Metamodel-assisted hybrid optimization strategy for model updating using vibration response data
- Author
- Yifei Li, Cao MaoSen, Ngoc Hoa Tran, Samir Khatir (UGent) , Minh Hoang Le, Thanh Sang To, Thanh Cuong Le and Magd Abdel Wahab (UGent)
- Organization
- Abstract
- In this study, an effective and novel method, termed Metamodel Assisted Hybrid of Particle Swarm Optimization with Genetic Algorithm (MA-HPSOGA), is developed to identify unknown structural dynamic parameters. The method first constructs four popular metamodels to substitute the computationally expensive numerical analysis based on the Latin hypercube sampling method and probabilistic finite element analysis, and their accuracy is assessed by R-squared. Subsequently, a suitable and low-cost metamodel is selected in combination with a hybrid optimization strategy by incorporating Genetic Algorithm (GA) into Particle Swarm Optimization (PSO). Two examples with measured vibration response data and different levels of complexity are used to verify the effectiveness and practicality of the presented method. The results showed that polynomial chaos expansion assisted HPSOGA has the highest computational efficiency and accuracy in the four coupled methods. Besides, compared to the conventional iteration-based dynamic parameter identification methods, the presented method shows an overwhelming advantage in terms of computational efficiency. Furthermore, the performance of HPSOGA is compared with its sub-algorithms, showing that the hybrid strategy offers faster convergence and stronger robustness. Our findings reveal that the MA-HPSOGA may be used as a promising method for achieving high-efficiency model updating in large-scale complex structures.
- Keywords
- General Engineering, Software, Vibration response, Probabilistic finite element analysis, Dynamic parameter identification, Hybrid optimization strategy, Metamodel
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01H4N1QRJVFKD2XCS0K92EC5WZ
- MLA
- Li, Yifei, et al. “Metamodel-Assisted Hybrid Optimization Strategy for Model Updating Using Vibration Response Data.” ADVANCES IN ENGINEERING SOFTWARE, vol. 185, 2023, doi:10.1016/j.advengsoft.2023.103515.
- APA
- Li, Y., MaoSen, C., Tran, N. H., Khatir, S., Le, M. H., To, T. S., … Abdel Wahab, M. (2023). Metamodel-assisted hybrid optimization strategy for model updating using vibration response data. ADVANCES IN ENGINEERING SOFTWARE, 185. https://doi.org/10.1016/j.advengsoft.2023.103515
- Chicago author-date
- Li, Yifei, Cao MaoSen, Ngoc Hoa Tran, Samir Khatir, Minh Hoang Le, Thanh Sang To, Thanh Cuong Le, and Magd Abdel Wahab. 2023. “Metamodel-Assisted Hybrid Optimization Strategy for Model Updating Using Vibration Response Data.” ADVANCES IN ENGINEERING SOFTWARE 185. https://doi.org/10.1016/j.advengsoft.2023.103515.
- Chicago author-date (all authors)
- Li, Yifei, Cao MaoSen, Ngoc Hoa Tran, Samir Khatir, Minh Hoang Le, Thanh Sang To, Thanh Cuong Le, and Magd Abdel Wahab. 2023. “Metamodel-Assisted Hybrid Optimization Strategy for Model Updating Using Vibration Response Data.” ADVANCES IN ENGINEERING SOFTWARE 185. doi:10.1016/j.advengsoft.2023.103515.
- Vancouver
- 1.Li Y, MaoSen C, Tran NH, Khatir S, Le MH, To TS, et al. Metamodel-assisted hybrid optimization strategy for model updating using vibration response data. ADVANCES IN ENGINEERING SOFTWARE. 2023;185.
- IEEE
- [1]Y. Li et al., “Metamodel-assisted hybrid optimization strategy for model updating using vibration response data,” ADVANCES IN ENGINEERING SOFTWARE, vol. 185, 2023.
@article{01H4N1QRJVFKD2XCS0K92EC5WZ,
abstract = {{In this study, an effective and novel method, termed Metamodel Assisted Hybrid of Particle Swarm Optimization with Genetic Algorithm (MA-HPSOGA), is developed to identify unknown structural dynamic parameters. The method first constructs four popular metamodels to substitute the computationally expensive numerical analysis based on the Latin hypercube sampling method and probabilistic finite element analysis, and their accuracy is assessed by R-squared. Subsequently, a suitable and low-cost metamodel is selected in combination with a hybrid optimization strategy by incorporating Genetic Algorithm (GA) into Particle Swarm Optimization (PSO). Two examples with measured vibration response data and different levels of complexity are used to verify the effectiveness and practicality of the presented method. The results showed that polynomial chaos expansion assisted HPSOGA has the highest computational efficiency and accuracy in the four coupled methods. Besides, compared to the conventional iteration-based dynamic parameter identification methods, the presented method shows an overwhelming advantage in terms of computational efficiency. Furthermore, the performance of HPSOGA is compared with its sub-algorithms, showing that the hybrid strategy offers faster convergence and stronger robustness. Our findings reveal that the MA-HPSOGA may be used as a promising method for achieving high-efficiency model updating in large-scale complex structures.}},
articleno = {{103515}},
author = {{Li, Yifei and MaoSen, Cao and Tran, Ngoc Hoa and Khatir, Samir and Le, Minh Hoang and To, Thanh Sang and Le, Thanh Cuong and Abdel Wahab, Magd}},
issn = {{0965-9978}},
journal = {{ADVANCES IN ENGINEERING SOFTWARE}},
keywords = {{General Engineering,Software,Vibration response,Probabilistic finite element analysis,Dynamic parameter identification,Hybrid optimization strategy,Metamodel}},
language = {{eng}},
pages = {{12}},
title = {{Metamodel-assisted hybrid optimization strategy for model updating using vibration response data}},
url = {{http://doi.org/10.1016/j.advengsoft.2023.103515}},
volume = {{185}},
year = {{2023}},
}
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