Finite element model updating for large span spatial steel structure considering uncertainties

Abstract

In order to establish the baseline finite element model for structural health monitoring, a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element model. In the new method, the finite element model was replaced by the multi-output support vector regression machine (MSVR). The interval variables of the measured frequency were sampled by Latin hypercube sampling method. The samples of frequency were regarded as the inputs of the trained MSVR. The outputs of MSVR were the target values of design parameters. The steel structure of National Aquatic Center for Beijing Olympic Games was introduced as a case for finite element model updating. The results show that the proposed method can avoid solving the problem of complicated calculation. Both the estimated values and associated uncertainties of the structure parameters can be obtained by the method. The static and dynamic characteristics of the updated finite element model are in good agreement with the measured data.

Finite element model updating for the Tsing Ma Bridge tower based on surrogate models

“…According to Teng et al, 10 the main steps of model updating based on the SVR method are as follows: (1) Constructing the samples: More precisely, the samples of the design parameters can be determined based on any experimental design technique, while the responses of the structure can be obtained by FE analysis for each sample. (2) Training the SVR model: The SVR model is trained with the responses and the corresponding design parameters, considered as the inputs and the outputs, respectively.…”

“…However, the relationship between the factors and the responses is not linear. The obtained RS mathematical equations (quadratic polynomials) of the first natural frequency based on CCD and BBD are shown in equations (10) and (11), respectively…”

“…Currently, three surrogate modelbased optimization methods have been applied in the field of civil FE model updating including the response surface (RS) method, the support vector regression (SVR) method, and the radial basis function neural network (RBFNN) method. The efficiency of the RS method have been demonstrated in bridge model updating by Deng and Cai, 6 Ren et al, 7 Ren and Chen, 8 and Zhou et al 9 According to the literatures, [10][11][12][13] the SVR method is efficient for FE model updating and system identifications of large civil structures. The RBFNN method is also efficient in the parameter identifications of the bridge bearings.…”

“…Besides the employed surrogate model, the experimental design technique used for sampling also influences the surrogate model-based model updating results. 1 Deng and Cai 6 and Zhou et al 9 used the central composite design (CCD) method, while Ren et al 7 and Teng et al 10 used the uniform design method. Which experimental design method can provide sufficient information required for establishing the accurate surrogate model for effective model updating with the minimum sampling workload?…”

“…However, the SVR method is a machine learning method based on statistical learning theory, and the RBFNN method employs artificial intelligence models. Teng et al 10 stated that the advantages of the SVR method are the fast convergence speed, the good prediction ability under a small number of samples, and the adaptability to the nonlinear model of complex relationships with higher precision. The RBFNN method has the advantages of the convenient network training, the ability to avoid local minimum, and the capacity to precisely fit any function, provided that neurons are sufficient in the radial basic level.…”