Development of a condition-based deterioration model for bridges in Rhode Island

Abstract

Almost one in four bridges in Rhode Island have been rated as structurally deficient in 2017 according to the American Society of Civil Engineer‘s (ASCE) most recent Report Card. This makes Rhode Island the state with the highest rate of structurally deficient bridges in the USA. Since the allocated financial resources from federal, state and local level are scarce, effective bridge management is of crucial importance to maintain bridges in a sufficient condition and preserve them from decay. A major part of Bridge Management Systems (BMS) is prediction models, which have become increasingly important in their function to forecast bridge durability and their need for repair and maintenance. In this study, three deterioration models, one for each major bridge element (i.e., deck, superstructure, and substructure) have been developed for the state of Rhode Island. The deterioration models were designed as Dynamic Bayesian Networks (DBN), which are based on annually recorded inspection data of Rhode Island’s bridges provided by the National Bridge Inventory (NBI). Several predictions have been made with varying input parameters for the model‘s variables, which illustrate the capability of the developed prediction models. Moreover, the DBN's updating ability is demonstrated by several sample predictions which incorporate the influence of simulated maintenance actions. Additionally, the NBI database has been used to investigate the correlation between several bridge related parameters and the deterioration of Rhode Island’s bridges.