Bayesian approach to condition monitoring of PRC bridges

This paper presents a damage detection procedure based on Bayesian analysis of data recorded by permanent monitoring systems as applied to condition assessment of Precast Reinforced Concrete (PRC) bridges. The concept is to assume a set of possible condition states of the structure, including an intact condition and various combinations of damage, such as failure of strands, cover spalling and cracking. Based on these states, a set of potential time response scenarios is evaluated first, each described by a vector of random parameters and by a theoretical model. Given the prior distribution of this vector, the method assigns posterior probability to each scenario as well as updated probability distributions to each parameter. The effectiveness of this method is illustrated as applied to a short span PRC bridge, which is currently in the design phase and will be instrumented with a number of fiber-optic long gauge-length strain sensors. A Finite Element Model is used to simulate the instantaneous and time-dependent behavior of the structure, while Monte Carlo simulations are performed to numerically evaluate the evidence functions necessary for implementation of the method. The ability of the method to recognize damage is discussed.