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Finite Element Model Updating for a Continuous Beam-Arch Composite Bridge Based on the RSM and a Nutcracker

Weihua Zhou1, Hongyin Yang1,2, Jing Hao1

  • 1School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, China.

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|August 14, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a new finite element (FE) model updating framework using ambient vibration data to improve bridge safety assessments. The proposed method significantly enhances FE model accuracy and demonstrates faster convergence than other optimization algorithms.

Keywords:
continuous beam–arch composite bridgefinite element model updatingnutcracker optimization algorithmresponse surface methodstochastic subspace identification

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Area of Science:

  • Civil Engineering
  • Structural Health Monitoring
  • Computational Mechanics

Background:

  • Accurate finite element (FE) models are crucial for civil engineering structure safety assessments.
  • Obtaining reliable FE model parameters for existing bridges is challenging without disruptive static load tests.
  • Ambient vibration data offers a non-disruptive alternative for structural parameter identification.

Purpose of the Study:

  • To develop and validate a novel FE model updating framework for existing bridges.
  • To integrate ambient vibration data with advanced optimization techniques for enhanced model accuracy.
  • To improve the reliability of structural safety assessments through precise FE model parameter calibration.

Main Methods:

  • Utilized stochastic subspace identification to extract natural frequencies from ambient vibration data.
  • Employed the response surface method to approximate the complex FE model.
  • Integrated the nutcracker optimization algorithm (NOA) for efficient and accurate FE model parameter updating.

Main Results:

  • The proposed framework reduced the average frequency error from 5.58% to 2.75% in a case study bridge.
  • The nutcracker optimization algorithm (NOA) demonstrated superior convergence speed, achieving results in 13 iterations compared to others.
  • The NOA significantly outperformed whale optimization and grey wolf optimizers in reducing the first transverse vibration frequency error.

Conclusions:

  • The developed FE model updating framework effectively enhances structural model accuracy using ambient vibration data.
  • The nutcracker optimization algorithm (NOA) offers a computationally efficient and accurate solution for FE model updating.
  • This approach provides a reliable, non-disruptive method for assessing the safety of existing bridges.