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| Structural damage detection based on an ant lion optimizer algorithm and trace sparse regularization |
| CHEN Chengbin1，YU Ling1,2，PAN Chudong1，CHEN Zepeng1 |
1.School of Mechanics and Construction Engineering, Jinan University, Guangzhou 510632, China;
2.MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou 510632, China |
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Abstract Classical swarm intelligence (SI) based structural damage detection (SDD) methods have some common deficiencies, such as low identified accuracy and low robustness.In order to solve the above problems, a novel method based on the ant lion optimizer (ALO) algorithm and trace sparse regularization was proposed to solve the SDD problem.First of all, the SDD inverse problem was transformed into a constrained optimization problem in mathematics.According to the model updating principle, the objective function of the SDD optimization problem was defined by the structural modal parameters.Then, the trace sparse constraint was introduced into the objective function.Finally, the validity of the proposed method was verified by numerical simulations of a simply-supported beam in damage patterns and by measurement data of a steel-tube simply-supported beam.The SDD results show that the proposed SDD method can effectively update the finite element model (FEM).Under different noise levels and damage patterns, the proposed method can accurately locate damages and quantify damage severities.
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Received: 12 April 2018
Published: 15 August 2019
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