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.

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.

Visual inspection of rock bolts could give some indications on their condition and performance. However, it might be misleading because most of the rock bolts are embedded in concrete or grouted in rock mass. Nondestructive methods have to be used for a proper diagnosis of the condition of rock bolts. This study deals with the determination and the location of the corrosion in rock bolts embedded in concrete using a pulse-echo test. Complementary tests were performed using an acoustic emission (AE) test, and measuring the mass loss from corrosion current. Pulse-echo test results showed that a decrease of the energy of the back echo and the trailing wall echo is indicative of the corrosion state. This method enables locating the flaws associated with the corrosion of rock bolts by the calculation of the normalized energy ratio between the echoes coming from the threads of healthy and corroded rock bolts. Results of acoustic emission and imposed electrochemical potential provide information on the corrosion initiation. This research indicates that the pulse-echo is an effective measurement technique for corrosion detection.

An understanding of the impacts of climate change on infrastructure is important in the context of reducing future socioeconomic losses. Previous research has introduced models based on empirical and mechanical analyses to predict the behavior of infrastructure considering climate change. However, the uncertainty and unavailability of information regarding climate change prevent the widespread use of these previous models. This paper presents a method for developing a climate impact-assessment system using fuzzy inferences to predict the alteration of infrastructure service life. Fuzzy inferences enable an understanding of the impacts of climate on infrastructure, with expert knowledge reflecting the interactions between multiple environmental factors. Based on the proposed method, the impacts of climate change on infrastructure can be analyzed by obtaining an expected service life with respect to various climate scenarios. A case study was conducted on a flexible pavement road in Alabama to show the applicability and utility of the method. The proposed method is expected to improve infrastructure management and planning practices, establishing proactive adaptation strategies to minimize additional infrastructure expenditure and damage.

In the case of a two-layer system, the impact-echo test has classically assumed that two frequency peaks are expected in the spectrum. In this paper, the spectrum of the impact-echo test is studied from a guided-wave approach both for the single-layer and for the two-layer cases. The results show that the classical impact-echo interpretation cannot predict, in general, the position of the peaks and that the classical results based on the impedance contrast between layers are not valid. As an alternative, a guided-wave approach based on the dispersion curves of the system is presented. Reasons why the impact-echo classical interpretation seems to work successfully in some cases are given. It is found that only in the particular cases where the times of flight of the P waves inside each layer, separately, are very similar, the classical predictions are valid for the main frequency. Finally, it is shown that the frequency peak values always correspond with zero group velocity frequencies or with cutoff frequencies in the dispersion curves. Results are tested with real field work.

Single-sided nuclear magnetic resonance is a nondestructive analytical technique by which the ingress behavior of hydrophobing agents as well as the properties and performance of the resulting hydrophobic layers can be assessed quantitatively. The method is used to compare the behavior of two low-molecular-weight hydrophobing agents, $n$-octyltriethoxysilane and $i$-butyltriethoxysilane, on concrete and to assess the properties of the resulting hydrophobic layers. For the octyl derivative, ingress appears to be a simple transport process without pronounced polycondensation in the liquid phase or reaction with the pore walls because the detectable amount stays rather constant during the first day of observation. In addition, the thickness of the resulting hydrophobic layer correlates well with the endpoint of the transport process. In contrast, the detectable amount of the butyl derivative is reduced to half during the first 18 h, indicating either substantial polycondensation or anchoring to the pore walls, and the hydrophobic layer is approximately 25% thicker than indicated by the ingress profiles. The different behavior could originate in differences in the molecular structure, particularly the steric demand of the alkyl substituents, although this requires a more detailed study. Both compounds proved to be excellent hydrophobing agents since the layers were found to be impermeable when kept in a shallow water bath (unidirectional transport through the layer) for more than 1 year. The results demonstrate that single-sided nuclear magnetic resonance is an excellent nondestructive tool for quality assurance when applying such protective layers.

Among several nondestructive testing methods, impact echo (IE) and ultrasonic surface waves (USW) have shown promising results for performance monitoring and condition assessment of concrete structures. To ensure their proper use, it is critical to quantify the potentials and limitations of those methods and to specify the degree of sensitivity of each method to different structural and defect parameters. In this study, the sensitivity of the IE and USW methods are investigated through high spatial resolution and extensive numerical simulations of intact and defective slabs. The results of the experimental and numerical studies show that the effectiveness and reliability of these two methods are limited by their sensitivity to several parameters, including boundary conditions, the geometry and material properties of a concrete structure, and the size and location of discontinuities in structures. Given the sensitivity of each method to several parameters, the combination of the IE and USW tests is recommended for more reliable quality control of newly constructed concrete structures and condition assessment of existing ones.

Highway agencies seek to establish road user cost responsibilities, in the form of marginal costs associated with the maintenance and rehabilitation (M&R) of their existing infrastructure, on the basis of lifecycle data on the infrastructure usage levels and repair costs. Due to the differences in physical characteristics and operational conditions across individual pavement segments, it can be hypothesized that the current practice, which imposes a uniform average user fee to cover repair damage of all pavements systemwide or within specific families, is not equitable. To address this issue, this paper assesses the marginal costs of pavement damage by accounting for segment-specific heterogeneity. To do this, the paper uses a random-parameters (RP) regression model. Through application of the developed model, the paper shows that the M&R marginal cost differs significantly across pavement segments. The results suggest that it is feasible for agencies to develop fee structures that charge different highway user fees for individual highway segments on the basis of the damage the users inflict to the pavement. This outcome can help agencies introduce more equitable charging for the use of their highways.

Predetermined evacuation zones can be used to estimate the demand of evacuees, which is helpful in assessing the resilience of transportation systems in the presence of natural disasters. Evacuation zones defined based on current road networks and environmental and demo-economic characteristics of a region cannot remain the same in the future because long-term climate change such as the rise of sea level would have major impacts on hurricane-related risks. Traditional methods for the prediction of future evacuation zones rely heavily on the storm surge models and could be time-consuming and costly to use. This study develops a novel grid cell–based data-driven method that can predict future evacuation zones under climate change without running the expensive storm surge models. The map of Manhattan, which is the central area of New York City, was uniformly split into $45\times 45{\mathrm{m}}^2$ grid cells as the basic geographical units of analysis. A decision tree and a random forest were used to capture the relationship between grid cell–specific features, such as geographical features, evacuation mobility, and demo-economic features, and current zone categories that could reflect the risk levels during hurricanes. Tenfold cross validation was used to evaluate model performance and it was found that the random forest outperformed the decision tree in term of the accuracy and kappa statistic. The random forest was used to predict the delineation of evacuation zones in the 2050s and 2090s based on the predicted sea-level rises and changes of demo-economic features. Compared with the current zoning, the areas with a need for evacuation are expected to expand in the future. The proposed method can be used to promptly estimate the future evacuation zones under different sea-level rise scenarios and can provide the convenience to assess transportation system resilience in the context of climate change.

Selection and construction of sustainable infrastructure is a complex process due to multicriteria decision requirements. The analytic hierarchy process (AHP) is a widely used multicriteria decision tool in civil engineering applications. However, the chief drawback of AHP is that it requires a large number of inputs (pairwise comparisons), which frequently leads to inconsistency in decision making. The main objective of this study is to reduce the number of inputs in AHP without compromising consistency. This study proposes to modify AHP with the use of the additive transitivity property of fuzzy preference relations because it significantly reduces the number of inputs, which minimizes inconsistency. The effectiveness of the proposed method was verified through a case study for selecting a contractor from six contractors. Inputs were taken from three decision makers for both traditional and proposed AHP, and the rankings obtained by the proposed method were compared with traditional AHP. The comparison revealed the use of a lesser number of inputs in conjunction with additive transitive fuzzy preference relations generated consistent judgments in minimal time.

Short-duration, high-intensity rainfall causes significant disruption to transport operations, and climate change is projected to increase the frequency and intensity of these events. Disruption costs of flooding are currently calculated using crude approaches. To support improved business cases for adapting urban infrastructure to climate change, this paper presents an integrated framework that couples simulations of flooding and transport to calculate the impacts of disruption. A function, constructed from a range of observational and experimental data sources, is used to relate flood depth to vehicle speed, which is more realistic than the typical approach of categorizing a road as either blocked or free flowing. The framework is demonstrated on Newcastle upon Tyne in the United Kingdom and shows that by the 2080s disruption across the city from a 1-in-50-year event could increase by 66%. A criticality index is developed and is shown to provide an effective metric to prioritize intervention options in the road network. In this case, just two adaptation interventions can reduce travel delays across the city by 32%.

Even though the concept of resilience has been around since the 1970s, it has recently become a critical part of discussions among transportation stakeholders and planners after natural disasters took a toll on U.S. infrastructure, including bridges, and left many stranded without basic infrastructure services or access to emergency facilities. This paper expands on previous research on transportation asset resilience and proposes a practical and simplified multistage framework to analyze and assess bridge resilience. Even though the proposed framework is qualitative, it directly addresses some of the key aspects of bridge operation and captures expert knowledge and lessons learned from the previous extreme events through a series of case studies. Two case studies are presented for bridges that were affected during Hurricane Katrina to exemplify the potential value of this approach in evaluating the resilience of bridges utilizing the existing bridge inventory data. The paper concludes with observations, comments, and suggestions for the next steps in improving the framework.

In the last few years, research studies have assessed the vulnerability of roadways to disasters and extreme events. However, in many of these studies the impact of the endogenous characteristics of the roadway network (such as the condition of the roads, and existing travel on the network) on roadway system vulnerability has not been adequately investigated. This paper investigates how condition of the roads, travel demand, travel patterns, and the position of links within the network affect the dynamics of vulnerably of the roadway systems. The method is applied to the Istanbul highway system and provides a probabilistic assessment of the vulnerability of the network using travel time and efficiency. The findings show that travel time vulnerability depends on the travel pattern in the network and increases with travel demand. The results also show that efficiency vulnerability and travel time vulnerability increase nonlinearly with network deterioration. When the network deterioration is not severe (disruption probabilities are low) the network efficiency vulnerability and travel time vulnerability depend on the average disruption probability of the roads within the network. With further deterioration (higher disruption probability values), both efficiency vulnerability and travel time vulnerability depend not only on the average disruption probability of the network but also on disruption probability of each link.

The vulnerability of our nation’s transportation infrastructure to climate change and extreme weather is now well documented and the transportation community has identified numerous strategies to potentially mitigate these vulnerabilities. The challenges to the infrastructure sector presented by climate change can only be met through collaboration between the climate science community, who evaluate what the future will likely look like, and the engineering community, who implement our societal response. To facilitate this process, the authors asked: what progress has been made and what needs to be done now in order to allow for the graceful convergence of these two disciplines? In late 2012, the Infrastructure and Climate Network (ICNet), a National Science Foundation–supported research collaboration network, was established to answer that question. This article presents examples of how the ICNet experience has shown the way toward a new generation of innovation and cross-disciplinary research, challenges that can be address by such collaboration, and specific guidance for partnerships and methods to effectively address complex questions requiring a cogeneration of knowledge.

The risk in multiyear asset management plans for pavement networks stems from uncertainties in inputs to these plans. Although the literature is rich in probabilistic models and procedures for individual components of asset management systems (e.g., probabilistic lifecycle cost analysis and probabilistic performance prediction models), risk and uncertainty have not yet been fully and explicitly incorporated into asset management plans. In this paper, performance risk is expressed as the probability of failing to sustain an average Condition Score greater than or equal to 70 (out of 100) throughout the planning horizon and as the probability of not being able to maintain 90% of the network in what could be classified as a good or better condition. This paper provides a methodology for incorporating risk assessment into asset management plans by accounting for uncertainties in key inputs to these plans with specific application to pavements. The inputs used include current pavement condition, available maintenance and rehabilitation funds, unit cost of maintenance and rehabilitation (M&R) treatments, and performance predictions. The uncertainties in these inputs are modeled as probability distributions, and the corresponding risks are evaluated using a Monte Carlo simulation. Maintenance and rehabilitation projects at network level are selected annually optimizing the benefit-cost ratio. This methodology was applied to a portion of the pavement network in Texas to demonstrate the practicality of the proposed approach. This pilot evaluation suggests that uncertainties in performance predictions and M&R unit costs have the greatest effect on the network’s performance risks in a multiyear planning period. This methodology also helps to evaluate the sensitivity of the overall risk to the sources of uncertainty. The incorporation of traffic uncertainty and monetarization of the benefit and consequently the risk can improve the methodology.

Timely implementation of bridge deck rehabilitation is important for sustaining the serviceability of bridges. With the chronic underfunding of infrastructure rehabilitation, the application of an accurate prioritization method is needed in order to maximize the investment effects of the limited budgets allocated for bridge deck rehabilitation. The prioritization methods currently used to plan bridge deck rehabilitation projects have evolved to the point where multiple attributes such as functional, economic, social, political, and environmental impact are integrated to determine the relative importance of rehabilitation needs at a network level. However, integrated scale-based prioritization methods measure the physical condition of an individual bridge deck as a part of these multiple attributes so it is possible for an individual bridge deck in physically urgent condition to be delayed to later fiscal years, at which time its further deteriorated condition may exceed the minimum condition limit. Therefore, a method is needed whereby this undesirable outcome is handled during the decision-making process. The objective of this paper is to propose a multitiered prioritization method that takes two hierarchical selection steps based on the urgency scale and total prioritization scale to prioritize rehabilitation projects in order to minimize the potential problems in the existing prioritization methods while maintaining the strengths of existing integrated prioritization methods. The urgency scale is defined as the severity level of the physical condition of an individual bridge deck for rehabilitation. It is calculated by estimating the acceptable time frame within which a rehabilitation project can be delayed until the physical condition of the bridge deck goes beyond the minimum threshold for rehabilitation. The proposed multitiered prioritization method is applied to a concrete bridge deck network as a case study. The results of the proposed method are compared to the results of other commonly used prioritization methods to demonstrate its effectiveness.

Nondestructive evaluation (NDE) methods have received growing acceptance in many testing tasks in the assessment of concrete infrastructures. Substantial progress in NDE methods for concrete structures can be achieved by discussing the specifics of each testing scenario. Because of the large variety of testing scenarios, the testing tasks must be isolated into subtasks, which can then be solved by NDE methods. A classification scheme for NDE tasks is described and discussed in this paper. Four major groups have been identified: the construction process, the concrete structure, physical or chemical processes, and material properties. For each of these groups, a number of subtasks are described. Typical parameter ranges and resolution requirements are illustrated and major influencing factors listed. Reference specimens may be designed to be used for performance evaluation, validation, and certification of NDE methods. The classifications can also be used to draft a research road map that benefits both the owners of infrastructure and the instrument developers.

Predictions of climate change can significantly affect the optimization of measures reducing the long-term risk for assets exposed to extreme events. Although a single climate model can be represented by a Markov stochastic process and directly integrated into the sequential decision-making procedure, optimization under epistemic uncertainty about the model is computationally more challenging. Decision makers have to define not only a set of models with corresponding probabilities, but also whether and how they will learn more about the likelihood of these models during the asset-management process. Different assumed learning rates about the climate can suggest opposite behaviors. For example, an agent believing, optimistically, that the correct model will soon be identified may prefer to wait for this information before making relevant decisions; on the other hand, an agent predicting, pessimistically, that no further information will ever be available may prefer to immediately take actions with long-term consequences. This paper proposes a set of optimization procedures based on the Markov decision process (MDP) framework to support decision making depending on the assumed learning rate, thus trading off the need for a prompt response with that for reducing uncertainty before deciding. Specifically, it outlines how approaches based on the MDP and hidden-mode MDPs, dynamic programming, and point-based value iteration can be used, depending on the assumptions on future learning. The paper describes the complexity of these procedures, discusses their performance in different settings, and applies them to flood risk mitigation.

Pavement deterioration models are an important part of any pavement management system. Many of these models suffer from endogeneity bias because of the inclusion of independent variables correlated with unobserved factors, which are captured by the model’s error terms. Examples of such endogenous variables include pavement overlay thickness and maintenance and rehabilitation activities, both of which are not randomly chosen but are in fact decision variables selected by pavement engineers based on field conditions. Inclusion of these variables in a pavement deterioration model can result in biased and inconsistent model parameter estimates, leading to incorrect insights. Previous research has shown that continuous endogenous variables, such as pavement overlay thickness, can be corrected using auxiliary models to replace the endogenous variable with an instrumented variable that has lower correlation with the unobserved error term. Discrete endogenous variables, such as the type of maintenance and rehabilitation activities, have been accounted for by modeling the likelihood of each potential outcome and developing individual deterioration models for each of the potential responses. This paper proposes an alternative approach to accommodate discrete endogenous variables—the selectivity correction method—that allows a single model to incorporate the impacts of all discrete choices. This approach is applied to develop a pavement-roughness progression model that incorporates both continuous and discrete endogenous variables using field data from Washington State. The result is a roughness progression model with consistent parameter estimates, which have more realistic values than those obtained in previous studies that used the same data.

A system is antifragile if its performance improves as the result of exposure to stressors, shocks, or disruptions. This behavior is typical of complex systems and is not usually exhibited by engineered technical systems. In fact, technical systems can display antifragility when new investments are allocated, e.g., after disasters. This study proposes an optimization model for the postdisaster restoration planning of infrastructure networks, taking into account the possibility of combining the construction of new components and the repair of failed ones. The strategic goal is to determine the optimal target system structure so that the performance of the target system is maximized under the constraints of investment cost and network connectivity. The problem is formulated as mixed-integer binary linear programming (MILP), and an efficient Benders decomposition algorithm is devised to cope with the computational complexity of its solution. The proposed approach is tested on a realistic infrastructure network: the 380-kV power transmission grid of northern Italy. The results show that the restored network can achieve an improved functionality as compared to the original network if new components are constructed and some failed components are not repaired, even when the former is much more expensive than the latter. Therefore, antifragility provides an opportunity for the system to meet future service demand increases and a perspective under which disruptions can be seen as chances for system performance improvements.

The authors present a Bayesian network (BN)-based approach for modeling and reliability assessment of infrastructure systems. The BN is a powerful framework that is able to account for uncertainties in component and system parameters, and perform updating of system assessments with new information. The exponential increase in memory storage required for the BN model as the size of the system increases has limited the applicability of BNs for reliability assessment of large infrastructure systems. Recently, a data-compression method was proposed to address this limitation. While significantly reducing the memory storage, computational time for constructing the BN and performing inference increased. In this paper, new methodologies are developed to increase the computational efficiency of a compression-based approach for BN modeling and reliability assessment of infrastructure systems. These include algorithms to improve the computational efficiency of the initial compression for constructing the BN, subsequent inference over the network, and overall system formulation. The algorithms are applied to a test example system to examine their performance for systems of increasing size, as well as to a 59-component power distribution network to demonstrate application to real systems. Performance of the proposed methodologies is compared to that of an existing, widely used BN algorithm. With the heuristics employed, the new algorithms are shown to achieve significant gains in both memory storage and computation time, enabling the modeling of large infrastructure systems as BNs for system reliability analysis.

Highway engineers understand the damaging impact on the performance of the road pavement when excess water enters into the pavement. As a consequence, most pavement design and rehabilitation methods recognize the importance of addressing moisture damage and related drainage deficiencies to ensure expected pavement performance. This study develops and demonstrates the application of the fault tree analysis (FTA) technique, to identify the moisture damage potential in flexible road pavements. The FTA includes qualitative and quantitative approaches for risk analysis. The qualitative risk assessment helps to identify the potential causes of any premature failure in the road pavement. The quantitative risk analysis involves the adaptation of a prediction method to identify the probability of failure at different stages of the lifecycle of the road pavement. This allowed both the early development of moisture damage in road pavements, along with permanent damage to be identified in the case study. The assessment of the FTA concluded it to be a viable technique for identifying risks associated with flexible road pavements that are prone to moisture damage.

To improve the resiliency of designs, particularly for long-lived infrastructure, current engineering practice must be updated to incorporate a range of future climate conditions that are likely to be different from the past. However, a considerable mismatch exists between climate model outputs and the data inputs needed for engineering designs. This paper provides a framework for incorporating climate trends into design standards and applications, including selecting the appropriate climate model source based on the intended application, understanding model performance and uncertainties, addressing differences in temporal and spatial scales, and interpreting results for engineering design. The framework is illustrated through an application to depth-duration-frequency curves, which are commonly used in stormwater design. A change factor method is used to update the curves in a case study of Pittsburgh. Extreme precipitation depth is expected to increase in the future for Pittsburgh for all return periods and durations examined, requiring revised standards and designs. Doubling the return period and using historical, stationary values may enable adequate design for short-duration storms; however, this method is shown to be insufficient to enable protective designs for longer-duration storms.

In recent years, public–private partnership (PPP) projects’ success has become an important issue for both practice and research because of the growing number of developing countries adopting the PPP policy. However, success means different things to different stakeholders, and most essentially success is an abstract conception. This paper therefore develops a pragmatic model to quantify the success of PPP projects in developing countries using a fuzzy synthetic evaluation method. The project success index model developed consists of three major critical success criteria groupings for PPP projects in developing countries; these include local development and disputes reduction, profit, cost, and technical specifications. Practitioners in developing countries can use the success index equation to determine the success levels of their projects in a more practical and objective manner. Additionally, the evaluation model makes it possible to compare the success levels of different PPP projects on the same basis for benchmarking purposes.

Knowledge about the strength gain of reinforced concrete structures facilitates speedy and accurate decision making in formwork removal and transfer of prestressing forces for prestressed concrete. In the current study, a set of two smart aggregates embedded in concrete cubes have been investigated for their suitability in evaluating the strength gain during the initial curing regime, through electromechanical impedance (EMI) signatures. Correlation of EMI signature from these embedded smart aggregates with progressive strength loss is also established. Different statistical metrics have been evaluated to quantify the variations between the reference and modified states. A relation between root-mean-square deviation (RMSD) and strength parameter is developed, which potentially could be used for arriving at the characteristic strength gain. The novelty is the proposed serial/parallel connected multisensor configuration with a cluster of lead zirconate titanate (PZT) sensors to monitor early-stage characteristics and later-stage strength evolution under a large-scale sensor deployment scenario. The experimental investigation on PZT-bonded concrete shear wall looked for variations in EMI signature under crack opening conditions. Experimental studies on PZT-bonded metal structures have also been carried out to study the variation in modal density characteristics and the effect of low-frequency vibration on the resulting EMI signature.

The peculiar nature of bridge infrastructure condition data persistently poses challenges in predicting bridge component deterioration that necessitate the continued investigation of probabilistic modeling techniques. These challenges include the uncertainty that characterizes bridge condition data due to the inherent random nature of deterioration factors and the existence of other variables that are not typically measured (unobserved factors responsible for deterioration), the panel nature of the data and its consequent observation-specific correlation and heterogeneity bias, and the lack of knowledge of the type and nature of past interventions. To these ends, this paper introduces a novel probabilistic modeling methodology intended to enhance the reliability of condition prediction by defining and quantifying the types of interventions and incorporating newly introduced explanatory variables to capture the effect of these intervention types on the future deterioration of bridge components. Unlike the current probabilistic techniques, the methodology proposed in this paper duly accounts for the maintenance and intervention history of in-service bridge components in the condition modeling process to reflect the real-world nondecay deterioration of the components. This paper demonstrates how the developed methodology can be implemented to predict the probability that a bridge component is in a given condition state at any given year. The marginal effects analysis provides bridge engineers with additional information about the individual strengths of the influential factors of bridge deterioration. The developed models can be useful for various agency functions, including the monitoring of bridge component performance and predicting their remaining service lives based on the bridge design and operational and environmental attributes.

Transport resilience is an important area of research in the global effort to adapt to climate change. This paper introduces and applies a stochastic modeling methodology to assess the impact of multihazard events. Most cities are exposed to multiple types of extreme events, sometimes simultaneously, and focusing on single events may lead to inadequate design recommendations. By assigning failure probabilities of road segments and by estimating road failure through Monte Carlo simulation, roads and areas that are particularly vulnerable to multihazard events can be detected. The performance of the large-scale road network of the Tokai region in Japan (prone to both typhoons and earthquakes) is analyzed by considering three scenarios of hazards: flash flood, earthquake, and the combination of both hazards. The model considers two key traffic performance characteristics: postdisaster reduced road capacity and hourly variations in travel demand. Overall, several areas in the region are found to be currently severely at risk, thus providing direct information that can help authorities test the effectiveness of future road infrastructure projects.

In the aftermath of an earthquake, structural damage and debris represent major obstructions to the mobility of evacuees and first responders and to normal economic activities. Few studies have integrated the performance of urban road networks with obstructions from structural damage and its impact on systemic vulnerability toward evacuation and emergency response operations. In this study, procedures to evaluate some of the impacts of debris have been proposed. A probabilistic model is developed and validated to estimate roadside debris generated during an earthquake and to evaluate the impact on systemic vulnerability. LaSalle, a borough of Montreal city, is taken as a case study. Systemic vulnerability of transportation networks and emergency facilities depends on the level of traffic in the road network, roadway capacity, proximity, robustness and redundancy of the emergency facilities, and the vulnerability of the built environment. The results from this analysis can be used to identify critical components and prioritize retrofits to the road network and the localization of emergency facilities and to prepare alternate emergency response plans.

The build–own–operate–transfer (BOOT) scheme is widely used for the provision of new bulk water supply. However, this scheme is complex and carries significant financial risks because of the characteristics of the water sector and the involvement of public-private stakeholders with new and extended responsibilities, large private capital, and long contract duration. Drawing on the Nungua Seawater Desalination Plant (NSDP) in Ghana, this study seeks to identify and assess the critical financial risks associated with BOOT water supply projects and evaluate the financial risk level of the NSDP project. The risks and their relative criticality on the NSDP project are investigated by using a questionnaire survey method. The questionnaire was formulated with a set of 18 risks derived from extant literature and project documentation. Perceived critical financial risks affecting the NSDP project were assessed by a team of experts who had direct involvement in the project. A fuzzy synthetic evaluation suggests that the project is financially risky and that all the risks are critical to the project. Bankruptcy of consortium members, unfavorable economy of the host country, uncertainty in tariff adjustment of water products, rate of return restrictions, and availability problem of private capital are the five most highly-ranked risks. The fuzzy technique is used to represent and model experiential knowledge of the survey participants and to address the fuzziness of their expert judgments. The study’s results facilitate prioritization of risks and a comprehensive risk management program during the lifecycle of the case project and future projects. The fuzzy technique is suitable for early phases of BOOT projects to prioritize the risks that require a detailed analysis and to predict the risk level of a project.

This paper addresses two areas of inquiry: (1) critical infrastructure resilience as recovery and (2) infrastructure interdependencies and dependencies, and both of these areas are evaluated in the context of selected extreme weather events (involving water and wind) related to climate change. The purpose is to address how interdependencies and dependencies among infrastructures relate to resilience in terms of affecting recovery time. A framework for resilient infrastructure in the absence of interdependencies and dependencies from previous research is presented first to understand how negative effects of introducing these relationships may escalate and, in some cases, increase recovery time and complexity, thereby reducing resilience when these extreme conditions occur. Then, resilience and interdependence are combined in the form of a conceptual model to analyze the additional complexity that interdependencies pose for resilience. The model is on the basis of scenarios that portray the relevance of infrastructure relationships in actual types of extreme events, primarily focusing on electric power, transportation, and water infrastructure. This work is relevant to decision makers, planners and operators across a broad spectrum of infrastructure services to support their efforts to reduce adverse consequences of interdependencies and dependencies among these services. It also supports the research base for these activities, for example, for the development and application of models.

To promote the resilience and protection of infrastructure assets from an all-hazards perspective, this paper describes the progress of interdependencies modeling and integration efforts to anticipate cascading failures among critical infrastructure systems. A data-centric architecture is adopted that integrates existing and proven infrastructure models used for impact assessment analyses to aid and enhance the design of resilient infrastructure systems. The assessment framework is applicable to all types of critical infrastructure and permits (1) the integration and automation of the assessment process, including threat and hazard identification and data acquisition; (2) the estimation and projection of impact zones; (3) the simulation of the initial effects on infrastructure assets resulting from an initiating disruptive event; (4) the evaluation of propagating effects within each infrastructure system; and (5) the simulation of the influence of cascading failures across infrastructure systems. The paper presents the application of the framework to integrate two proven energy models—EPfast, for electric power, and NGfast, for natural gas—to anticipate regional and local cascading failures, and design resilient energy systems. Two state-level case studies illustrate the approach in simulating the propagation of disruptions between the natural gas and electric power systems.

Monitoring and maintaining a system of degradable infrastructure facilities imposes considerable costs on transportation agencies. To find the optimal timing and type of maintenance, rehabilitation, and reconstruction (MR&R) activities, Markovian models have been widely used, usually under the assumptions of fixed inspection schedule and error-free measurements. For a system of infrastructure facilities, identifying facility-specific MR&R activities is challenging and has garnered attention only recently. This paper presents a unifying framework that seeks the optimal spatial and temporal distributions of inspection and MR&R (IMR&R) decisions for a system of infrastructure facilities in the presence of measurement uncertainty. The framework is numerically shown to generate aggregate system-level results which are consistent with the findings in the previous research. At the disaggregate level, however, it is found that the timing and type of IMR&R policies for some facilities will impose facility-level costs that do not follow the increasing trend of aggregate system-level costs as the inspection measurement error increases. This highlights the need for adopting the facility-specific approach when planning IMR&R policies for a system of infrastructure facilities.

Sidewalks constitute the main guideway for the walking mode of transport and serve as a fundamental infrastructure for personal travel including commuting. This is because practically every motorized trip is preceded or concluded by nonmotorized travel. The assessment of sidewalk performance, in the sense of its suitability for walking, involves consideration of multiple aspects, whose precise treatment requires in turn the use of multicriteria methods to support decisions. This article proposes a multicriteria methodology for this purpose, thus setting the stage for a subsequent agency decision regarding maintenance strategy development. The methodology is based on a set of infrastructure attributes, directly intervenable by these authorities, and uses the ELECTRE TRI method to assign sidewalks under study to performance classes. It is practical to use and can be applied to any city, at any scale. The approach is thoroughly discussed, and demonstrated for a case study comprising several sidewalks in the city of Coimbra, Portugal. The results indicate that a considerable fraction of these sidewalks are in mediocre condition, and the multicriteria classifying methodology readily suggested intervention strategies, effectively aiding in the decision making.

This study identifies the factors influencing the occurrence of graffiti on highway infrastructure, specifically bridges and soundwalls in Nevada. Using data from the Las Vegas area, linear regression models were developed to link the occurrence of graffiti (in terms of graffiti size) to highway infrastructure locations and the presence of graffiti countermeasures. Bridges at high-profile freeways are found to experience more graffiti compared with bridges at low-profile freeways. Infrastructure located in commercial areas or low income residential communities is not found to influence the intensity of graffiti occurrence at bridges. Fencing is found to be an effective measure to prevent graffiti occurrence. Soundwalls next to high profile freeways are found to have more graffiti compared with those at low-profile freeways. Graffiti appears in clusters; graffiti locations at soundwalls may not be proportional to the length of the soundwalls. Soundwalls in residential areas tend to have more graffiti than those in commercial areas. The developed models can be used to predict the locations that are more likely to suffer from graffiti problems and the amount of graffiti to occur. Agencies can use the results of this study to develop appropriate countermeasures to combat graffiti problems.

In a period of increasingly constrained budgets, traffic engineers must develop more cost-effective maintenance strategies for traffic signal systems. Existing literature suggests that many agencies lack methods to systematically identify signal maintenance opportunities, and even fewer measure the impact of maintenance activities. This paper presents a method of using high-resolution signal controller data to identify intersections with failed inductive loop detection, which can result in substantial misallocation of green times and unnecessary vehicle delay. An application of this methodology is explored on a major urban arterial, with the resulting repairs translating to approximately $900,000 in annual user benefits. The results presented in this paper are easily implementable, given the increasing adoption of high-resolution signal controller data. Such methods can also be used to more clearly communicate maintenance success stories with the general public and elected officials.

This paper is a part of an expansive research work aimed at assessing 82 weathering steel overhead sign structures (WSOSSs) in the Charleston Interstate System in West Virginia. A total of 26 comprehensive inspection forms were developed to objectively evaluate the current condition of 11 general types of sign structures. This article focuses on analyzing 25 single-armed and double-armed ground-mount-cantilever WSOSSs (GMC-WSOSSs). A reliable ultrasonic testing technique was used to examine key components of the sign structures while the other components were inspected by visual inspection technique. A rating methodology was developed to evaluate the sign structures at both the element level and their overall condition. The element condition was rated based on the developed rating criteria and score. The overall condition of each sign structure was then evaluated by the ratio between the total score of each structure ($S$) and its maximum possible total score ($S_{\max }$). As a result of this work, it is concluded that all the GMC-WSOSSs performed relatively well after more than 40 years of service and exposure to moist weather condition of Kanawha County (climate zone 4A). Specifically, as a result of the developed rating methodology, 52% of sign structures were found to be in fair condition and 48% were in good condition. The rating system is intended to assist the West Virginia Department of Transportation in making rational decisions about whether there is a need to repair or replace at-risk elements, connections, or structures.

This study determines the most critically vulnerable major roadways in Norfolk and Virginia Beach, Virginia. Sea-level rise predictions are combined with the mean higher high water and 99% tidal datums and storm surge predictions to project flood water surface elevations through the year 2100. LiDAR data are used to compare major roadway elevations with the projected flood water elevations to determine which roadway segments would be flooded under different scenarios. Traffic data are used to determine critical road segments in the region (heavily traveled and low elevation). Results suggest that by the year 2100, and assuming intermediate sea-level predictions, approximately 10% of major roadways will regularly flood at high tide and 15% will flood at the 99% tide; this increases to more than 65% given a 100-year storm surge event. Five critical road segments are identified that would recurrently flood at high tide by 2100. These road segments should be the focus of infrastructure investments to improve the resiliency of the transportation network within the cities.

How to select decision-making schemes is a major concern in large-scale infrastructure projects. This paper proposes a new approach for this selection problem, which integrates collaborative information with individual information focusing on the linguistic variables of experts. It also applies an extended fuzzy technique to order preference by similarity to ideal solution (TOPSIS), to determine the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS), and to rank the order of all alternative schemes through calculating the relative closeness of each alternative scheme to the FPIS and FNIS. A case study of Hong Kong-Zhuhai-Macao-Bridge (HZMB) project in China is used to demonstrate how to apply this approach and what its advantages are. Compared to the existing methods, this proposed method can produce reasonable solutions for real-world practice.