Modeling Deterioration of Bridge Components with Binary Probit Techniques with Random Effects

Highway agencies use deterioration models to monitor the performance of bridge components (deck, superstructure, and substructure) and to predict their remaining service lives on the basis of the attributes of the bridges and their operating environments. Most bridge deterioration models are deterministic in nature. However, as a result of recent federal legislation, agencies are paying more attention to risk-based performance evaluation and decision making and are seeking models that incorporate stochastic elements. Probabilistic models provide more robust predictions of conditions. This paper describes the development of ordered binary probit (BP) models that duly account for observation-specific effects. The models describe the bridge component deterioration trends, specifically the probability that the component condition will drop from one state to another. This paper also acknowledges past similar or related efforts in this area of research but presents new insights and simplifies the complexity associated with BP models. To demonstrate the application of the models, data from over 5,000 in-service bridges were accessed; included were component age, superstructure material type, type of service under bridge, highway functional class, truck traffic, climate severity, rehabilitation history, condition switching state in last inspection period, and current condition rating. With the use of the developed BP models, a simulation was conducted to predict the probability of the component condition dropping from one state to another, where the predicted future condition is based on simulation involving the predicted probability and the current condition. The paper also presents visualizations of the deterioration trend simulation for each bridge component.

Language

  • English

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Filing Info

  • Accession Number: 01604841
  • Record Type: Publication
  • ISBN: 9780309369770
  • Files: TRIS, TRB, ATRI
  • Created Date: Jul 20 2016 10:10AM