A Bayesian Approach for Structural Health Monitoring of Concrete Bridge

Chloride attack from de-icing salt during winter may diffuse through the concrete cover and corrosion will initiate in concrete bridge when the chloride concentration exceed the threshold value. It may lead to loss of strength and unserviceability of the bridge. Structural health monitoring system (SHMS) is used to monitor the corrosion process of reinforcement and has been actively developed recently. However, the monitoring system is subjected to uncertainties associated with material, environmental load and structural effects. Hence the need for probabilistic analysis expressing life cycle performance in a reliability format. Modelling uncertainty is often associated with limited knowledge which can be reduced by increasing the availability of data. In this study, a method is developed to improve confidence in predicting corrosion concentration with taking into account time dependent reliability analysis. Bayesian updating method is used to update belief by taking into account the prior belief given the likelihood that such event is known. Monte Carlo simulation is used to calculate the probability of failure for annual increment over the life time of the structure based on multiple observations. It is found that by using Bayesian updating, uncertainty of the posterior model is reduced hence increased confidence in predicting future performance of the concrete bridg