Journal of Infrastructure Systems

The transportation system infrastructure in the United States is vital to sustaining the nation’s economy, safety, and well-being. In its recent special report, Potential Impacts of Climate Change on U.S. Transportation, the National Research Council concluded that climate change will have significant impacts on transportation systems, affecting design, construction, operation, and maintenance. Furthermore, the report calls for a focus on the problem now through adaptation planning to avoid costly future investments and disruptions to operations. This begins with sharing information, resources, best practices, and lessons learned across jurisdictional lines and among different stakeholders. With this in mind, a select group of stakeholders representing the freight-transportation industry and climate science participated in a summit held at Vanderbilt University in 2011, to reach consensus on the needs and challenges that must be addressed in order for successful adaptation strategies to emerge. This paper presents a brief review of the relevant literature on this topic, shares the outcomes and findings of summit deliberations, and provides recommendations for a path forward.

To meet greenhouse gas (GHG) emissions for the transportation sector, the United States is expected to expand infrastructure for producing and distributing lignocellulosic biofuels over the next decade. To compare the life-cycle GHG footprint of biofuels to the petroleum baseline, emissions associated with feedstock and fuel handling, storage, and transportation must be included. U.S.-specific life-cycle GHG emission factors were developed for each major distribution chain activity by applying a hybrid life-cycle assessment methodology to the construction, manufacturing, operation and maintenance of each component. A projection was then made for the fleet of infrastructure components necessary to distribute 21 billion gal. (79.5 billion L) of ethanol derived entirely from Miscanthus grass for comparison to the baseline petroleum system. Owing to geographic, physical, and chemical properties of biomass and alcohols, the distribution system for Miscanthus-based ethanol is more capital- and energy-intensive than petroleum per unit of fuel energy delivered and was estimated to be over five times more GHG-intensive than petroleum (i.e., 17—18 versus $3.2\mathrm{g}{\mathrm{CO}}_2\text{-}\mathrm{e}/\mathrm{MJ}$ of consumed fuel, ignoring feedstock production and conversion). Lower life-cycle emissions could be attained by employing more efficient and durable equipment and vehicles; reducing material losses; minimizing feedstock delivery, biofuel delivery, and consumer refueling errand distances; and producing more energy-dense biofuels than ethanol.

Transportation corridors at regional and national scales are vulnerable to region-wide natural disasters resulting from tropical cyclones, including tropical storms and hurricanes. In addition to floods and debris causing the corridors to be impassable, the widespread damage to signs, lights, and signals brings severe safety concerns to the system users. Agencies must prepare for tropical cyclone-related needs and anticipate various storm scenarios in order to prioritize their investment for upgrading existing equipment. This paper develops a method to consider investment alternatives for highway equipment (i.e., signs, signals, and lights) through the evaluation of trade-offs between cost and damage. First, the authors develop a model for determining wind damage to vulnerable equipment. Second, they characterize the types of highway equipment and associated costs. Third, they evaluate trade-offs among upfront equipment upgrade costs and potential wind damage to equipment in reference to particular tropical cyclone scenarios. Considering the various consequences of five hurricane categories enables the decision maker an opportunity to understand the trade-offs and to invest in equipment consistent with individual needs and values of the agency. To demonstrate the method for this paper, the authors use a study area consisting of 209 km (130 mi) of interstate highways and 309 km (190 mi) of U.S. and state routes managed by the Virginia Dept. of Transportation (VDOT). This paper is of interest to emergency managers, regulators, transportation agencies, and others.

The performance of structures and infrastructure under natural hazards can have a significant impact on the sustainability of the system, often characterized in terms of environmental, economic, or social indicators of performance. This paper commences with a brief review of the relation of natural hazard performance to sustainability and an assessment of bridge infrastructure sustainability with an emphasis on environmental indicators. A framework for life cycle sustainability analysis (LCS-A) is then posed that elucidates the role of natural hazard risks when evaluating sustainable bridge performance using risk-based indicators of environmental sustainability, including embodied energy and carbon dioxide (${\mathrm{CO}}_2$) emissions. Its application provides insight on the sustainability of mitigating damage from natural hazards through retrofitting deficient structures, considering uncertainty in the hazard occurrence, structural performance, and repair or reconstruction actions that affect energy expenditure and emissions. A case study is presented to evaluate indicators of environmental sustainability for individual bridges and for a regional portfolio of bridges susceptible to aging and seismic hazards. The results show the significant impact that retrofit can have on reducing the expected value of emissions and embodied energy from lifetime hazard exposure.

Millions of culverts exist in the United States, and they are aging rapidly. Inspection of all the culverts consumes a lot of time and resources. Instead of inspecting each culvert every 5 years, this study presents a more intelligent approach to predict the condition of each culvert. An artificial neural network (ANN) model is built to assess the condition of the culverts based on culvert inventory data. The overall condition-rating predictions are compared with the condition rating based on manual inspection. The results of this study have shown that ANN was able to predict culvert adjusted overall rating with high precision, as the course of action score prediction rate was 100%. Sensitivity analysis of the ANN model is provided to assess the effect of variables. The goal of this study is to show that more intelligent culvert-management systems could be devised by taking advantage of artificial intelligence.

This paper presents an extension to a partially observable Markov decision process so that its solution can take into account, at the beginning of the planning, the possible availability of free information in future time periods. It is assumed that such information has a Bayesian network structure. The proposed approach requires a smaller computational effort than the classical approaches used to solve dynamic Bayesian networks. Furthermore, it allows the user to (1) take advantage of prior probability distributions of relevant random variables that do not necessarily have a direct causal relationship with the state of the system; and (2) rationally take into account the effects of accidental or rare events (such as seismic activities) that may occur during future time periods of the planning horizon. The methodology is illustrated through an example problem that concerns the optimization of inspection, maintenance, and rehabilitation strategies of road pavement over a 14-year planning horizon.

In bridge management practice, bridge condition is the fundamental information needed to allow decision makers to make well-informed decisions regarding preservation, rehabilitation, or replacement of a bridge or network of bridges. The National Bridge Inventory (NBI) condition ratings, collected since the early 1970s, and the Commonly Recognized (CoRe) element condition data, collected since the early 1990s, are two major sources of bridge condition data in the United States. General NBI condition ratings are utilized for performance assessment, performance reporting, resource allocation, and selection of bridge projects by all levels of government. Since the early 1990s, the bridge management community has been interested in an algorithm to predict the categorical NBI condition rating classes from the more quantitative and detailed CoRe element condition data. An algorithm with sufficient predictive accuracy would make only CoRe element inspections necessary and would provide time and resource savings. This paper presents a new methodology for this purpose, using classification and regression trees (CARTs). The CART analyses were conducted with the bridge condition data provided by three state transportation agencies, using data from 2006 to 2010. The statistical results point to a more accurate prediction method than the previous algorithms described in the literature.

Development of deterioration models for pavements is an essential part of maintenance and rehabilitation planning. Often, when pavement-deterioration models are developed for developing countries or states that do not have regularly scheduled condition surveys, the only available data are experimental. Experimental data sets fail to include environmental variables and cannot capture properly the aging process of pavements, so the estimated models suffer from biases. This paper describes the development of a pavement-crack-initiation model by combining experimental and field data to correct for such biases. The American Association of State Highway Officials (AASHO) Road Test is used as the experimental data set, which is combined with field data from the Washington State Department of Transportation. The two models are first estimated separately, correcting for endogeneity biases that may exist in the field model. Joint estimation is used next to quantify the bias in the experimental data set and estimate the parameters of the combined model. This study shows that joint estimation can lead to more robust crack-initiation models compared to those estimated separately by the two data sets.

The prediction of pipe failures in urban water systems is a complex process because the available failure records, originating in work orders, are often short and incomplete. To identify a robust and simple model with good failure prediction results using short data history, three existing models were compared in this study: the single-variate Poisson process, the Weibull accelerated lifetime model, and the linear-extended Yule process. This work also presents modifications to these models that enable them to produce more accurate predictions and overcome computational issues for practical software implementation. The three models, together with the improvements where applicable, were applied to water supply system data provided by a Portuguese water utility, and the results were comparatively analysed to assess the accuracy of each model. The Weibull accelerated lifetime model yielded the best results among the three models, accurately predicting failures and detecting pipes with high failure likelihood; however, it is based on Monte Carlo simulations, which can be time-consuming. The linear extended Yule process could also effectively detect pipes with higher failure likelihood; however, it presented a clear tendency to overestimate the number of future failures. The single-variate Poisson process is the simplest of the three models and produced failure prediction results of lower quality.

Reliable cost estimation of construction, operation, and maintenance (O&M) of water treatment plants is essential for project planning and design. The authors have developed construction and O&M cost models for different unit operations and processes involved in water treatment plants. These models are developed from historical cost data and can be used to develop preliminary cost estimates for major project components and to screen alternative process trains of a water treatment project during the planning phases of the project. The historical cost data were updated to April 2011 costs by using cost indexes from Engineering News Record (ENR) and Bureau of Labor Statistics (BLS), in addition to April 2011 costs of energy and labor. This paper presents the use of a single cost index to further update construction and O&M costs. This approach has significant advantages over using multiple indexes (such as ENR and BLS indexes) for different cost components. Using the method presented in this paper, future cost updating by ENR construction and building cost indexes becomes simple and straightforward. Actual bid prices for one new water treatment plant construction and two water treatment plant expansion projects were obtained from reputed consulting firms and compared to estimated costs by using the cost models presented in this paper. Further validation is recommended by use of the equations and comparisons of the costs to the detailed estimates and actual bids. Accurate forecasting of the costs of a project is site specific and cannot be generalized. Design plans and specifications are needed to develop accurate cost estimates based on equipment, materials, and labor.

Water distribution network models for large municipalities have tens of thousands of interconnecting pipes and junctions with complex hydraulic controls. Many water security applications, including sensor placement optimization, require detailed simulation of potential contamination incidents. The postsimulation optimization problem can easily exceed memory on standard desktop computers. Large networks can be skeletonized to reduce computation; however, this alters network hydraulics, and therefore sensor placement. The objective of this paper is to evaluate a two-tiered sensor placement approach that combines hydraulic and water quality simulations using all-pipes, or original, network models with subsequent geographic aggregation of time and impact values to reduce memory requirements. The two-tiered approach first places sensors on aggregated regions, then refines the solution to actual nodes in the original model. The two-tiered sensor placement approach is compared to results using the original network and skeletonized networks based on solution quality, memory use, and runtime. Results show that skeletonized networks introduce error in sensor placement. Two-tiered sensor placement using geographic aggregation replicates the original model solution to within 5% in most cases.

The water end-use segment (WES), consisting of activities that utilize water in homes and buildings, has been identified as a major component of energy use in the urban water supply system. In this paper, an analytical framework is presented which can be used at the planning stages of new urban developments to assess future building-level water demands and associated energy requirements. The framework is applied to Masdar City, a new urban area in the United Arab Emirates, which has been targeted in its design to be a future zero-carbon and zero-waste city. Results show that the energy intensity (in electric kWh) in WES for Masdar City may range from 2.6 to $4\mathrm{kWh}/{\mathrm{m}}^3$. The dominant use of energy in this segment is attributed to water heating requirements, and the total energy use for obtaining hot water is estimated to range from approximately 20 to 50 million kWh annually. It is found that the residential sector in the city can have the greatest impact in affecting energy requirements associated with water use. For every unit reduction (in $\mathrm{L}/\mathrm{person}/\mathrm{day}$) of indoor residential water use, it is estimated that up to 225 MWh may be saved annually.

Deterioration in cast iron mains manifests itself in the form of corrosion. External corrosion is typically found to occur in pipes buried in corrosive soils while internal corrosion is dependent on water chemistry and flow characteristics. In the literature, corrosion of cast iron pipes (external and internal) is typically characterized by corrosion pit depth even though corroded area and corroded pit volume as well as pit location may enhance this characterization. Knowledge of these corrosion pit properties permits the assessment of its structural integrity. Typically, internal and external corrosion pits and corroded areas are observed to occur in many irregular shapes and sizes, which make their characterization a challenge. This paper describes extreme value statistical models that can be used to estimate external and internal corrosion pit depths in cast iron pipes using indirect properties or parameters. The goal of these corrosion models is to be able to predict corrosion pit depth based on available data with an acceptable degree of confidence. The external corrosion model is calibrated using external corrosion data collected by Thames Water Utilities Ltd (TWUL) and subsequently validated with data obtained from the inspection of four cast iron pipe lengths. The internal corrosion model was calibrated using internal corrosion data but sufficient appropriate data was not available for its validation.

There are nearly 4,800,000 km (3,000,000 miles) of paved roads and 80,000 km (50,000 miles) of paved highways in the United States, many of which are in poor condition and approaching the end of their design life. To upgrade this valuable infrastructure, state and federal governments have advocated for rubberized asphalt concrete (RAC) technology, which would meet the current needs without compromising the ability of future generations to meet their own demands. This paper presents a cost-estimating system for rubberized asphalt road rehabilitation projects. The proposed system utilizes information collected from 44 projects and applies neural networks to perform its task. This tool is believed to be helpful in many road pavement applications, such as preparing accurate budget estimates and life-cost analyses, as well as managing financial resources in limited budget environments.