Damage Identification of Frame Structure Based on Approximate Metropolis–Hastings Algorithm and Probability Density Evolution Method
Y. Yang, Yuhong Ling, Xiaoheng Tan, S. Wang, R. Q. Wang
Abstract
This paper proposes a new method to identify local damages in frame structures based on approximate Metropolis–Hastings (AMH) algorithm and statistical moment. By analyzing the sensitivity of different statistical moment-based damage indices, the fusion index of fourth-order displacement moment and eighth-order acceleration moment is selected. Then the local damages in frame structures are primarily evaluated by AMH algorithm, where Gibbs sampling is adopted. Finally, the uncertainty of identified local damages is analyzed by using probability density evolution method (PDEM). Numerical simulations have been conducted to compare the proposed method with other similar damage detection methods, showing that the proposed method is more time-saving due to the involvement of Gibbs sampling and more accurate in assessing the damage severity. Experimental study of a 12-story benchmark frame testing has also been carried out, further validating the effectiveness of the proposed method.