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| Structural sparse damage identification considering ambient temperature variations based on support vector machine and enhanced moth-flame optimization |
| Received:March 02, 2021 Revised:November 05, 2021 |
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| DOI：10.7511/jslx20210302002 |
| KeyWord:structural damage identification temperature effect sparse regularization support vector machine sparse damage optimization algorithm I-40 bridge |
| Author | Institution |
| 雷勇志 |
武汉工程大学 土木工程与建筑学院, 武汉 |
| 黄民水 |
武汉工程大学 土木工程与建筑学院, 武汉 |
| 顾箭峰 |
武汉工程大学 土木工程与建筑学院, 武汉 |
| 杨雨厚 |
广西交科集团有限公司, 南宁 |
| 舒国明 |
河北交通职业技术学院, 路桥工程系, 石家庄 |
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| Abstract: |
| Civil engineering structures are always affected by ambient temperature variations,which will influence the results of modal testing and set up obstacles to the evaluation of real structural damage.Furthermore,a damage identification method based on an optimization algorithm is easy to be trapped in a local optimum and lower computing efficiency when the method is used to locate and quantify damage.Aiming to the above problems,in this paper,a damage identification method,which is based on support vector machine (SVM) and enhanced moth-flame optimization (EMFO),is proposed to solve a structural sparse damage identification problem considering temperature variations.Firstly,SVM is used to quantify structural temperature variations.Then,a sparse regularization method is introduced to determine structural sparse damage conditions.Thirdly,the temperature variations and damage situation obtained in the previous step are adopted to perform the initialization of EMFO,which can narrow the search space,and enhance damage identification efficiency.Finally,two examples,a simulated simply supported beam considering temperature variations and random noise effects,and a real engineering structure of I-40 Bridge,a large steel-concrete composite bridge,are utilized to verify the effectiveness of the proposed method. |
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