Journal of Infrastructure Systems

Decision making for managing risks to critical infrastructure systems requires accounting for (1) the uncertain behavior of disruptive events; and (2) the interdependent nature of such systems that lead to large-scale inoperability. This paper integrates a dynamic risk-based interdependency model, the dynamic inoperability input-output model, with a multiobjective decision tree to analyze preparedness decisions. The use of a dynamic model allows for resilience and recovery decisions to be incorporated in the decision-making framework, and uncertainty is accounted for using probability distributions. The multiobjective inoperability decision tree is applied to the study of transportation infrastructure disruptions, namely closures of an inland waterway and an inland waterway port. A data-driven multiregional study of the Port of Catoosa in Oklahoma, along the Mississippi River Navigation System, is discussed and suggests careful consideration when investing larger amounts toward port security.

Pavement management relies on the evaluation of the condition of pavement at different times during the life of the structure. The combination of condition indicators and the knowledge of how they are used in any pavement condition model is fundamental for pavement rating. To handle complexity and information redundancy, this paper proposes the principal-component analysis (PCA) to evaluate the relative importance of different types of distresses on the condition assessment for flexible pavements, and to use relevant condition features to establish criteria for pavement management. The important outcomes in applying the PCA approach were: even with the dimensionality reduction dictated by the variance, there was limited loss of information with regard to the section condition that did not affect the overall objective of pavement management; also, information redundancy was minimized.

This paper presents a new algorithm for automated crack detection in sewer inspection closed-circuit television (CCTV) images. Cracks often have a long and thin rectangular shape with a darker appearance relative to other components in the image; therefore, they typically manifest as edges. The proposed algorithm exploits previous information on the visual characteristics of crack features in typical CCTV images to efficiently identify actual cracks and filter out background noise. The algorithm consists of three main steps. The first preprocessing step prepares the CCTV image for crack detection by identifying a set of candidate crack fragments using the Sobel method to detect horizontal and vertical edges separately. The Hough transform is then used to identify and remove the edges associated with information labels typically found in CCTV images. The second step applies a set of morphological operations to enhance candidate crack segments by filling the gaps between closely adjacent and aligned edges. The enhancement step results in merging crack fragments that potentially represent segments of the same crack curve. In the third step, two filters are defined based on previous knowledge of the visual characteristics of cracks, and then applied to remove noise edges and extract a set of real crack segments. We tested the proposed algorithm on a set of CCTV videos obtained from the cities of Regina and Calgary in Canada. The experimental results demonstrated the efficiency of the proposed algorithm, and showed its robustness in detecting various patterns of sewer cracks.

The objective of this research was to integrate current data sources to develop a methodology for assessing and mitigating the potential impacts of sea level rise (SLR) on Florida’s transportation infrastructure to assist transportation planning. The proposed approach integrates the Florida Department of Transportation (FDOT) information system with existing topographical and geological data to facilitate (1) the evaluation of current and projected SLR impacts on Florida’s coastline and low-lying terrain areas, and (2) the identification of the physical transportation infrastructure that is most likely to be affected by frequent to continuous flooding because of SLR so that solutions could be sought. The projection of SLR, and the timing for the same, was outlined using a benchmark approach that brackets time intervals as opposed to specific timing for improvements. Further research to evaluate the impact of sea level rise on ponding and storm surge is a future, more difficult area of investigation.

Successful management of deteriorating structures requires the reliable prediction of damage occurrence as well as the time-dependent damage propagation under uncertainty. The reliability of the performance prediction process can be significantly improved by integrating information gained from inspection and monitoring actions. This integration leads to a more accurate prediction of the time-dependent damage level and, eventually, to a better supported decision-making process. In this paper, a probabilistic approach is proposed to find an optimum management plan for fatigue-sensitive structures by integrating the available information from inspection actions. The proposed approach utilizes a probabilistic time-dependent damage criterion, inspection cost, and failure cost to find the optimum inspection times under uncertainty. New information resulting from inspection actions performed during the lifetime of the structure is used to update the damage propagation parameters as well as the optimization procedure. This process results in an enhanced management plan which can provide managers the ability to make real-time decisions based on inspection results. The integration of this new information and its impact on the life-cycle management process are thoroughly investigated. In addition, a realistic fatigue critical detail is used to illustrate the proposed probabilistic approach.

Project life-cycle cost analysis (LCCA) is a reasonable approach to compare pavement design alternatives, but not taking into consideration of pavement performance in the most LCCA approaches may lead not to obtain the most desirable alternative for road users and associated highway agencies. This article introduces a multicriteria decision making method for pavement project evaluation using LCCA that (1) utilizes fuzzy set theory to model and handle uncertainties; (2) takes into account the extra user costs attributable to inadequate pavement condition; and (3) as a criterion, considers life-cycle pavement performance that describes overall pavement serviceability condition. Using a hypothetical project, the paper clarifies the performance of the proposed method for long-term evaluating alternative pavement design strategies. The alternatives are compared with respect to each criterion of the method, and also using sensitivity analysis, it is determined that changes in the decision maker’s risk and confidence attitudes can affect the ranking of the alternatives.

Localization of sensor nodes is one of the most essential tasks for sensor network applications for infrastructure monitoring. Normally, the accuracy of global positioning system (GPS) positioning installed in wireless sensor network is in the order of meters (which is insufficient for infrastructure monitoring). This paper developed a GPS wireless sensor network with a capability of onboard GPS analysis based on a relative positioning algorithm with the accuracy in the order of centimeters. However, employment of a high-performance and energy-hungry CPU is inevitable for this onboard GPS analysis method. To reduce the energy consumption, a sensor node called hybrid sensor node equipped with dual CPUs has been developed. A high-performance CPU on the hybrid sensor node is used only for onboard GPS analysis and another CPU with low energy consumption is used for other tasks such as controling of a GPS receiver and a wireless transceiver. This paper describes the design concept, the hardware, and the software of the newly developed hybrid sensor node. The overview of the sensor network system, task allocation, and the performance evaluation of the whole system work flow from the view point of energy consumption are also described in detail.

In recent years, the concepts of resilience and sustainability have become very topical and popular. The concept of sustainability rose to prominence in the late 1980s and became a central issue in world politics, when the construction industry began to generate the first sustainable building assessment systems with more or less equally weighted environmental, economic, and social aspects for office buildings over their life cycles. On the other hand, resilience is usually connected to the occurrence of extreme events during the life cycle of structures and infrastructures. In the last decade, it has been used to minimize specifically direct and indirect losses from hazards through enhanced resistance and robustness to extreme events, as well as more effective recovery strategies. A detailed comparison of the studies dealing with either infrastructure sustainability or resilience presented in this paper leads to the conclusion that they have a vast number of similarities and common characteristics. For instance, they both combine structural analyses with social and economic aspects; they both rely on techniques for the life-cycle analysis and decision making; they both are in an early stage, where the academic world is trying to find the best way to promote the application of the scientific results among professional engineers and the industry. Indeed, both approaches try to optimize a system, such as a civil infrastructure system, with respect to structural design, utilized material, maintenance plans, management strategies, and impacts on the society. However, for the most part, researchers and practitioners focusing on either resilience or sustainability operate without a mutual consideration of the findings, which leads to a severe inefficiency. Therefore, this paper suggests that resilience and sustainability are complementary and should be used in an integrated perspective. In particular, the proposed approach is rooted in the well-established framework of risk assessment. The impact of the infrastructure and its service states on the society in normal operational conditions (assessed by sustainability analysis) and after exceptional events (assessed by resilience analysis) should be weighted by the associated probabilities of occurrence and combined in a global impact assessment. The proposed perspective and assessment technique is applicable to various types of civil infrastructure systems, but the case of transportation networks and bridge systems is emphasized herein. A numerical application dealing with the comparative analysis of two possible bridge layouts is presented to exemplify the approach. The results show that both resilience and sustainability analyses assess a relevant amount of the impact of the bridge on the community where it is built, so neither one can be neglected.

A clusterwise linear regression model of pavement deterioration is presented. The model provides a framework to simultaneously segment a population and to describe performance with a set of regression models, one for each segment. Instead of relying on observed criteria, the objective in the segmentation is to maximize within-segment variation explained by a set of commonly specified regression models. To illustrate the methodology, performance models were estimated for a panel of 131 pavements from the American Association of State Highway Officials (AASHO) road test. Pavements in different segments display systematic but unobserved differences in their responses, i.e., unobserved heterogeneity, which manifests itself in segment-level coefficients that differ in their magnitude and sign. This is radically different than other approaches in the literature that explain such differences with individual-level error/intercept terms, but that rely on the assumption of constant and homogeneous coefficients capturing the effect of explanatory variables across the population. How segment-level effects can be used to support tailored management policies, e.g., maintenance and repair, or setting weight restrictions is discussed. Finally, a rigorous assessment is conducted of the proposed model that includes comparison with a population-level regression model and to a clusterwise model that relies on observed factors from the original experimental design: the loop-lane, i.e., the design-loading, configuration.

Ensuring a transportation system’s operational efficiency and effectiveness is a challenge particularly for road maintenance agencies in winter times. Winter road maintenance involves snow plowing and chemicals/abrasives spreading, and both are performed by using a number of specific types of trucks. However, most departments of transportation do not maintain sufficient trucks and hence they may resort to contract additional equipment to make up for the shortfall. Based on the Newsvendor problem, this study develops a novel stochastic model to optimize the number of plows that should be contracted prior to a snow season to minimize the total cost, considering the frequency and distribution of various intensities of snow events, geometric condition of the roadways and traffic speeds. A solution methodology based on stochastic modeling is proposed and applied to a numerical example utilizing actual snow data. Additionally, a sensitivity analysis is performed to evaluate the impact of model parameters on the optimum solution.

Various energy regulations including corporate average fuel economy (CAFE) are forcing auto manufacturers and auto distributors to improve the fuel efficiency of vehicles significantly over the next decade. Conversely, improved automotive technologies and higher gasoline fuel prices enable better alternative-fuel vehicles (AFV), including hybrid vehicles (HV) and electric cars. This research evaluates the impacts of CAFE regulations in automobile and light truck fleet fuel economies, and effects of HV and AFV in the fleet, on gasoline-tax-based revenues. The new sales survivability (NSS) model, along with new vehicle sales data and vehicle survivability data from 1980 to 2005, were used to estimate the fleet mix, vehicle miles traveled (VMT) by fleet mix, and revenue projections through the year 2025. Five scenarios were considered to analyze the impacts of CAFE regulations and introduction of HV and AFV on fuel consumption and fuel-tax-based revenues for highway revenues. The results show that the CAFE regulation alone is expected to reduce the fuel-tax-based revenue by more than 21% in the year 2025. Similarly, the introduction of HV and AFV along with CAFE regulations is expected to reduce fuel-tax-based revenues by more than 31 and 37%, respectively, in the year 2025. This paper shows the significance of considering the effects of new regulations and technological improvements in estimating future fuel-based revenues. The revenue estimated in this paper is much lower than the estimates developed by previous studies. This shows that modifications to the existing system of highway financing would be required much earlier than anticipated.

This paper assesses the toll road concession program started by the federal government of Brazil, from its beginning in 1996 until 2012. Brazil has one of the longest road networks, under private concessions, in the entire world. The authors use the case study approach, based on the available information, to analyze the evolution of the program over the course of its history. Specifically, a detailed analysis is conducted from three perspectives: those of the competitiveness of the tender, the impact of toll modifications caused by the rebalancing of the economics of the contract, and the actual evolution of the traffic flows as compared to their estimates, respectively. It is found that the most recent auctions have been very competitive. Moreover, with the original toll roads, bidders relied on early traffic forecasts that turned out to be overly optimistic, and over the years, contract rebalancing has led to large increases in toll rates. Overall, and even though Brazil’s program can be considered successful in many ways, there is still considerable room for improvement.

This case study discusses post-event reconnaissance research of the September 8, 2011, power outage that left San Diego County, California, without electricity for up to 12 h. The objective of this case study is to synthesize and analyze the impacts of the outage and responses to the event. Understanding the outage’s impacts and responses helps to reveal restoration practices and contexts that promote meeting both technical and nontechnical goals. This study reveals several issues related to restoration decision-making and communication related to critical customers, particularly those responsible for health care, wastewater and potable water management, fuel provision, and food service. Restoration did not occur and was not communicated in such a way to avoid impacts to dependent critical infrastructure, reflect state restoration criteria, or meet expectations of a variety of power customers. Insight from this case study suggests three themes to guide research and development of best practices for power restoration: (1) emergency preparedness and public information, (2) power continuity, and (3) protocols and planning. Assessment of these themes are put in context with other studies of contemporary power outages and restoration to argue that power restoration protocols, policy, and planning are an important area for interdisciplinary research.