# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
###### 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. finite element model updating; wavelet neural network; wind-driven optimization; surrogate model; bridge 202  
strong fitting ability, simple structure, and broad applicability and is combined with an optimization algorithm with strong global search ability and high efficiency to iteratively optimize the updating parameters. Thus, this method simplifies the model updating process and improves the efficiency of the updating work, and it is the most widely used method in the field of model updating [11; 12; 13; 14]. The surrogate models commonly used in structural finite element model updating mainly include the polynomial response surface model [15; 16; 17], radial basis function (RBF) model [18; 19], Kriging model [20], and neural network model [21; 22; 23; 24; 25]. [PERSON] [26] proposed a bridge structure finite element model updating method based on the sparrow search algorithm (SSA) and the polynomial response surface method, and by updating the model of the high-dimensional locally damaged cantilever beam, the feasibility of the proposed method was verified. To more reasonably determine the number of node points of the Kriging surrogate model and to improve the prediction accuracy of the Kriging model for the extreme value region of the objective function, [PERSON] [27] proposed an adaptive Kriging model and then applied it to the finite element model updating of bridge structures; the first-order frequency response surfaces of standard Kriging and adaptive Kriging fitting were established, and the finite element model updating of the two models was compared: The results show that the adaptive Kriging model can obtain better updating results. [PERSON] [28] also combined the Kriging surrogate model with the improved gravity search algorithm (GSA), updated the initial finite element model using the load test data of the Nanzhonghuan Bridge, and obtained a reliable structural benchmark finite element model.