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Bayesian estimation approach based on modified SCAM algorithm and its application in structural damage identification
Author(s) -
Zhao Yinan,
Gong Maosheng,
Zuo Zhanxuan,
Gao Yanbin
Publication year - 2021
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2654
Subject(s) - identification (biology) , bayesian probability , computer science , algorithm , estimation , artificial intelligence , machine learning , engineering , botany , systems engineering , biology
Summary The single‐component adaptive Metropolis (SCAM) algorithm, as one of the Markov chain Monte Carlo (MCMC) sampling methods, is very effective at solving high‐dimensional posterior joint probability distributions. However, studies show that the SCAM algorithm is prone to generating duplicate samples, thus lowering the sampling efficiency and causing large calculation errors. To solve this problem, a modified SCAM algorithm is proposed in the present study. In the modified algorithm, the expression of the variance of the proposal distribution is redefined so that the Markov chain formed by the sample sequence is relatively stable. With the obtained structural response, the posterior joint probability distribution of the physical parameters of the structure is achieved using Bayesian theory, which is then combined with the modified SCAM algorithm to solve the posterior marginal probability distribution and the optimal estimates. Hence, the structural damage is identified. The validity, reliability, and practicability of the modified SCAM algorithm are verified through theoretical analysis, a numerical simulation of the structural example, and a shaking table test. The results show that the modified SCAM algorithm not only enhances the accuracy of the calculation results but also increases the sampling efficiency. This method can be used as a reference for physical parameter identification and damage assessment of the engineering structures.