Condition prediction of concrete bridge decks using markov chain Monte carlo-based method

In view of budget limitations and inadequate investment in civil infrastructure, concrete bridges are deteriorating; raising concern for public safety. This state of affairs necessitates the development of a smart and efficient integrated method for optimized bridge intervention plans at the project and network levels. The present study focuses on modelling deterioration of concrete bridge decks. A reliable deterioration model enables transportation agencies to optimize their maintenance, repair, and rehabilitation (MR&R) plans, and consequently address needed maintenance works effectively. This paper presents a hybrid Bayesian-optimization method to calibrate transition probabilities of the developed Markovian model. These probabilities are demonstrated in the form of posterior distributions, whereas the transition from a condition state to the next lower state is represented by a function that captures the severity of defects such as corrosion, delamination, cracking, spalling, and pop-out. The Bayesian belief network is utilized to investigate the severity of these defects. The proposed method incorporates Markov chain Monte Carlo (MCMC) Metropolis-Hastings algorithm to derive the posterior distributions of transition probabilities. Finally, a stochastic optimization model is designed to build a variable transition probability matrix for each five-year zone in an effort to speed up the computational effort.