A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment
Structural health monitoring is an important research field being investigated around the globe. In recent years, meta-heuristics are being used to solve the complex inverse problem of structural damage assessment. In this work, a novel approach depending on a new meta-heuristic and effective object...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-03-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016821004592 |
| _version_ | 1818908559887302656 |
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| author | Nizar Faisal Alkayem Lei Shen Panagiotis G. Asteris Milan Sokol Zhiqiang Xin Maosen Cao |
| author_facet | Nizar Faisal Alkayem Lei Shen Panagiotis G. Asteris Milan Sokol Zhiqiang Xin Maosen Cao |
| author_sort | Nizar Faisal Alkayem |
| collection | DOAJ |
| description | Structural health monitoring is an important research field being investigated around the globe. In recent years, meta-heuristics are being used to solve the complex inverse problem of structural damage assessment. In this work, a novel approach depending on a new meta-heuristic and effective objective function formulation is proposed. Firstly, by considering some research shortcomings, a triple modal-based objective function combination is employed to improve the precision of damage identification. Secondly, a new self-adaptive algorithm which combines the powerful features of the stochastic fractal search with improved mechanisms into one framework, is developed. Moreover, the concept of quasi-oppositional learning is utilized to improve the overall exploration in both initial and executive stages. The new algorithm, called the self- adaptive quasi-oppositional stochastic fractal search (SA-QSFS), is benchmarked using well-known benchmark functions and applied on the IASC-ASCE FE model for damage assessment. Various damage scenarios are studied using partial modal data and noisy conditions. The proposed technique demonstrates outstanding performance and can be recommended to solve continuous optimization problems. |
| first_indexed | 2024-12-19T22:12:57Z |
| format | Article |
| id | doaj.art-9dcdbcd071534e4aad43d4d9f22332e0 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-12-19T22:12:57Z |
| publishDate | 2022-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-9dcdbcd071534e4aad43d4d9f22332e02022-12-21T20:03:51ZengElsevierAlexandria Engineering Journal1110-01682022-03-0161319221936A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessmentNizar Faisal Alkayem0Lei Shen1Panagiotis G. Asteris2Milan Sokol3Zhiqiang Xin4Maosen Cao5College of Civil and Transportation Engineering, Hohai University, 210098, ChinaDepartment of Engineering Mechanics, Hohai University, 210098, ChinaComputational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121 Athens, GreeceSlovak University of Technology, Faculty of Civil Engineering, Department of Structural Mechanics, Radlinského 11, 810 05 Bratislava, SlovakiaDepartment of Engineering Mechanics, Hohai University, 210098, ChinaJiangxi Province Key Laboratory of Environmental Geotechnical Engineering and Hazards Control, Jiangxi University of Science and Technology, Ganzhou 341000, China; Department of Engineering Mechanics, Hohai University, 210098, China; Corresponding author at: Nantong Ocean and Coastal Engineering Research Institute, Hohai University, Nantong 226000, China.Structural health monitoring is an important research field being investigated around the globe. In recent years, meta-heuristics are being used to solve the complex inverse problem of structural damage assessment. In this work, a novel approach depending on a new meta-heuristic and effective objective function formulation is proposed. Firstly, by considering some research shortcomings, a triple modal-based objective function combination is employed to improve the precision of damage identification. Secondly, a new self-adaptive algorithm which combines the powerful features of the stochastic fractal search with improved mechanisms into one framework, is developed. Moreover, the concept of quasi-oppositional learning is utilized to improve the overall exploration in both initial and executive stages. The new algorithm, called the self- adaptive quasi-oppositional stochastic fractal search (SA-QSFS), is benchmarked using well-known benchmark functions and applied on the IASC-ASCE FE model for damage assessment. Various damage scenarios are studied using partial modal data and noisy conditions. The proposed technique demonstrates outstanding performance and can be recommended to solve continuous optimization problems.http://www.sciencedirect.com/science/article/pii/S1110016821004592Structural damage assessmentStochastic fractal searchQuasi-oppositional learningModal features |
| spellingShingle | Nizar Faisal Alkayem Lei Shen Panagiotis G. Asteris Milan Sokol Zhiqiang Xin Maosen Cao A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment Alexandria Engineering Journal Structural damage assessment Stochastic fractal search Quasi-oppositional learning Modal features |
| title | A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment |
| title_full | A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment |
| title_fullStr | A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment |
| title_full_unstemmed | A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment |
| title_short | A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment |
| title_sort | new self adaptive quasi oppositional stochastic fractal search for the inverse problem of structural damage assessment |
| topic | Structural damage assessment Stochastic fractal search Quasi-oppositional learning Modal features |
| url | http://www.sciencedirect.com/science/article/pii/S1110016821004592 |
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