Architecture design of the Bayesian deep neural network in structural model updating

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (6) : 184-190.

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Architecture design of the Bayesian deep neural network in structural model updating

HE Yuxuan,YIN Tao*,WANG Xi

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Abstract

Bayesian Neural Network (BNN) generally has stronger noise robustness than ordinary neural networks, and has gradually attracted attention in the fields of structural system identification and health monitoring. Currently, relevant literature in this field mainly focuses on the application and architecture design of single-hidden-layer BNN. Multi-hidden-layer architectures with a certain depth usually have stronger generalization capabilities in fitting complex high-dimensional data than single-hidden-layer ones, but research on the optimal design of multi-hidden-layer BNN architectures has not yet been reported in the current literature. This paper aims to carry out optimal architecture design on multi-hidden-layer BNN combined with finite element (FE) model updating problems. A quantitative measure of multi-hidden-layer BNN performance based on evidence logarithm is developed, and an efficient algorithm is also proposed to simultaneously configure the number of neurons in each hidden layer to achieve an optimal architecture design solution of multi-hidden-layer BNN for model updating problems. The correctness and effectiveness of the proposed method are verified by refining the initial FE model of a large-span steel pedestrian bridge utilizing field measured data.

Key words

Structural system identification / Structural health monitoring / FE model updating / Bayesian deep neural network / Optimal architecture design

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HE Yuxuan, YIN Tao, WANG Xi. Architecture design of the Bayesian deep neural network in structural model updating[J]. Journal of Vibration and Shock, 2025, 44(6): 184-190

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