Journal of Infrastructure Systems

This paper presents a framework for probabilistic sustainability design of reinforced-concrete transportation infrastructure incorporating optimization of maintenance. The framework contains three components: (1) stochastic service life models predicting the deterioration of structural components and repair timing; (2) stochastic lifecycle impact assessment (LCIA) models that estimate the sustainability impacts from construction and maintenance activities; and (3) a lifecycle optimization model (LCO) automating the repair and limit-state selection process to minimize the sustainability impact. In this study, the LCO model is constructed by using a dynamic programming method along with Monte Carlo simulation. A case study is presented in which the authors compute the probability that a maintenance scheme allowing more degradation will meet the ${\mathrm{CO}}_2$ equivalent (${\mathrm{CO}}_2\text{-}\mathrm{eq}$) emission reduction target of the United Nations Intergovernmental Panel on Climate Change (IPCC) by 2050. By integrating lifecycle optimization with sustainability design, the proposed framework removes the arbitrary nature of repair and limit-state selection. It also provides a formal method to evaluate and compare different maintenance designs of reinforced-concrete transportation infrastructure toward a higher degree of sustainable development.

Indirect consequences to shocks and stresses are mounting globally. Estimating and developing plans to treat these risks remains a challenge. The difficulty is determining ahead of time how the losses, and the potential resolutions, might propagate through the interactions of complex connected systems of systems when generally only high-level temporal statistics are available. In this paper, a novel graph model coupled with a maximum entropy likelihood estimator are proposed to map the complex interactions and to assess the statistics resulting from different initiating scenarios. The method involves defining initial conditions from all-hazards effects, followed by generation of event sequences from the high-level statistics, to estimate probabilities of different outcomes after a shock or stress. In this way, a general method is devised to assess indirect consequence in operational loss terms and to assess the merits of risk treatment options. Incorporating these indirect consequence calculations with direct consequence assessments provides a way to support a balance of treatment of these forms of consequence in a multiobjective fashion.

Scouring around foundations is the most common cause of bridge failures. Scour risk assessment tools allow engineers to better quantify risks associated with existing bridge infrastructure. However, the majority of these tools, including HYRISK, do not incorporate soil property information. This paper develops an approach that associates the critical shear stress of a soil to its Unified Soil Classification System (USCS) soil group classification. This approach is applied to approximately 40 bridges in Georgia using HYRISK and shows how incorporating soil property information changes the expected financial losses associated with a bridge failure. This is important because it enables engineers to use soil property information that is commonly found on boring logs to direct limited maintenance funds to those bridges that are most at risk to scour failure.

Public-private partnerships (PPPs) have been extensively used to deliver an array of infrastructure projects in Australia; they have become an integral part of several state governments’ procurement strategies. Over the last decade, however, several major PPP projects have incurred significant cost and schedule overruns, which have marred their use as a mechanism for delivering much-needed infrastructure. The paucity of performance measurement frameworks has contributed to the difficulty of proactively managing the effective and efficient delivery of PPPs, particularly during operations and maintenance. With this in mind, a lifecycle and stakeholder-oriented performance measurement framework (PMF) is conceptualized within the context of social infrastructure PPPs. The opinions of 160 practitioners about the proposed measures and their core indicators were solicited using a questionnaire survey. Confirmatory factor analysis was used to test the significance of proposed measures and their relevant core indicators (CIs) for measuring the performance of PPP projects throughout their lifecycle. The analysis reveals that the proposed PMF is a reliable and robust tool that can be used to assist with ensuring that social infrastructure PPPs can be delivered successfully throughout each phase of their lifecycle. The developed PMF provides governments and private consortiums with a robust and practical tool to measure and control the performance of their future projects.

Disaster resilience and serviceability of transportation networks are highly influenced by the post-event state and functionality of their bridges. However, the evaluation of the functionality of bridges in the aftermath of extreme events and particularly during the restoration process has not been subjected to extensive studies in the past, due to several difficulties, such as the uncertainties involved in the restoration and decision-making processes. To this end, this paper presents a new framework to estimate probabilistic restoration functions for damaged bridges. The restoration schedule of the bridge is obtained through a mathematical optimization, given the restoration tasks of its components, as well as a number of logical practical construction considerations and logistic constraints. The residual functionality of the bridge is obtained from the restoration schedule by taking into account a number of safety- and construction-induced traffic disruptions during the repair process. A simulation-based technique is proposed to compute the probabilistic characteristics of the functionality. The application of the proposed methodology is presented for the case of a multispan simply supported (MSSS) steel girder bridge numerically analyzed for a seismic event scenario.

The leading source of weather-related deaths in the United States is heat, and future projections show that the frequency, duration, and intensity of heat events will increase in the Southwest. Presently, there is a dearth of knowledge about how infrastructure may perform during heat waves or could contribute to social vulnerability. To understand how buildings perform in heat and potentially stress people, indoor air temperature changes when air conditioning is inaccessible are modeled for building archetypes in Los Angeles, California, and Phoenix, Arizona, when air conditioning is inaccessible is estimated. An energy simulation model is used to estimate how quickly indoor air temperature changes when building archetypes are exposed to extreme heat. Building age and geometry (which together determine the building envelope material composition) are found to be the strongest indicators of thermal envelope performance. Older neighborhoods in Los Angeles and Phoenix (often more centrally located in the metropolitan areas) are found to contain the buildings whose interiors warm the fastest, raising particular concern because these regions are also forecast to experience temperature increases. To combat infrastructure vulnerability and provide heat refuge for residents, incentives should be adopted to strategically retrofit buildings where both socially vulnerable populations reside and increasing temperatures are forecast.

The nation’s bridges are critical assets that facilitate travel and commerce, making them important components of economic growth. However, bridge repair and rehabilitation needs far exceed the resources available to maintain them. Prediction of future bridge network conditions is essential to bridge asset management because it provides valuable information for budgeting and medium- to long-term planning. This paper focuses on the use of tensor factorization, an advanced data analysis approach, as a tool that can be used for both exploratory analysis and prediction of network-level performance of bridges in states across the United States. The network-level performance indicators in this work are defined as a percentage of bridges that are structurally deficient, functionally obsolete, or both. These indicators are calculated based on bridge areas and counts. The tensor decomposition approach decomposes multidimensional data into lower-order forms while preserving variation over time. Prediction of future bridge network conditions was done using the compressed time factor matrix, which yielded mean absolute error (MAE) values indicating promise for this new approach.

Cost-leveling models have been introduced recently to minimize the fluctuations in annual maintenance, repair, and rehabilitation (MRR) costs within a multiyear infrastructure asset-management plan. However, these models were found to produce limited results because they ignored the impacts on all the preceding/succeeding MRR activities in a multiyear plan when the initially identified implementation time of one MRR activity was adjusted for cost-leveling purposes. This paper proposes a method to generate multiple MRR solutions at the facility level for existing cost-leveling models at the network level. The method utilizes two concepts as follows: (1) the fast elitist nondominated sorting genetic algorithm (NSGA-II) to generate a Pareto optimal set of feasible MRR alternatives, and (2) the pseudoweight vector approach with the Euclidean distance and a confidence interval to select multiple MRR solutions from the Pareto optimal set identified by NSGA-II. The proposed method is applied to a sample concrete bridge deck as a case study in this paper; the results show that the proposed method was able to provide multiple MRR solutions having various profiles in terms of the intervention times of the MRR activities and annual MRR costs. Consequently, the proposed method advances the theoretical framework of existing cost-leveling models by combining four approaches to provide multiple MRR solutions at the facility level: (1) NSGA-II algorithm, (2) pseudoweight vector approach, (3) Euclidean distance, and (4) a confidence interval. In addition, it is expected that the proposed method can be well applied to existing multiobjective optimization models to generate a set of nondominated good policies for infrastructure asset management.

This paper proposes an approach for developing optimal risk-based management strategies for bridges in coastal regions. Currently, bridge management strategies in these regions are designed to limit the negative effects corrosion has on bridge performance under traffic loads. However, recent large-scale hurricanes and their associated damage to bridges have demonstrated the need to consider hurricanes when making bridge management decisions. This paper aims to develop optimal risk-based management strategies that include both repair actions to address deteriorating performance under traffic loads and retrofit actions to minimize the potential for failure during hurricanes. The proposed approach considers the uncertainties associated with hazards, and the economic, social, and environmental consequences of failure under traffic loads and hurricanes. The optimal management strategies are achieved by formulating and solving a multiobjective optimization problem. The objectives of this optimization problem include minimizing the lifecycle costs accrued due to repair and retrofit actions and minimizing the maximum risk over the lifecycle of the bridge. The proposed risk-based approach is applied to a steel girder bridge located in a hurricane-prone region.

Pavements heavily influence the management costs in highway networks. Operating pavements represents a challenging task involving complex decisions on the application of maintenance actions to keep them at a reasonable level of performance. The major difficulty in applying computational tools to support decision making lies in a large number of pavement sections as a result of a high length of road networks. This paper addresses maintenance scheduling for pavements by consolidating this task into two stages. In each stage, multiobjective optimization is used to optimize maintenance schedules. The main motivation is to obtain a computationally treatable model for large road networks. The first stage is defined by a collection of pavement sections composing the road network. In this stage, the performance and maintenance models are addressed. These models account for uncertainties in the future performance and effects of maintenance by defining model parameters as random variables. The second stage refers to combining maintenance schedules for individual sections to determine the optimal maintenance plan at the network level. The results obtained for the real road network demonstrate the validity and usefulness of the proposed framework. Moreover, this framework is general and can be extended to different types of infrastructure assets.

This paper presents a procedure to select explanatory variables, without the influence of human bias, for deterioration model development using publicly available data for bridges in Wyoming. Using this information, including geometric data and climate information, candidate explanatory variables can be converted into normalized numeric values and analyzed prior to the development of deterministic or stochastic deterioration models. The prevailing approach for explanatory variable selection for bridge condition modeling is to use expert opinions solicited from experienced engineers. This may introduce personal human influenced biases into the deterioration modeling process. A framework using least absolute shrinkage and selection operator (LASSO) penalized regression and covariance analysis are combined to compensate for this potential bias. The resultant of LASSO, combined with the cross validation, is used to develop a LASSO solution path that visualizes the relative significance of investigated explanatory variables. In order to analyze yearly inspected data in the framework, the cross-validation analysis is used as a standard for the selection of minimum number of explanatory variables using the solution path. The proposed method is demonstrated through the creation of deterministic deterioration models for deck, superstructure, and substructure for Wyoming bridges and compared to explanatory variables using the expert selection method. The comparison shows a significant decrease in error using the presented framework based on the least squares of relative error.

Managers of modern large-scale construction projects are under pressure to meet higher customer expectations with tighter budgets. Although they deal with numerous issues in the purchasing and manufacturing processes, selecting effective and efficient material suppliers is among the most critical one. This selection is often very challenging when lacking precise information. Moreover, the construction contractor and the suppliers often have conflicting interests and make decisions individually. As research concerning the aforementioned issues is still relatively scarce, this paper proposes a multiobjective bilevel programming model with random fuzzy coefficients for supplier selection problem with multiple items (SSP-MI) in a large-scale construction project. The upper level problem deals with the construction contractor who selects suppliers to minimize total cost, maximize service, and item quality. The lower level problem deals with the suppliers who allocate supplied items to maximize their own total profit. For solving this complex bilevel nonlinear model with uncertainties an expected value operator method is first used to deal with the uncertain variables, and then Karush-Kuhn-Tucker (KKT) conditions and a combinatorial algorithm with a sectional genetic algorithm with fuzzy logic controller (flc-SGA) and a weighted-sum method (WSM) based on satisfactory degree (SD) denoted as flc-SGA with SD-based WSM are proposed. Finally, the proposed approach is demonstrated to be effective when carried out in the Pubugou Hydropower construction project.

In order to minimize the costs, risks, and disruptions associated with structural inspections, robotic systems have increasingly been studied as an enhancement to current inspection practices. Combined with the increasing variety of commercially available robots, the last two decades have seen dramatic growth in the application of such systems. The use of these systems spans the breadth of civil infrastructure works, and the variety of implemented robotic systems is growing rapidly. However, the highly interdisciplinary nature of research in this field means that results are disseminated across a broad variety of publications. This review paper aggregates these studies in an effort to distill the state of the art in inspection robotics, as well as to assess outstanding challenges in the field and possibilities for the future. Overall, analysis of these studies illustrates that the design of inspection robots is often a case-specific compromise between competing needs for sophisticated inspection sensing and for flexible locomotion in challenging field environments. This review also points toward the growing use of robots as a platform to deploy advanced nondestructive evaluation (NDE) technologies, as well as the expanded use of commercially available robotic systems. Two key outstanding challenges for future researchers are suggested as well. The first is the need for more sophisticated, and inspection-driven, robot autonomy. The other is the need to process and manipulate the massive data sets that modern robots generate.

Predictions of the risk to built infrastructure posed by climate and land-use change have suggested that bridge collapses may increase due to more frequent or intense flooding. Assessments of the United States often assume that bridges may collapse when the 100-year flood (i.e., a flood with 1% annual frequency of exceedance) occurs, but this assumption has not been fully tested because of a lack of comprehensive collapse records. Thirty-five bridges for which a stream gauge on or near the bridge recorded the flow during total or partial collapse were identified and used to test this assumption. Flood frequency analyses, other statistical analyses, and structural reliability methods were used to quantify the return periods of collapse-inducing flows, identify trends linked to event and site characteristics, and evaluate the potential importance of collapse return period variability in assessing the impact of climate and land-use change on hydraulic collapse risk. The results indicate that the collapse-inducing flow return periods varied considerably (range: 1 to $>\mathrm{1,000}\text{years}$) and were frequently lower than values considered in many climate impact assessments: 23 of the 35 bridges were estimated to have collapsed during flows with return periods of lower than 100 years. Annual failure probabilities computed using the full distribution of return periods of the collapse-inducing flows, as opposed to central values (e.g., means), were more sensitive to an assumed increase or decrease in the underlying frequency of flooding. These results suggest that linking bridge collapse to only the 100-year flow does not capture significant variability associated with collapse return periods, potentially reducing sensitivity to flood frequency changes and reducing the robustness of assessments of the impact of climate, land-use, and streamflow-regulation change on hydraulic bridge collapse risk.

This paper presents a condition-based treatment methodology for a type of rail surface defect called squat. The proposed methodology is based on a set of robust and predictive fuzzy key performance indicators. A fuzzy Takagi–Sugeno interval model is used to predict squat evolution for different scenarios over a time horizon. Models including the effects of maintenance to treat squats, via either grinding or replacement of the rail, are also described. A railway track may contain a huge number of squats distributed in the rail surface with different levels of severity. This paper proposes to aggregate the local squat interval models into track-level performance indicators including the number and density of squats per track partition. To facilitate the analysis of the overall condition, a single fuzzy global performance indicator per track partition is proposed based on a fuzzy expert system that combines all the scenarios and predictions over time. The proposed methodology relies on the early detection of squats using axle box acceleration measurements. Real-life measurements from the Meppel-Leeuwarden track in the Dutch railway network are used to show the benefits of the proposed methodology. The use of robust and predictive fuzzy performance indicators facilitates the visualization of the track health condition and eases the maintenance decision process.

A field evaluation of an underwater tunnel was conducted using a variety of nondestructive testing (NDT) methods, including visual inspection, air- and ground-coupled ground-penetrating radar (GPR), ultrasonic tomography (UST), and impact echo (IE). An air-coupled GPR antenna was used along with visual inspection to identify areas of interest in the Chesapeake Channel Tunnel near Norfolk, Virginia. After potential regions were identified, a robotic scanner with pneumatically driven suction feet was used to automatically scan the area using ground-coupled GPR, UST, and IE. Regions were also evaluated using manually applied UST. These methods combined with powerful software presented the element under inspection as a 3D volume for thorough inspection. This study shows that this particular combination of NDT techniques can efficiently and effectively identify and locate reinforcement, backwall depth, and potentially delaminated areas. By analyzing the phase shift of ultrasonic waves, it is also shown that UST techniques can be used to potentially determine the condition of the backwall and reinforcement bonding.

A method is developed for presentation of a concrete bridge deck condition assessed by multiple nondestructive evaluation (NDE) technologies using a three-dimensional (3D) visualization program. Four types of NDE data are merged and visualized in the program: (1) impact echo for mapping and describing the severity level of concrete delamination; (2) ultrasonic surface wave for concrete quality (elastic modulus) assessment; (3) electrical resistivity for estimating the corrosion rate of steel reinforcement; and (4) high-resolution imaging of a bridge deck surface for documenting signs of deterioration, previous repairs, and surface wear. The developed visualization platform integrates the four NDE data types and visualizes in a 3D space in a very intuitive way. As such, the program assists in understanding of the complex relationships of bridge deck conditions assessed by multiple NDE techniques. In addition, a correlation between external (surface cracks, wear, and previous repairs) and internal deterioration (delamination, concrete degradation, and corrosion) can be studied and visually identified utilizing the developed 3D visualization program.

Visual testing, as one of the oldest methods for nondestructive testing (NDT), plays a large role in the inspection of civil infrastructure. As NDT has evolved, more quantitative techniques have emerged such as vibration analysis. New computer vision techniques for analyzing the small motions in videos, collectively called motion magnification, have been recently developed, allowing quantitative measurement of the vibration behavior of structures from videos. Video cameras offer the benefit of long range measurement and can collect a large amount of data at once because each pixel is effectively a sensor. This paper presents a video camera-based vibration measurement methodology for civil infrastructure. As a proof of concept, measurements are made of an antenna tower on top of the Green Building on the campus of the Massachusetts Institute of Technology (MIT) from a distance of over 175 m, and the resonant frequency of the antenna tower on the roof is identified with an amplitude of 0.21 mm, which was less than $1/170$th of a pixel. Methods for improving the noise floor of the measurement are discussed, especially for motion compensation and the effects of video downsampling, and suggestions are given for implementing the methodology into a structural health monitoring (SHM) scheme for existing and new structures.

Intelligent transportation systems (ITS) assets provide the opportunity to improve the efficacy of the current transportation infrastructure. However, transportation agencies face continuous challenges in planning and managing ITS assets. The focus of this research is on improving the planning of traffic-signal control technology as an important ITS subsystem. To this end, this research presents a decision support system (DSS) for evaluation of controller software assets. The proposed DSS consists of four components, namely knowledge, model, dialog, and database management. The core of the DSS is a fuzzy analytic hierarchy process methodology, relying on expert-knowledge acquisition. The proposed DSS is implemented on a case study for the Virginia Department of Transportation. The results indicate that the experts and the integrated DSS play a major role in obtaining a precise and consistent evaluation of signal controller software alternatives. Moreover, the developed decision support methodology is transferable among agencies. Finally, the proposed DSS provides a framework for further development of evaluation methods for other ITS assets.