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Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm

Yifei Li, Minh Hoang Le, Samir Khatir (UGent) , Thanh Sang To, Thanh Cuong Le, Cao MaoSen and Magd Abdel Wahab (UGent)
(2023) ENGINEERING STRUCTURES. 283.
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Abstract
Structural damage identification plays a crucial role in structural health monitoring. In this study, a novelty method for structural damage identification is developed, which employs an advanced surrogate modelling technique to drive a new hybrid optimization strategy, namely a combination of K-means clustering optimizer and genetic algorithm (HKOGA). The core of this method is using the reliable sparse polynomial chaos expansion model as a cost-effective substitute for the computationally expensive structural finite element models, thus greatly improving the efficiency of the optimization strategy in finding the optimal value of the objective function. To evaluate the performance of this hybrid optimization strategy, seven optimization algorithms are selected and compared with it for 23 classical benchmark functions, and the comparative results show that the HKOGA has the best performance. Taking a small-scaled laboratory dam as an example, the efficiency and reliability of the proposed method to cope with the problems concerning finite element model updating and structural damage identification are explored. Two important findings are as follows. (i) For finite element model updating, compared to the conventional method based on iterative optimization, the proposed method improves computational efficiency by a factor of 59 while maintaining computational accuracy. (ii) For structural damage identification, leaving aside the huge leap in computational efficiency, the HKOGA has a faster convergence rate, stronger robustness, and higher accuracy than its sub-algorithm K-means clustering optimizer (KO). The results show that this method can be severed as an extremely efficient and potential tool to identify damage in large and complex structures.
Keywords
Civil and Structural Engineering, Structural damage identification, Sparse polynomial chaos expansion, Hybrid optimization strategy, Model updating, Laboratory dam, MODAL STRAIN-ENERGY, DIFFERENTIAL EVOLUTION, REGRESSION

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Citation

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MLA
Li, Yifei, et al. “Structure Damage Identification in Dams Using Sparse Polynomial Chaos Expansion Combined with Hybrid K-Means Clustering Optimizer and Genetic Algorithm.” ENGINEERING STRUCTURES, vol. 283, 2023, doi:10.1016/j.engstruct.2023.115891.
APA
Li, Y., Le, M. H., Khatir, S., To, T. S., Le, T. C., MaoSen, C., & Abdel Wahab, M. (2023). Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm. ENGINEERING STRUCTURES, 283. https://doi.org/10.1016/j.engstruct.2023.115891
Chicago author-date
Li, Yifei, Minh Hoang Le, Samir Khatir, Thanh Sang To, Thanh Cuong Le, Cao MaoSen, and Magd Abdel Wahab. 2023. “Structure Damage Identification in Dams Using Sparse Polynomial Chaos Expansion Combined with Hybrid K-Means Clustering Optimizer and Genetic Algorithm.” ENGINEERING STRUCTURES 283. https://doi.org/10.1016/j.engstruct.2023.115891.
Chicago author-date (all authors)
Li, Yifei, Minh Hoang Le, Samir Khatir, Thanh Sang To, Thanh Cuong Le, Cao MaoSen, and Magd Abdel Wahab. 2023. “Structure Damage Identification in Dams Using Sparse Polynomial Chaos Expansion Combined with Hybrid K-Means Clustering Optimizer and Genetic Algorithm.” ENGINEERING STRUCTURES 283. doi:10.1016/j.engstruct.2023.115891.
Vancouver
1.
Li Y, Le MH, Khatir S, To TS, Le TC, MaoSen C, et al. Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm. ENGINEERING STRUCTURES. 2023;283.
IEEE
[1]
Y. Li et al., “Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm,” ENGINEERING STRUCTURES, vol. 283, 2023.
@article{01GTRWPN3RD6PECHS10P19BB81,
  abstract     = {{Structural damage identification plays a crucial role in structural health monitoring. In this study, a novelty method for structural damage identification is developed, which employs an advanced surrogate modelling technique to drive a new hybrid optimization strategy, namely a combination of K-means clustering optimizer and genetic algorithm (HKOGA). The core of this method is using the reliable sparse polynomial chaos expansion model as a cost-effective substitute for the computationally expensive structural finite element models, thus greatly improving the efficiency of the optimization strategy in finding the optimal value of the objective function. To evaluate the performance of this hybrid optimization strategy, seven optimization algorithms are selected and compared with it for 23 classical benchmark functions, and the comparative results show that the HKOGA has the best performance. Taking a small-scaled laboratory dam as an example, the efficiency and reliability of the proposed method to cope with the problems concerning finite element model updating and structural damage identification are explored. Two important findings are as follows. (i) For finite element model updating, compared to the conventional method based on iterative optimization, the proposed method improves computational efficiency by a factor of 59 while maintaining computational accuracy. (ii) For structural damage identification, leaving aside the huge leap in computational efficiency, the HKOGA has a faster convergence rate, stronger robustness, and higher accuracy than its sub-algorithm K-means clustering optimizer (KO). The results show that this method can be severed as an extremely efficient and potential tool to identify damage in large and complex structures.}},
  articleno    = {{115891}},
  author       = {{Li, Yifei and Le, Minh Hoang and Khatir, Samir and To, Thanh Sang and Le, Thanh Cuong and MaoSen, Cao and Abdel Wahab, Magd}},
  issn         = {{0141-0296}},
  journal      = {{ENGINEERING STRUCTURES}},
  keywords     = {{Civil and Structural Engineering,Structural damage identification,Sparse polynomial chaos expansion,Hybrid optimization strategy,Model updating,Laboratory dam,MODAL STRAIN-ENERGY,DIFFERENTIAL EVOLUTION,REGRESSION}},
  language     = {{eng}},
  pages        = {{20}},
  title        = {{Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm}},
  url          = {{http://doi.org/10.1016/j.engstruct.2023.115891}},
  volume       = {{283}},
  year         = {{2023}},
}
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