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Abstract

To achieve accurate prediction and analysis of the structural behavior of long-span suspension bridges, a finite element model updating method is proposed. An optimized radial basis function neural network (RBFNN) is constructed using a genetic algorithm to map the relationship between structural responses and parameters. Using structural dynamic responses as inputs, the model updating was performed on a long-span suspension bridge using both numerical simulation data and actual data from a health monitoring system. The results show that the optimized RBFNN effectively represents the functional relationship between structural responses and parameters to be updated. Model updating via the optimized RBFNN significantly enhances the accuracy of finite element model calculations. After updating based on numerical simulation data, the calculation error was reduced to within 1.5%. When dynamic characteristics from the health monitoring system were used as feature data, the optimized RBFNN effectively updated the suspension bridge model. The maximum calculation error was reduced from 20.37% before updating to within 7%, achieving a maximum error reduction of 77.91%.

Publication Date

5-11-2023

DOI

10.14048/j.issn.1671-2579.2023.02.014

First Page

80

Last Page

84

Submission Date

March 2025

Reference

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