Multi-Scale Finite Element Model Update Method Based on a Multi-Objective Evolutionary Algorithm

HU Wenzhe; CUI Chuang; WANG Hao; ZHANG Qinghua

doi:10.13204/j.gyjzG22061306

Volume 53 Issue 8

Aug. 2023

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HU Wenzhe, CUI Chuang, WANG Hao, ZHANG Qinghua. Multi-Scale Finite Element Model Update Method Based on a Multi-Objective Evolutionary Algorithm[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(8): 161-167. doi: 10.13204/j.gyjzG22061306

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Multi-Scale Finite Element Model Update Method Based on a Multi-Objective Evolutionary Algorithm

doi: 10.13204/j.gyjzG22061306

Department of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received Date: 2022-06-13
Available Online: 2023-10-17

Abstract

Abstract

Determining the current state of long-span bridges and clarifying the force characteristics of key parts are the prerequisites for evaluating their performance degradation and remaining life. A multi-scale surrogate model for long-span bridges was established by using the Kriging metamodel, and a large-span bridge model update method was developed by using a multi-objective evolutionary algorithm and an evolutionary control algorithm. Taking a typical long-span cable-stayed bridge as an example, a multi-scale finite element model of the long-span cable-stayed bridge was established. The overall structure of the global model adopted beam elements and local shell elements, and the boundary conditions were satisfied by using the multi-point constraint method (MPC). Based on the measured data of the health monitoring system, the natural vibration frequency, displacement response and stress response of the model were corrected, and the corrected influence line was obtained. The results showed that the global and local index correction results obtained by the multi-objective evolutionary algorithm were in good agreement with the measured data. Compared with the initial finite element calculation values, the average relative error of the natural frequency was reduced by 3.38%, and the average relative error of the displacement response was relatively high. The error was reduced by 10% and the average relative error of the stress response was reduced by 5%. In general, the Kriging metamodel and the multi-objective evolutionary algorithm could be used to correct and update the multi-scale model of long-span bridges.
- long-span bridge,
- multi-scale finite element model correction,
- Kriging metamodel,
- R2-MOEA multi-objective optimization,
- evolutionary control

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References(21)

References

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