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. 2023 Nov 14;23(22):9185.
doi: 10.3390/s23229185.

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

Haifang He  1 , Baojun Zeng  2 , Yulong Zhou  1 , Yuanyuan Song  3 , Tianneng Zhang  3 , Han Su  3 , Jian Wang  4
Affiliations

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

Haifang He et al. Sensors (Basel). .

Abstract

Aging, corrosive environments, and inadequate maintenance may result in performance deterioration of civil infrastructures, and finite element model updating is a commonly employed structural health monitoring procedure in civil engineering to reflect the current situation and to ensure the safety and serviceability of structures. Using the finite element model updating process to obtain the relationship between the structural responses and updating parameters, this paper proposes a method of using the wavelet neural network (WNN) as the surrogate model combined with the wind-driven optimization (WDO) algorithm to update the structural finite element model. The method was applied to finite element model updating of a continuous beam structure of three equal spans to verify its feasibility, the results show that the WNN can reflect the nonlinear relationship between structural responses and the parameters and has an outstanding simulation performance; the WDO has an excellent ability for optimization and can effectively improve the efficiency of model updating. Finally, the method was applied to update a real bridge model, and the results show that the finite element model update based on WDO and WNN is applicable to the updating of a multi-parameter bridge model, which has practical significance in engineering and high efficiency in finite element model updating. The differences between the updated values and measured values are all within the range of 5%, while the maximum difference was reduced from -10.9% to -3.6%. The proposed finite element model updating method is applicable and practical for multi-parameter bridge model updating and has the advantages of high updating efficiency, reliability, and practical significance.

Keywords: bridge; finite element model updating; surrogate model; wavelet neural network; wind-driven optimization.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Network structure of WNN.
Figure 2
Figure 2
Combination of WDO and WNN.
Figure 3
Figure 3
The friction force (Fg) and pressure gradient force (Fp) in the one-dimensional coordinate system.
Figure 4
Figure 4
Calculation flow of WDO.
Figure 5
Figure 5
The structural representation of the finite element model.
Figure 6
Figure 6
The training process of WNN.
Figure 7
Figure 7
The iterative curve of WDO.
Figure 8
Figure 8
The elevation of Ningbo Bund Bridge.
Figure 9
Figure 9
Finite element model of Ningbo Bund Bridge.
Figure 10
Figure 10
The schematic diagram of the triangular inclined tower structure.
Figure 11
Figure 11
First three-order mode shapes of the bridge.
Figure 12
Figure 12
Sensitivity analysis of updating parameters.
Figure 13
Figure 13
The training process of WNN.
Figure 14
Figure 14
The iterative curve of WDO.
See this image and copyright information in PMC

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