A computerized hybrid Bayesian-based approach for modelling the deterioration of concrete bridge decks

Abstract

Bridges are aging and deteriorating, thus, reliable deterioration modeling is regarded as one of the vital components of Bridge management systems. This article presents an automated defect-based tool to predict the future condition of the bridge decks by calibrating the Markovian model based on a hybrid Bayesian-optimization approach. The in-state probabilities are demonstrated in the form of posterior distributions, whereas the transition from a condition state to the next lower state is a function of the severities of five types of bridge defects. In the present study, the Bayesian belief network is employed to construct the likelihood function by modeling the dependencies between the bridge defects. The maximum entropy optimization is incorporated to compute the missing conditional probabilities. The proposed approach utilizes Markov chain Monte Carlo Metropolis-Hastings algorithm to derive the posterior distributions. Finally, a stochastic optimization model is designed to build a variable transition probability matrix for each five-year zone via genetic algorithm. An automated tool is programmed using C#.net programming language to facilitate the implementation of the developed deterioration model by the users. Results show that the proposed model outperformed some commonly-utilized deterioration models as per three performance indicators which are: root-mean squared error, mean absolute error, chi-squared statistic.