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Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization.

Haifang He1, Baojun Zeng2, Yulong Zhou1

  • 1National Engineering Laboratory of Bridge Safety and Technology (Beijing), Research Institute of Highway Ministry of Transport, Beijing 100088, China.

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|November 25, 2023
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Summary
This summary is machine-generated.

A new method uses wavelet neural networks (WNN) and wind-driven optimization (WDO) to update finite element models for civil infrastructure, improving structural health monitoring and ensuring safety.

Keywords:
bridgefinite element model updatingsurrogate modelwavelet neural networkwind-driven optimization

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Area of Science:

  • Civil Engineering
  • Structural Health Monitoring
  • Computational Mechanics

Background:

  • Civil infrastructure performance deteriorates due to aging, corrosion, and poor maintenance.
  • Finite element model updating is crucial for structural health monitoring and ensuring safety.
  • Accurate models are needed to reflect the current state of structures.

Purpose of the Study:

  • To propose an efficient method for finite element model updating.
  • To enhance structural health monitoring using advanced computational techniques.
  • To validate the method on a real-world bridge model.

Main Methods:

  • Utilizing wavelet neural networks (WNN) as surrogate models.
  • Employing the wind-driven optimization (WDO) algorithm for parameter updating.
  • Applying the combined WNN-WDO method to a continuous beam and a real bridge model.

Main Results:

  • WNN effectively captures nonlinear relationships between structural responses and parameters.
  • WDO significantly improves the efficiency and accuracy of finite element model updating.
  • The method successfully updated a multi-parameter bridge model with differences within 5%.

Conclusions:

  • The WNN-WDO method is a practical and efficient approach for multi-parameter bridge model updating.
  • This method offers high reliability and practical significance for engineering applications.
  • The study demonstrates a robust solution for structural health monitoring and performance assessment.