Richard Kerr

USER COST MODEL FOR BRIDGE MANAGEMENT SYSTEMS

User cost models are used in bridge management systems to quantify, in economic terms, the potential safety and mobility benefits of functional improvements to bridges. One component of many such models is a formula to predict traffic accident risk on the basis of bridge characteristics such as roadway width and approach roadway alignment. A critical analysis of the user cost model used in AASHTO’s Pontis and Bridgit systems found that the model was overly sensitive to extremes of roadway width, yielding unrealistically high benefit estimates. A new model was developed using Florida data on bridge characteristics and traffic accidents. The new model has superior behavior and statistical characteristics on a full inventory of state highway bridges. Since it relies solely on National Bridge Inventory data items, the model is readily transferable to many different bridge management systems.

Modeling the Risk of Advanced Deterioration in Bridge Management Systems

The Florida Department of Transportation (DOT) has developed a set of risk models for its bridge management system; the models are built into its existing Excel-based project-level and network-level decision support tools and are intended for eventual use in AASHTOs Pontis 5.2. One of these new models is an analysis of the risk associated with advanced deterioration. This model extends the reach of Floridas existing deterioration models to estimate the likelihood of service disruption if a badly deteriorated element should be unrepaired. The Florida DOT maintains data about demolished and replaced bridges as inactive records in its Pontis database. The condition and characteristics of the removed bridges were statistically analyzed to explore the reasons for the end of each bridges life. On the basis of the available data, including information on load posting and reconstruction, the likelihood of service disruption was reasonably quantified. The best models developed in the research used a combination of linear and lognormal forms and reflected the buildup of risk caused by repeated Markovian deterioration as well as the typical delay that occurred between the observation of the deteriorated conditions and the resulting action to replace or reconstruct the bridge. As a by-product of the research, a linear failure probability model was developed; the model is suitable for Pontis releases up to 4.5. This model will be helpful to bridge owners who do not have their own source of estimates for the probability of bridge element failure.

Estimating agency cost of maintenance, repair, and rehabilitation for Florida bridges

The Florida Department of Transportation (FDOT) recently conducted a study to develop agency cost factors for maintenance, rehabilitation, and repair (MR&R) of bridges, for the state’s implementation of the AASHTOWare Pontis® Bridge Management System. The study analyzed three information systems on maintenance and construction history available at the FDOT. Pontis defines many combinations of bridge element, environment, condition, and feasible action, resulting in more unit cost values than can be estimated from the available data. To simplify cost determination, a classification scheme (MR&R Action Sub-category) was devised to group the Pontis combinations. Statistical analysis was performed on the raw cost data to extract useful numbers from the available data sources. The results were supplemented with the judgment of an FDOT panel of experts. Although the panel found the analysis of historical data to be useful for 70% of the actions considered, only 50% of the actions had a statistically significant sample, and only 15% were usable directly from a statistical analysis. The main issues were differences in data collection units and differences or uncertainties in the scope of recorded actions. The study recommended improvements in the automated systems to enhance the ability to use the data in future updates of the agency cost model.

Bridge Health Index: Study of Element Condition States and Importance Weights

The AASHTO Pontis bridge management system has been used to support network-level and project-level decision making on the condition and functional obsolescence of bridges. State departments of transportation often develop bridge inspection data collection methods, deterioration models, cost models, and other preservation analysis capabilities to comply with the requirements of the federal Government Accounting Standards Board. The bridge health index (BHI) in the Pontis bridge management system has been used in the evaluation of the condition of bridges and elements at the project and network levels. This paper investigates three issues in the computation of the BHI: the effects of using linear and nonlinear scales for the condition state weights when computing the element health index (EHI); the application of amplification weights to EHI values to emphasize bridge elements in bad condition; and the development of element weights based on element replacement costs, element long-term costs, element vulnerability to hazard risks, and a combination of these measures. Historical condition data from element-based inspection were used to evaluate these effects at the network level.

Modeling Hurricane Hazards and Damage on Florida Bridges

The state of Florida is developing a risk-based decision support model, specifically for its state-maintained bridges, to handle the occurrence of natural hazards, such as hurricanes, tornadoes, floods, landslides, and wildfires. Many bridges in Florida are vulnerable to damage caused by these hazards. With a focus on hurricanes, this paper develops prediction models (estimates of likelihoods) based on historical data of hazard events in Florida, as well as on existing national models. The Poisson process was assumed for the occurrence of natural events; predictions were estimated for the annual probability of occurrence. It was observed that, on average, the probability of hurricane occurrence at bridge locations decreased with the hurricane intensity (category number). In comparison, wildfires, another common hazard, had the highest likelihood estimates of the natural hazards. With a focus on physical damage to bridges, the consequences of hurricanes were also estimated on the basis of historical data within Florida. The damaged bridge components and elements were identified, and the levels of damage and the associated repair costs were assessed.