Journal of Infrastructure Systems

Infrastructures are important facilities in urban areas servicing societies. Failure of these systems could pose a significant threat to the public. Increasing the infrastructure’s reliability is one way to ensure that these facilities preserve their performance during natural hazards such as flooding. In this study, a scheme is proposed for financial resource allocation to waste water treatment plants (WWTPs) with the goal of improving resilience and reliability. For this purpose, after determining which WWTPs’ factors (characteristics) are effective on flood resilience, a multicriteria decision-making approach is used to quantify resilience. Then, factors that could be improved by financial resource allocation are identified to increase resilience. To best allocate the available financial resources to these factors, an optimization routine is proposed. This routine uses the technique for order of preference by similarity to ideal solution method and genetic algorithm, and results in a trade-off curve for each plant. To find the optimal way of allocating financial resources among all WWTPs, a second optimization routine is proposed. Finally, to evaluate the WWTPs’ performance against flooding, reliability is calculated using the load-resistance concept. Fourteen WWTPs in New York City were considered in the case study to test the methodology. Results show that by using optimization tools, financial resources allocation could substantially improve resilience and the reliability of WWTPs. The results also indicate that in evaluating the system’s performance to deal with flooding, reliability and resilience result in compatible findings; however, they measure the performance based on different concepts and enumerations. The proposed methodology can be extended and applied for other infrastructures as well.

This research investigates the adaptive capacity enhancement problem of network systems and proposes a framework for allocating limited resources during the response phase in the aftermath of a disruptive event. The proposed formulation optimizes the performance level to which a network can quickly and temporarily adapt to after a disruption. Optimal resource allocation is determined with respect to the spatial dimensions of network components and available resources, the effectiveness of the resources, the importance of each element, and the system-wide impact to potential flows within the network. The proposed framework is amenable for use with a variety of component importance measures (IM) that exist in literature as well as various settings of priorities for demand nodes and time periods. These importance measures and priorities, and the specifics of the disruptive event, may result in vastly different resource allocation strategies. The efficacy of the optimization model is demonstrated on a case study based on a French electric power transmission network under simulated disruptions. Multiple component importance metrics and resource availability scenarios are examined.

The expected final budgeted costs of infrastructure assets are often exceeded during project delivery. Being able to determine the likelihood of changes to the final budget can enable clients to implement strategies to manage and control costs during construction. To understand the changing nature of costs, this paper examines the cost profiles of 1,093 water infrastructure projects that were delivered by a water utility company. Cost overruns were experienced in 656 projects. Only 1 project was delivered on budget; the remaining 436 were completed under final budget approval. A mean cost overrun and underrun of $+19.97$ and $-32\%$, are found, respectively. The best-fit distribution for cost overruns and underruns for determining their probability of occurrence is calculated. The research moves beyond examining the cost performance of heterogeneous data sets that have dominated previous studies to the use of a homogeneous sample, which enables more-reliable contingency forecasts to be determined.

Utility managers in charge of water pipe networks that are exposed to high seismicity make difficult decisions regarding the allocation of a limited rehabilitation budget to be most effective in enhancing a network’s postearthquake serviceability. Seismic vulnerability models are typically integrated with simple prioritization methods to identify the critical pipes subjected to earthquakes. These methods do not distribute resources at the system level and may not provide an economical solution. The objective of this paper is to develop an approach to identify the critical pipes for proactive seismic rehabilitation that will enhance a network’s postearthquake serviceability when only a finite length of pipes can be rehabilitated. To achieve this objective, a proper stochastic combinatorial optimization was formulated and then solved, using a genetic algorithm that was integrated with a network-level seismic vulnerability model. The approach was implemented to identify critical links for proactive seismic rehabilitation of two benchmark networks. The results showed that this approach outperforms the simple length-based prioritization methods used by the utilities, as well as the latest proposed methodology in the literature, in identifying the critical pipes in a water pipe network subjected to an earthquake.

This study applies spatial analysis to investigate the relationship between crashes, wet road conditions, and physical roadway variables. Wet roadway and flood risk factors are not included in the data-driven systemic safety analysis approach currently used by the Federal Highway Administration’s Office of Safety. The presented methodology identifies variables that influence crash frequency under wet road conditions by examining spatial correlations between crash data and factors expected to influence roadway drainage patterns. Predicted variables were examined of the geographical information system software ArcMap, such as surface slope, urban influence, and stream influence (relative location within the floodplain). Analysis results revealed a statistical relationship between the predicted variables and the crash severity level. Specifically, urban influence was identified as the most influential factor (37% correlation) followed by stream influence (10% correlation). This information reveals correlations between variables in the physical environment, which may eventually be used to identify areas of high crash risk in existing roadway infrastructure and thus improve the ability for practitioners to develop proactive safety measures.

Even as new incentive programs emerge to encourage homeowners to strengthen their homes so as to reduce the risk of damage in extreme events, little is known about how homeowners make such decisions. In this paper, we combine revealed and stated preference survey data to develop separate mixed logit models for homeowner decisions about retrofits aimed at addressing four different types of hurricane damage—wind damage to the roof, openings (windows, doors), and roof-to-wall connection, and flood damage. Results provide evidence that offering a grant increases the likelihood of retrofitting, but offer no such evidence for incentives in the form of low-interest loans or insurance premium reductions. The models also suggest that the probability of retrofitting varies by type (e.g., installing shutters versus strengthening the roof), with the most interest in strengthening openings, and that homeowners are more likely to retrofit when they are closer to the coast, younger, in newer homes, or within a year of a hurricane experience.

Planning agencies increasingly use pavement management systems (PMS) to determine an optimal preservation strategy for their roadway assets. To develop cost-effective resource allocation policies, it is important that a PMS embed pavement degradation models that accurately depict its progression over time. Presently, PMS frameworks use two broad classes of methods (Markov chains and simplified, trend-stationary regression models) to project pavement degradation. These approaches make contradictory assumptions regarding (1) the degree to which variation is aleatory/epistemic, and (2) the long-term persistence of sudden changes in pavement distress. Consequently, this research constructs a panel data variance ratio test to evaluate if pavement degradation conforms to a hypothesis that convolves the assumptions of the two prevailing approaches: a random walk with drift that captures relevant exogenous information. The authors apply their model to publicly available data on pavement roughness, one pavement distress mechanism of primary interest to planners, as part of the Federal Highway Administration’s Long-Term Pavement Performance (LTPP) program. The case study results are unable to reject the null hypothesis that pavement roughness follows a random walk with drift, a model structure that contradicts the current assumptions of PMS platforms. The methods developed by the authors offer decision makers an opportunity to augment their current PMS approaches, because the misspecification of pavement degradation will cause such decision-support tools to select suboptimal allocation policies.

Due to the growing trend of world economic globalization and “The Belt and Road” initiative launched by the Chinese government, transnational public–private partnership (TPPP) has become a crucial way for construction and investment companies to invest in other countries. However, investing in another country for PPP projects is not free of risks, due mainly to unfamiliarity with different environments and conditions. This paper aims at identifying and evaluating risk factors in TPPP projects. Drawing on the X hydropower project, the identified 22 critical risk factors affecting the TPPP project were assessed by industry experts who had direct involvement in the project. The intuitionistic fuzzy analytic hierarchy process (IFAHP) technique was used in the assessment of the risk factors. The results indicated that technology, natural environment, construction, administrative, and political risk were the top five risk factors. The findings of this study improve the understanding of the risk factors in TPPP projects and could be useful for risk management professionals and researchers. Future research is needed to develop risk assessment and management models for TPPP projects based on current risk factors in TPPP projects.

An individual’s perceived risk is an important source for determining responses during a fire evacuation. The level of perceived risk is continuously influenced by an interpretation of the updated emergency information from the surroundings. In this regard, this research aims to model and analyze the effect of perceived risk that changes from observing dynamic emergency cues (i.e., information from the fire itself, other occupants, and facility emergency systems) on evacuation behaviors in public building fire situations using an agent-based modeling approach. The simulation results highlight the difference in the effect of dynamic emergency cues on evacuation performances based on the type of emergency cues and facility layout. In addition, the result emphasizes the necessity of considering the side effects of dynamic emergency cues on the evacuation performance. Using the developed model, this research provides insights into the appropriate use of dynamic emergency information considering the extended influence of perceived risk on evacuation performance.

This paper examines Leadership in Energy and Environmental Design for Neighborhood Development (LEED-ND), a sustainability rating system for neighborhoods, to determine the extent to which its supported actions also contribute to a neighborhood’s specified resilience. A wide range of future shocks and stresses are considered; some of which relate to climate change. A matrix is created in order to test the resilience potential of 68 sustainability actions extracted from LEED-ND against 24 possible future shocks/stresses. Many actions encouraged by LEED-ND contribute to resilience to a number of future shocks and stresses, especially energy shortage, heatwaves, aging population, population increase, and extreme precipitation. However, to some future shocks and stresses, LEED-ND contributes minimal or no resilience (e.g., sewage infrastructure failure). The LEED-ND action that contributes resilience to the greatest number of future shocks/stresses is good connectivity. There are a few conflicts between LEED-ND and resilience goals (e.g., compact development), but they are minimal. It is concluded that LEED-ND does contribute to a neighborhood’s specified resilience, but not comprehensively so or in the most optimal way.

A vulnerability assessment of bridges subjected to hurricane events is beneficial to policy makers prioritizing their critical assets for inspection, maintenance, and retrofit. The proposed parameterized fragility assessment effectively builds a relationship between the hurricane hazard intensity/bridge parameters and bridge response by implementing a metamodeling approach, and produces a fragility surface which describes the probability of failure in terms of two hazard intensity measures: wind speed and water elevation. These environmental parameters are practical indicators of hazard intensity. The independent variables comprise bridge material/geometric parameters as well as hazard intensity parameters, and the output parameters represent a binary classification of bridge failure or no-failure states. The proposed approach is used in a vulnerability assessment of Georgia’s coastal bridges using three-dimensional finite-element models. One thousand five hundred statistical samples are generated using a range of parameters determined from common bridge types to study seven metamodels. The results indicate that the random forest model is the most suitable in predicting hurricane-induced failure, and that fragility surfaces are successfully created for two hazard intensity parameters.

Why do outdated bridges persist even when there are capable engineering management systems to remediate such systems and their associated costs? This study explores the institutional constraints that inhibit bridge managers from addressing these challenges. More specifically, it depicts bridges as institutional relics, whereby a bridge’s physical attributes reflect the accepted standards of the time and later persist even when those standards may change. Through a systematic analysis of the National Bridge Inventory, bridges are more likely to be institutional relics when built prior to the adoption of national design mandates (regulative-based relics) or in locales whose engineering norms conflict with these national standards (normative-based relics). Whereas state owners seem more sensitive to regulatory pressures, substate and private owners seem more sensitive to normative pressures. Finally, more physically constrained urban settings impede the ability of bridge managers to address those bridges that are identified as relics. These results are tied to contemporary issues in bridge management and used to propose policy actions to better address restrictive clearance issues.

This paper addresses the use of traffic-speed deflection devices for the structural evaluation of pavements at the network level as part of an effort funded by the Federal Highway Administration. Highlights from three major efforts—gathering of information to identify viable devices, field trials at or near the MnROAD facility to evaluate viable devices, and data analyses to identify and select the best deflection indices—are presented. In addition, suggestions for the implementation of the results within network-level pavement management system applications are provided and technology improvements recommended.

Bridge quality control plans and asset management systems are often integrated in collective frameworks used by authorities nationally and internationally. Similarities among these frameworks include the utilization of performance indicators for quality appraisal. However, there are discrepancies in the ways such frameworks are researched, developed, and applied. Additionally, there are problematic aspects even in their common characteristics, like the relative absence of correlation between observed performance indicator values and benchmarked ones, and the deliverables’ disparity due to the diversity of the quantified indicator variables. For the alleviation of such problematic aspects, this paper presents a bridge quality appraisal methodological and computational framework. It builds on (1) the adaptation of the sustainable building methodology and its combination with expert input solicitation methods and the research findings of the European Cooperation in Science & Technology (COST) Action TU1406, and (2) a multi-input adaptation of the analytical hierarchy process. The methodology is presented in terms of its general procedural steps and calculating requirements, and then it is tailored to reinforced concrete overpass roadway bridges. The methodology’s application in a real case study concludes the presented research.

Increased runoff rates on paved surfaces pose safety risks to city residents. Therefore, research on permeable pavement, with the goal of reducing runoff, has been growing in popularity. This study uses experimental and numerical approaches in the hydrologic design of a permeable-pavement system. The concepts of fluid flow are used to represent water flow in permeable media, and a numerical analysis is used to solve the differential equation. Thickness and porosity are the main variables in the simulation. Using a time–space model, the study finds that varying these factors has a significant effect on the characteristics of water discharge, which is illustrated using a cumulative hydrograph. The relationship between base layer properties and the initial flow delay is linear while that between storage capacity and water loss is nonlinear. Finally, a mathematical function is generated from simulation results that represent the water flow conditions, given various base layer properties of the permeable-pavement system, and can be used as a predictive function.

Public Administrations need to raise private financial resources to provide for infrastructures. These projects are, however, perceived by investors as risky, so it is necessary to look for risk-sharing mechanisms that make them attractive and fundable. The establishment of a contingent subsidy linked to the volume of traffic reduces the variance of cash flows and reduces the risk of the project. The optional nature of this type of mechanism means that the project must be assessed within the real options framework, because traditional methods of project valuation are inappropriate when they incorporate flexibility and uncertainty. This work quantifies the value of a subsidy to the project depending on the volume of traffic under two different approaches: using analytical methods and simulation. The results show that the establishment of a subsidy according to traffic levels contributes an important value to the project.