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Iterative two‐stage approach for identifying structural damage by combining the modal strain energy decomposition method with the multiobjective particle swarm optimization algorithm
Author(s) -
Xu Mingqiang,
Wang Shuqing,
Jiang Yufeng
Publication year - 2019
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.2301
Subject(s) - particle swarm optimization , algorithm , stage (stratigraphy) , modal , iterative and incremental development , mathematical optimization , iterative method , decomposition , structural health monitoring , computer science , process (computing) , energy (signal processing) , mathematics , statistics , structural engineering , engineering , materials science , paleontology , ecology , software engineering , polymer chemistry , biology , operating system
Summary An iterative two‐stage structural damage identification approach that combines the modal strain energy decomposition (MSED) method with the multiobjective particle swarm optimization algorithm is presented. The proposed scheme is inspired by the general noniterative two‐stage approach, which attempts to locate damage in the first stage and estimate the severity of damage in the second stage; however, the present approach differs by constructing an iterative MSED indicator to perform damage localization and severity estimation in a cyclic manner. Consequently, false positive indications of damage are gradually suppressed, and the precision of the damage severity estimation is improved as the iteration continues. In addition, a new element activation operation is embedded into the iterative process; in this step, possible false negative indications of damage excluded in the first stage can be activated and subsequently retested to identify damage, thereby reducing the false negative rate. Furthermore, a comparative study is conducted between the iterative two‐stage approach and the general noniterative approach by contemplating a 3‐D structure, and the effects of measurement noise and spatial incompleteness are considered.