Open AccessEfficient reliability assessment method for bridges based on Markov Chain Monte Carlo (MCMC) with Metropolis-Hasting Algorithm (MHA)
Author(s) -
Liye Zhang,
Leilei Dong,
Shoushan Cheng,
Wanheng Li,
Bingjian Wang,
Hanyong Liu,
Ke Chen
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/580/1/012030
Subject(s) - markov chain monte carlo , reliability (semiconductor) , computer science , monte carlo method , markov chain , algorithm , metropolis–hastings algorithm , markov process , reliability engineering , mathematical optimization , data mining , mathematics , statistics , engineering , machine learning , power (physics) , physics , quantum mechanics
Reliability assessment plays a vital roles in bridge health monitoring (BHM) technique. The analysis results of inspection data and monitoring data, such as numerical data, image data and video data, are not well due to there is no efficient reliability assessment method. This paper analysed the applied effect of Markov Chain Monte Carlo (MCMC) simulation method. The subset simulation method is used to analyse small failure probability events. Furthermore, the reliability assessment process based on Markov Chain Monte Carlo (MCMC) simulation method with Metropolis-Hasting Algorithm (MHA) is proposed. The advantage of this method is to improve the application efficiency and accuracy of reliability assessment based on BHM data.