Journal of Infrastructure Systems

Cathodic protection is a viable measure to extend the residual life of water mains and thus defer capital investments in their rehabilitation and renewal. The effectiveness of cathodic protection varies with the unique set of conditions under which it is applied and is therefore difficult to quantify. Consequently, reported case histories have been largely anecdotal and most often based on the observed reduction of water main breaks following the application of cathodic protection. Models to assess the impact of cathodic protection programs were proposed in a previous companion paper “Quantifying the Effectiveness of Cathodic Protection in Water Mains: Theory.” This paper describes the application of these models to five groups of water mains from two water utilities that have had on-going programs of hot spot and retrofit cathodic protection. The case histories show that cathodic protection programs (hot spot and retrofit strategies) have had a significant effect on reducing breakage rates, however, in some cases it appears that the much cheaper hot spot strategy is sufficiently effective and there is no need to retrofit as well. The proposed analytical methods should assist water utilities to optimize the implementation and scheduling of future cathodic protection programs.

This paper reviews and utilizes the current body of telecommuting related research to study the costs and benefits of home-based telecommuting. Monte Carlo simulation methods were utilized to help account for costs or benefits that remain highly variable or have not been well documented by past research. This study illustrates the conditions under which the business case for telecommuting is supported or weakened. Conditions for the employee (the telecommuter) are generally most favorable when: (1) the employer bears the equipment cost; (2) commute distances are above average; (3) the commute vehicle has below-average fuel economy; (4) travel time is highly valued; and (5) telecommuting is frequent, while conditions for the employer are most favorable when: (1) the telecommuter bears the equipment cost; (2) there is low telecommuter attrition; (3) the employee is highly productive on telecommuting days; (4) the employee’s time is highly valued; and (5) telecommuting is frequent. For the employer, telecommuting is also favorable if parking and office space savings are realized. While public sector benefits are conceivable, they remain insignificant in most situations because the impacts on the transportation network are probably not concentrated enough over a specific transportation corridor to realize infrastructure benefits and not quantified or valued enough within a regional air district to realize significant air quality benefits. Further, the public sector loses fuel tax revenue. Altogether, this paper provides insight into the potential public sector impacts of telecommuting, as well as the federal, state, regional, and local public policy implications that arise when telecommuting is considered among other transportation demand management alternatives.

This paper describes investigations into a development of a new application of neural networks (NN) for prediction of pipeline failure. Results show higher correlations with recorded data when compared with the two existing statistical models. The shifted time power model gives results in total number of failures and the shifted time exponential model gives results in number of failures per year. The database was large but neither complete and nor fully accurate. Factors influencing pipeline deterioration were missing from the database. Using the NN technique on this database produced models of pipeline failure, in terms of failures/km/year, that more closely matched the number of failures of a particular asset recorded for the period.

Current regional hurricane loss estimation models use the present-day vulnerability of the building inventory in predicting future losses and assume that it remains static throughout the time horizon of interest. In reality, the hurricane vulnerability of a region changes with time due to changes in the types and conditions of buildings. Loss estimation models, therefore, may misestimate risk and the effectiveness of mitigation plans based on these loss estimates may be compromised. In this paper, the writers develop a regional vulnerability change model to estimate changes over time in the hurricane wind vulnerability of a regional inventory of wood-frame houses. The model integrates the effects of five types of changes—changing building codes, changing technologies, building demolition and construction, structural aging, and upgrading efforts—in a simulation framework to forecast changes in the vulnerability of a region. Projectile impacts, falling trees, and flooding are not considered currently. A case study application of the model was conducted for one-story, gable roof, wood buildings in New Hanover County, N.C., for the period 2000–2020. Results describe the estimated overall change in the hurricane wind vulnerability, the relative contributions of the five types of change, a sensitivity analysis, and the long-term effects of a hypothetical “what-if” mitigation scenario on the evolution of regional vulnerability.

Monte Carlo simulation techniques are used with bridge fragility curves to evaluate bridge damage in terms of a bridge damage index, and highway network link damage in terms of a link damage index. Static, user-equilibrium analysis is used to evaluate total transportation network delay due to seismically induced damage inflicted on the Los Angeles and Orange County State highway and freeway network. A method of regional seismic risk analysis for highway systems is developed based on the definition of scenario earthquakes representing the seismic hazard of the region, and hazard-consistent probabilities are computed for each scenario. The final result of these efforts is a transportation system risk curve.

The research team evaluates whether a proportional hazards model can successfully be applied to a water distribution piping network located in a complex urban area in order to identify which risk factors play a role in the failure of urban water pipes, to what extent, and, for each pipe, to forecast the number of future breaks. Risk factors can be inherent to the pipe (material, diameter, length, date of installation, break history) or reflecting environmental conditions (traffic, water zone, proximity to subways, highways.) Long Island City, an area of New York City with 220 miles of water pipes and 20 years of break data (1982–2002) constitutes the case study. Various conditions of applicability pertaining to the stratification of the data based on material, break frequency, and history are examined.

This paper presents a modeling framework for planning pavement resurfacing activities on highway networks in the case of continuous pavement state, discrete time, and infinite horizon. Optimal resurfacing policies that minimize discounted life-cycle costs are obtained by solving a multidimensional dynamic program, where travelers’ route choices and the agency’s resource allocation decisions are considered simultaneously. To reduce computational difficulty, policy iteration is used together with a parametric function approximation technique. Numerical examples show that the proposed approach solves the planning problem efficiently. The effect of various factors (travelers’ route choice, agency budget, etc.) on the optimal policies is also analyzed.

The U.S. Army’s Engineer Research and Development Center—Construction Engineering Research Laboratory has developed a “knowledge-based” approach to planning and conducting routine facility inspections. Rather than simply recording deficiencies on a calendar-based schedule this new approach uses knowledge of facilities to develop and execute a tailored inspection plan for individually defined facility “management units.” This knowledge includes measurable attributes such as facility importance, management unit importance, condition (past, present, and predicted future), desired condition thresholds, and expected asset remaining life. Also, recognizing that there are several inspection objectives to be met at different times in a management unit life cycle, different levels of inspections may be used at different times to attain those objectives. The “knowledge-based condition survey inspection (KBCSI)” approach marries all of these elements together to develop inspection plans for a vast array of facility components. The result is more meaningful facility inspection information with less inspection effort compared to traditional calendar-based inspection approaches. This paper discusses the KBCSI approach as applied to building component life cycle management.