undertake immediate intervention. Furthermore, the massive number of intervention activities occurring in cities leads to detrimental social, environmental, and economic impacts on the community. Thus, coordinating municipal infrastructure maintenance is progressively becoming of paramount importance to cope with those tough challenges and reduce service disruptions, repair cost, and time. This research presents an asset management solution to ensure proper expenditure utilization while maintaining infrastructure performance. The framework revolves around three core models: (1) a central database that contains detailed asset inventory for the infrastructure systems; (2) multidimensional computational models to assess the performance of the intervention plan through the duration and cost savings as well as the asset condition state; and (3) a trilevel multiobjective goal optimization model that relies on a combination of metaheuristic rules, goal optimization, and genetic algorithms to plan the corridor interventions across the planning horizon. To demonstrate the functionality of the framework, the system was applied to a $9\text{-}\mathrm{km}$ stretch of roads, water, and sewer networks from the city of Montreal. The preliminary results of the coordinated intervention scenario displayed an overall improvement of 8%, broken down to 11% improvement in the network condition state, 7% financial savings, and 6% temporal savings, as opposed to the conventional (uncoordinated) intervention scenario. Furthermore, the coordinated intervention scenario proved to be more efficient in more than 70% of the corridors under study, revealing less number of visits to the same corridor and causing less service disruption to the surrounding community, as opposed to the conventional intervention scenario.

This paper assesses the impact of procurement strategies on the local governments’ service delivery in the context of India. The validity of the rhetorical arguments previously developed in the extant literature—innovation, market’s maturity, quality specifiability, scope economies, competition, information asymmetries and transaction attributes—that are used to promote bundled public-private partnership (PPP) arrangements are explored. This is done using longitudinal case study methodology in an abductive research setting. Six cities of India and their activities concerning the municipal solid waste (MSW) management are used for testing. Thirty-nine semistructured interviews with PPP stakeholders, observations, and documentary analysis provided the evidence. The cases have offered important insights on how and why variations tend to occur among the two procurement models, i.e., bundling and unbundling and what practices can contribute to the success of these projects.

Infrastructure has been discovered to be the backbone of an economy due to its influence on business and productivity. However, a significant investment funding gap was observed in infrastructural projects, particularly regarding toll roads. Therefore, this paper aims to provide a solution to fill this gap through the use of a hybrid financing model with deep discount project bonds and land leases. Both Latin hypercube and system dynamics simulation methods were used for analysis due to their ability to handle large and complex systems. Moreover, a case study was used to accurately depict the concept. The simulation results showed it is possible to overcome the risk of the ramp-up at first operation by avoiding land acquisition delays, minimizing construction cost, and optimizing toll road tariff and deep discount bond yield interest instead of creating a new entity. Furthermore, it is also possible for the government to improve its revenue by renting the land along the toll road in order to reduce the dependence on land acquisition costs from the state budget allocation.

Cities all over the world are converting maps of their infrastructure systems from legacy formats [such as paper maps and computer-aided design (CAD) drawings] to geographic information systems (GIS). Compared with CAD, GIS tend to offer more flexibility in terms of managing, updating, analyzing, and processing data. Nonetheless, the conversion process to GIS can be extremely challenging from a technical point of view. Moreover, the original data in a legacy format often contain errors, and pieces of infrastructure are often missing. What is more, even once the conversion process is complete, the maintenance of the data and the fusion of the data set with other data sets can be challenging. Leveraging recent technological advances (such as machine learning and semantic reasoning), this paper proposes a framework to better manage infrastructure data. More specifically, a smart data-management protocol is presented to successfully convert infrastructure maps from CAD to GIS that includes a data-cleaning procedure in CAD and machine-learning algorithmic solutions to validate or suggest edits of the infrastructure once converted to GIS. In addition, the protocol includes elements of version control to keep track of how urban infrastructure evolves over time as well as a procedure to combine GIS infrastructure maps with other data sets (such as sociodemographic data) that can be used for optimal scheduling of asset maintenance and repair.