2019 CSCE Annual Conference - Laval (Greater Montreal)

2019 CSCE Annual Conference - Laval (Greater Montreal) Conference


Title
Condition Prediction of Concrete Bridge Decks Using Markov Chain Monte Carlo-Based Method


Author(s)
Mr. Eslam Mohammed Abdelkader, Concordia university (Presenter)
Dr. Osama Moselhi, Concordia University, BCEE
Dr. Mohamed Marzouk, Cairo University
Dr. Tarek Zayed, The Hong Kong Polytechnic University
Abstract

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.