Journal of Infrastructure Systems

This paper investigates an incremental pavement performance model based on experimental data from the American Association of State Highway Officials road test. Structural properties, environmental effects, and traffic loading, the three main factors dominating the characteristic of pavement performance, are incorporated into the model. Due to the limited number of variables that can be controlled and observed, unobserved heterogeneity is almost inevitable. Most of the existing models did not fully account for the heterogeneity issue. In this paper, the Bayesian approach is adopted for its ability to address the issue of interest. The Bayesian approach aims to obtain probabilistic parameter distributions through a combination of existing knowledge (prior) and information from the data collected. The Markov chain Monte Carlo simulation is applied to estimate parameter distributions. Due to significant variability in the parameters, the need exists to address heterogeneity in modeling pavement performance. Furthermore, it is shown that not all the parameters are normally distributed. It is therefore suggested that the performance model developed in this research provides a more realistic forecast than most previous models. In addition, pavement deterioration forecast based on the Gibbs output is performed at different confidence levels with varying inspection frequencies, which can enhance the decision-making process in pavement management. In general, the Bayesian approach presented in this paper provides an effective and flexible alternative for model estimation and updating, which can be applied to both the road test data sites and other data sources of interest.

Determining the consequences of a water contamination event is an important concern in the field of water systems security. Morbidity and mortality resulting from such a contamination are influenced in part by the amount of contaminated water consumed and the time between consumption and medical treatment. Water quality sensors in the water distribution network may shorten this time and help the users avoid the contaminant’s adverse effects by alerting authorities to unusual water quality parameters; otherwise, the authorities may first suspect contamination when the victims begin seeking treatment. Once the irregular water quality parameters have been detected, some time may still elapse before all users stop consuming the contaminated water. Modeling this additional delay is the focus of this article. This delay has been divided into five independent, sequential processes. The first phase is the amount of time required to transmit the sensed or measured contaminant concentrations to the local authorities. The second process includes the authorities’ efforts to verify that there is a genuine contamination event, usually through additional water quality tests. The third stage includes any measures that the authorities take in preparation to alerting the public to the threat including agency coordination, drafting announcements, contacting media, and printing flyers. The fourth phase of the delay is the time required to transmit the news of the contamination to the public. The final period encompasses the time elapsed while the system users, after being informed of the contamination, decide whether or not to comply with instructions on how to avoid the adverse effects. Probability distributions are constructed for the duration of each phase of the delay based on data collected from historical water contamination events and other disasters and characteristics of typical sensor networks. The entire response process is modeled using a Monte Carlo approach to determine probability distributions of response delay.

The Public Health Security and Bioterrorism Response Act of 2002 amended the Safe Drinking Water Act requiring community water systems serving over 3,300 customers to complete water vulnerability assessments (WVAs); however, no specific methodology was provided. Forty-five government and industry agencies associated with water security were identified. Existing WVA methodologies were compared to the EPA’s six-step WVA guidance and an expanded version of the hierarchical holographic modeling (HHM) framework for the hardening of water systems, a technique previously used for assessing vulnerability of infrastructure. Neither the AWWA Water System Security Field Guide, nor the Security Vulnerability Self-Assessment Guide for Small Drinking Water Systems were in complete agreement with the EPA’s WVA guidance. Inclusion of HHM risk scenarios in the assessed methodologies ranged from 64 to 95%. Fifty-nine of the 112 HHM risk scenarios were not included in at least one, and 34 risk scenarios were not included in at least two WVA methods. No method emerged as a “best practice” technique for small and medium sized water systems. Consistent standards for WVA methods are needed.

This case study shows how designers of infrastructure systems can evaluate flexibility in engineering systems in fairly simple ways. Specifically, it illustrates a spreadsheet approach to valuing “real options” in a project. The model avoids complex financial procedures, which are both inappropriate for most design issues and constitute barriers to understanding and thus achieving the substantial improvement in performance that real options enable. The spreadsheet approach uses standard procedures; is based on data available in practice; and provides graphics that explain the results intuitively. It should thus be readily accessible to practicing professionals responsible for engineering design and management. A practical application to the design of a parking garage demonstrates the ease of use and presentation of results of this approach.

We present a multiobjective optimization model for determining an efficient budget allocation for a portfolio of infrastructure projects. The model takes into account both the cost and the priority rank of each project while considering probabilistic constraints related to the available budget. A zero-one multiobjective optimization problem with chance constraints is developed and solved. This approach helps to more accurately take into account competing objectives such as cost and value of the funded projects, as well as budget risk to find a Pareto optimal set of project selection alternatives. We present numerical results based on infrastructure projects from a United States governmental agency in which we analyze the Pareto optimal set of solutions for this problem and test the working of the model with several cases that highlight budget contingencies needed in the face of random project costs.

Telework has emerged as a possible solution to transportation-related air pollution problems. This paper analyzes, both deterministically and probabilistically, a California-based 1-day telework scenario, and explores how the mode of transportation and other parameters such as vehicle miles traveled, vehicle model, occupancy rate, telecommuting frequency, and season (heating or cooling) affect the air pollution effects of telework programs when energy consumption-related emissions due to heating, cooling, lighting, and the use of electronic and electrical equipment (in the home and company office) are accounted for. Among others, the study found that total telework-related ${\mathrm{CO}}_2$ emissions during the cooling season and ${\mathrm{SO}}_2$ , ${\mathrm{NO}}_x$ , and hydrocarbon emissions in both seasons appear to be lower than nontelework emissions for all modes of transportation (except for light rail with higher ${\mathrm{NO}}_x$ emissions and urban transit buses with roughly equal ${\mathrm{NO}}_x$ emissions in the heating season). Light rail also has higher telework ${\mathrm{N}}_2\mathrm{O}$ and ${\mathrm{CH}}_4$ emissions. However, given the uncertainties in the data, the differences may be negligible. Urban transit buses and commuter express buses were found to be associated with more telework than nontelework $\mathrm{CO}$ emissions in both seasons. For these two modes, telework ${\mathrm{PM}}_{10}$ emissions are higher in the cooling and about the same in the heating season than nontelework emissions. Natural gas-powered ferries have more telework ${\mathrm{PM}}_{10}$ emissions than nontelework emissions. The study also found that for low-frequency telework programs energy use impacts could overturn transportation-related emission reductions independent of the mode of transportation used. Avoiding more polluting modes of transportation, increasing occupancy rates, substituting longer commutes and especially increasing telecommuting frequency could counteract these negative effects.

In 1993 Headquarters, U.S. Army Corps of Engineers, issued the first formal Corps-wide analysis procedure providing guidance for analyzing the effects of barge impact loading on navigation structures. The guidance was rescinded in 2001 due to concerns about the conservatism of the computed results for usual and unusual loadings and accuracy with shallow approach impact angles and direct head-on impact. This paper addresses the interpretation of full-scale, low-velocity, controlled barge train (15-barge) impact experiments conducted at the decommissioned Gallipolis Lock at Robert C. Byrd Lock and Dam, Gallipolis Ferry, W.V. Two sets of methodologies were used to interpret the barge-to-wall impact data: (1) the use of the equations of equilibrium combined with an assumed value for the coefficient of friction between the barge train and the stiff-to-rigid wall, and (2) the energy method. A simplified empirical correlation is presented to calculate the maximum impact force as a function of the linear momentum normal to the wall using forces measured during these full-scale impact experiments. This empirical correlation is the basis for new Corps guidance for calculating glancing-blow impact forces.