Research Area: Metaheuristic Computing
This paper proposes a novel structural damage identification approach by using the clustering based Tree Seeds Algorithm, termed as C-TSA, taking into account of both the finite element modeling errors and measurement noise. In order to make the standard TSA more powerful and robust, K-means cluster technique is introduced into the standard TSA before starting the seeds search, which is beneficial to enhance the algorithm’s global optimization performance. The objective function based on the modal data is formulated for structural damage identification. Numerical studies on benchmark functions and a 61-bar truss structure are conducted to investigate the accuracy and robustness of the proposed approach. The finite element modelling errors and noises in the measurement data are considered. Experimental verifications on a laboratory steel frame structure model is conducted to further validate the accuracy of the proposed approach. The results from the numerical and experimental studies are compared with those obtained from several latest evolutionary algorithms. The identification results demonstrate that the proposed approach is more competitive and robust for structural damage identification even considering the modelling errors and measurement noises.
Keywords:
Tree seeds algorithm
Clustering
Structural damage identification
Uncertainty
Modal data
Noise
Author(s) Name: Zhenghao Ding, Jun Lia, Hong Haoa, Zhong-Rong Lu
Journal name: Engineering Structures
Conferrence name:
Publisher name: ELSEVIER
DOI: 10.1016/j.engstruct.2019.01.118
Volume Information: Volume 185
