Journal of Infrastructure Systems

Pontis is a bridge management system that performs a network level analysis suggesting specific bridge repair/retrofit projects for projected deterioration and budgets. However, Pontis is not a project level analysis that specifies the design or process of constructing a repair/new build. The Pontis cost associated with the suggested repair/retrofit is not designed to represent the specific real-life cost of a suggested repair/retrofit. This paper documents research in parametric cost engineering to provide a comprehensive approach for a total project cost evaluation. A review of the treatment of structural elements from the bridge cost component of the Parametric Cost Engineering System is provided herein and compared with the methodology of Pontis. The writers recommend that the methodology of Pontis for a network level analysis be augmented by a project level analysis utilizing parametric cost engineering to evaluate the repair/new build cost of bridge treatments. The writers’ parametric recommendation is described herein.

Effective planning for the renewal of water distribution systems requires accurate quantification of the structural deterioration of water mains. Direct inspection of all water mains in a distribution system is almost always prohibitively expensive. Identifying water-main breakage patterns over time is an effective and inexpensive alternative for gauging the structural deterioration of a water distribution system. While the structural deterioration of the pipe is generally considered to be a steady, monotone process, some of the environmental and operational stresses acting upon it are time dependent, steady, or transient. These stresses result in sets of “noisy” breakage rate data that often mask the underlying deterioration (aging) patterns, especially in small data sets. If the cause of these random stresses can be identified and attributed to measurable phenomena (for example, temperatures, precipitation, and so on), a more accurate pipe deterioration pattern can be obtained. Further, better predictions of water-main breakage could be made if these phenomena could be forecast with any degree of accuracy. A method is presented for analyzing how breakage rate patterns of water mains are affected by time-dependent factors. The method is versatile enough to consider any number of underlying factors, but the solution becomes more complex and more data are required as the number of factors increases. A case study is presented to demonstrate the method. Several time-dependent factors were examined, some of which were found to be significant contributors to the time variation of breakage rates.

We estimate the building resource requirements, electricity and energy used, greenhouse gas releases, hazardous waste generated, and toxic air releases for the construction, usage, and demolition of typical U.S. residences in 1997. Within the three phases, usage (54% of economic activity) is the largest consumer of electricity (95%) and energy (93%) and the largest emitter of greenhouse gases (92%), while the construction phase (46% of economic activity) is the largest air toxics emitter (57%) and contributes 51% of hazardous waste. The disposal phase contribution is negligible in all of these categories. From the standpoint of the entire U.S. economy, residential buildings account for 5.3% of the Gross Domestic Product, 38% of electricity consumption, 26% of energy consumption, 24% of greenhouse gas emissions, 26% of hazardous waste, and 12% of toxic air emissions. We comment on possible remedial actions—including some current public policies—to address environmental impacts.

Sound infrastructure deterioration models are essential for accurately predicting future conditions that, in turn, are key inputs to effective maintenance and rehabilitation decision making. The challenge central to developing accurate deterioration models is that condition is often measured on a discrete scale, such as inspectors’ ratings. Furthermore, deterioration is a stochastic process that varies widely with several factors, many of which are generally not captured by available data. Consequently, probabilistic discrete-state models are often used to characterize deterioration. Such models are based on transition probabilities that capture the nature of the evolution of condition states from one discrete time point to the next. However, current methods for determining such probabilities suffer from several serious limitations. An alternative approach addressing these limitations is presented. A probabilistic model of the time spent in a state (referred to as duration) is developed, and the approach used for estimating its parameters is described. Furthermore, the method for determining the corresponding state transition probabilities from the estimated duration models is derived. The testing for the Markovian property is also discussed, and incorporating the effects of history dependence, if found present, directly in the developed duration model is described. Finally, the overall methodology is demonstrated using a data set of reinforced concrete bridge deck observations.

A number of state departments of transportation (DOTs) have established bridge management systems (BMSs) to assist in prioritizing and optimizing bridge improvement programs within budgetary constraints. Several factors, including age, traffic, and environment, have been identified in current BMS literature as having a direct impact on the deterioration of bridge components or elements. However, no formal methodology—using bridge inspection data—exists to determine the effects of the environment. Estimating bridge element service lives without considering environmental effects may lead to biased estimates. Further, existing deterioration models are incapable of using the nonnumeric element level inspection data (G=good, F=fair, P=poor, and C=critical), which most state DOTs have been collecting for nearly 30 years per Federal Highway Administration guidelines. A methodology is proposed using statistical analysis to determine the effects of environmental regions on mean service life estimates of paint on steel girder bridges, using bridge inspection field data collected by bridge inspectors. The data were based on the nonnumeric condition appraisal (G-F-P-C) format used for a bridge element or protective system condition evaluation. The methodology proposed an environmental classification system and service life estimates for use in BMS programs.