Strain monitoring based bridge reliability assessment using parametric Bayesian mixture model

Bridge condition assessment by use of structural health monitoring (SHM) data has been recognized as a promising approach towards the condition-based preventive maintenance. In-service bridges are normally subjected to multiple types loads such highway traffic, railway wind and thermal effect, resulting in heterogeneous multimodal structure strain/stress responses. This study aims develop an SHM-based bridge reliability procedure terms parametric Bayesian mixture modelling. The model admits representation responses, while paradigm enables both aleatory epistemic uncertainties be accounted for By defining appropriate priors parameters that viewed random variables interpret uncertainty, analytical form full conditional posteriors is derived. A Markov chain Monte Carlo (MCMC) algorithm conjunction with Bayes factor explored determine optimal order estimate joint posterior parameters. In compliance framework, index elicited using first-order method. estimated value index, which serves quantitative measure in-service bridge, can successively updated accumulation data. proposed method exemplified one-year strain acquired from instrumented Tsing Ma Suspension Bridge, evolution obtained.