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| Efficient iterative updating inversion method based on Kriging surrogate model |
| Received:March 14, 2022 Revised:April 20, 2022 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20220314002 |
| KeyWord:parameter inversion Kriging surrogate model particle swarm optimization iterative updating |
| Author | Institution |
| 周广得 |
河海大学 工程力学系, 南京 |
| 吕小龙 |
河海大学 工程力学系, 南京 |
| 黄丹 |
河海大学 工程力学系, 南京 |
| 姜冬菊 |
河海大学 工程力学系, 南京 |
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| Abstract: |
| To improve the efficiency and accuracy of inverse analysis of massive structures like a concrete dam,as well as to reduce the computational cost of finite element analysis,a new efficient iterative updating inversion method was constructed based on the recently developed Kriging surrogate model and particle swarm optimization (PSO) algorithm.The spatial distribution of the initial sample points was determined by the Latin hypercube sampling (LHS) method,and the corresponding responses were obtained by the finite element method to produce the samples with which a roughly initial Kriging model can be constructed.The regional elastic modulus of the massive structures is determined by using a combination of the surrogate model and PSO algorithm with excellent global optimization performance.The parameters obtained from the inverse analysis are applied in the subsequent finite element analysis to get the corresponding displacements and combining them as a new sample adding to the sample set to update the surrogate model for higher accuracy.Numerical examples show that the proposed hybrid method is suitable for inverse analysis of massive structures such as a concrete dam with higher accuracy and efficiency while remarkably reducing the computational cost of normal analysis in traditional model-based inverse analysis. |