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作者机构:Jinan Univ Sch Mech & Construct Engn Guangzhou 510632 Guangdong Peoples R China Jinan Univ MOE Key Lab Disaster Forecast & Control Engn Guangzhou Guangdong Peoples R China
出 版 物:《ADVANCES IN STRUCTURAL ENGINEERING》 (结构工程进展)
年 卷 期:2019年第22卷第3期
页 面:597-612页
核心收录:
学科分类:08[工学] 081402[工学-结构工程] 081304[工学-建筑技术科学] 0813[工学-建筑学] 0814[工学-土木工程]
基 金:National Natural Science Foundation of China [51678278 51278226]
主 题:antlion optimizer algorithm sparse regularization structural damage detection trace Lasso weighted strategy
摘 要:With the rapid development of computation technologies, swarm intelligence-based algorithms become an innovative technique used for addressing structural damage detection issues, but traditional swarm intelligence-based structural damage detection methods often face with insufficient detection accuracy and lower robustness to noise. As an exploring attempt, a novel structural damage detection method is proposed to tackle the above deficiency via combining weighted strategy with trace least absolute shrinkage and selection operator (Lasso). First, an objective function is defined for the structural damage detection optimization problem by using structural modal parameters;a weighted strategy and the trace Lasso are also involved into the objection function. A novel antlion optimizer algorithm is then employed as a solution solver to the structural damage detection optimization problem. To assess the capability of the proposed structural damage detection method, two numerical simulations and a series of laboratory experiments are performed, and a comparative study on effects of different parameters, such as weighted coefficients, regularization parameters and damage patterns, on the proposed structural damage detection methods are also carried out. Illustrated results show that the proposed structural damage detection method via combining weighted strategy with trace Lasso is able to accurately locate structural damages and quantify damage severities of structures.