Journal of Infrastructure Systems

Concrete gravity dams are an important part of the nation's infrastructure. Many dams have been in service for decades. During the service life of a dam, operating conditions and natural environmental factors may have led to some deterioration in its structural integrity, mechanical equipment, and foundation. Moreover, advances in the methodologies by which design-basis events for natural phenomena hazards are identified have caused these events to be revised upward, in some cases significantly. An increasing number of existing dams fail to meet the more recent performance criteria. A fragility model of a dam provides a tool for rational safety assessment and decision making by using a probabilistic framework to manage the various sources of uncertainty that affect dam performance. In this paper, basic fragility concepts are presented, and databases required to support the fragility assessment are identified. The method is illustrated using a concrete monolith from the Bluestone Dam in West Virginia, designed in the late 1930s. Traditional design practices have been sufficiently conservative that the probability of dam failure under a probable maximum flood 11 m (36 ft) higher than the original design-basis flood remains small.

The development of models to predict the distribution of times between changes in condition-state for reinforced concrete bridge decks is presented in this paper. The authors used semiparametric hazard rate modeling techniques to develop stochastic duration models for bridge decks in the Indiana Bridge Inventory database. The advantage of semiparametric methods is that they do not constrain the hazard rate to follow a prespecified probability density function. This approach is flexible enough to accommodate different types of deterioration processes, and can account properly for both censored and uncensored data. Furthermore, it relates observed deterioration to relevant explanatory variables. Finally, because this approach quantifies the time-dependent hazard rate function, it facilitates the computation of time-dependent transition probabilities for use in bridge management systems. The models presented in this paper are characterized by realistic trends. The estimated parameters are statistically significant and have, for the most part, intuitively correct signs. However, the models include only a small number of explanatory variables, and thus do not capture the entire array of relevant factors that contribute to bridge deck deterioration. This is especially true when they are compared to the state-based models obtained in earlier research using the same data. The reasons are that the deck conditions are observed at infrequent points in time (every 2 years) and that the window of observation is relatively short (10 years).

This paper describes the MSRP (Model for Station Rehabilitation Planning) for selecting subway stations for rehabilitation. This method evaluates the stations at four different levels: (1) technical/social evaluation including functional criteria and social/community/political criteria; (2) database, which includes cost estimates for partial and full rehabilitation of stations with respect to functional and social/political criteria; (3) funding sources and constraints; and (4) management constraints. The existing procedure for station selection for rehabilitation used by the Metropolitan Transit Authority New York City Transit (MTA NYCT), various criteria used for evaluation, and limitations of the system are described. The development of the MSRP has been explained in relation to the MTA NYCT system requirements, and the procedure of the MSRP has been explained through a hypothetical example. This paper illustrates the results and the benefits of the proposed model for station rehabilitation planning.

Constrained funding requires that resources be allocated and used efficiently in order to maximize the safety, utility, and state of good repair of public facilities. During the life cycle of any public facility various risk factors might influence the performance and operating cost of the infrastructure. The risk factors influence the longevity, fitness, and adaptability of the facility dictating the necessity and timing of renewal or rehabilitation. This paper identifies risk factors that influence the cost-effective management, operation, and maintenance of bridges, roads and highways, and subway stations as well as how and when in the project life cycle the identified risk factors impact the associated facility costs.

The understanding of sewer deterioration mechanisms helps asset managers in developing deterioration models to estimate whether sewers have deteriorated sufficiently for likely collapses. While an accurate deterioration model is a significant component of an infrastructure management system, research in the area of deterioration modeling of sewer systems in the United States is still in its infancy. Research in the area of highways has matured and has become the basis for studies in sewer systems. Modeling the deterioration of highway systems involves methods that vary in complexity from Markov-chains-based models to econometric models. This paper presents the use of Markov-chains-based models in conjunction with nonlinear optimization for extending infrastructure management modeling to sewer systems. Modeling the structural condition of sewers using the Markov-chains-based model has its own challenges. This paper discusses the problems encountered in developing deterioration models for sewer management systems and suggests some possible improvements for deterioration modeling of combined sewer systems.