A novel evolutionary algorithm for structural model updating with a hybrid initialization and multi-stage update strategy

Measurement errors, spatial incompleteness, and parameter uncertainties frequently lead to significant deviations between the estimated correction coefficients of structural model updating and their actual values. To address the limitations, a novel multi-objective evolutionary algorithm called MOEA...

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Vydáno v:Ocean engineering Ročník 305; s. 117836
Hlavní autoři: Jiang, Yufeng, Ma, Chunke, Wang, Shuqing, Li, Yunhui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.08.2024
Témata:
Hybrid strategy
Multi-objective evolutionary algorithm
Offshore platform
Regularization technique
Sensitivity classification
Structural model updating
Offshore platform
Structural model updating
Sensitivity classification
Multi-objective evolutionary algorithm
Regularization technique
Hybrid strategy
ISSN:0029-8018, 1873-5258
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Shrnutí:Measurement errors, spatial incompleteness, and parameter uncertainties frequently lead to significant deviations between the estimated correction coefficients of structural model updating and their actual values. To address the limitations, a novel multi-objective evolutionary algorithm called MOEA-HiMs is proposed for structural model updating, which incorporates a hybrid initialization and multi-stage update strategy. Two main contributions are summarized: (a) the introduction of optimal point and elite set initialization to enhance population diversity, which is achieved through the use of Latin hypercube sampling and a regularization technique, respectively; (b) the division of the optimization process into multiple stages, where solutions are corrected to improve converging efficiency using an appropriate update strategy. Additionally, the elite set is selected through the execution of sensitivity classification using a novel mathematical sensitivity index. The effectiveness and robustness of the MOEA-HiMs are demonstrated by the numerical offshore jacket platform and an experimentally-scaled platform structure. Both numerical and experimental results prove that the novel proposed MOEA-HiMs is more effective and robust than the traditional MOEA when using limited information, even under heavy noise conditions. The algorithm can serve as preliminary methods for online structural model updating of offshore platforms. •An evolutionary algorithm that incorporates a hybrid initialization and multi-stage update strategy (MOEA-HiMs) is proposed.•A normalized sensitivity index is proposed and K-means clustering analysis is performed for sensitivity classification.•Numerical and experimental results demonstrate that the MOEA-HiMs is more effective and robust compared to the MOEA.•The MOEA-HiMs significantly enhances the precision and accuracy of the correction coefficients that have lower sensitivities.•The MOEA-HiMs effectively solves ill-conditioned problems in structural model updating that are caused by measured noises.
ISSN:0029-8018
1873-5258
DOI:10.1016/j.oceaneng.2024.117836