Finite Element Model Updating of Tower Cranes Based on the Non-dominated Sorting Genetic Algorithm

doi:10.12068/j.issn.1005-3026.2018.07.021

Abstract

Abstract: A finite element model updating method based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was proposed， which ensures that the established finite element model can accurately reflect the actual state of the structure. Firstly， the finite element model was established， and the effective response surface substitution model was obtained based on the quadratic polynomial response surface method. Then the response surface model was updated with NSGA-Ⅱ. Finally a reliable finite element model was obtained which can satisfy the requirements of engineering precision. An engineering example of a tower crane’s finite element model updating was provided. In accordance with the measured data， the results indicated that the multi-objective optimization algorithm based on NSGA-Ⅱ has an ideal effect for the finite element model updating， and the updated finite element model can accurately reflect the mechanical properties of the structure.

Key words: model updating, quadratic polynomial, response surface method, non-dominated sorting genetic algorithm, multi-objective optimization

CLC Number:

TH213

QIN Xian-rong， ZHANG Qing， LIU Chao， XU Jian. Finite Element Model Updating of Tower Cranes Based on the Non-dominated Sorting Genetic Algorithm[J]. Journal of Northeastern University Natural Science, 2018, 39(7): 1017-1021.

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