Substructure approach for Bayesian probabilistic model updating using response reconstruction technique

In recent years, several substructural identification methods have been developed for structural health monitoring. Most of these methods are deterministic, and unknown parameters in the target can be identified. However, the uncertainties in the identified results cannot be evaluated. This paper presents a Bayesian probabilistic model updating approach for substructure identifi-cation. A new response reconstruction technique is explored and combined with the Bayesian inference method for probabilistic model updating of the target substructure. The large-scale structure was divided into substructures, and the uncertainties in the identified results were evaluated. The stochastic gradient descent method is proposed for estimating the maximum likelihood estimation and maximum a posteriori of the unknown parameters in the target sub-structure. The posterior distributions of the unknown parameters are estimated using an asymptotic approximation. Numerical studies on a three-span beam structure and experimental studies on an eight-floor steel frame were conducted to verify the accuracy and efficiency of the proposed method. The results show that the estimated results match the actual values, and reasonable standard deviations can be obtained.

Automated summary

This study proposes a Bayesian probabilistic model updating approach for substructure identification, which evaluates the uncertainties in identified results by analyzing the responses of large-scale structures. Numerical experiments on a three-span beam structure and experimental studies on an eight-floor steel frame were conducted to verify the accuracy and efficiency of the proposed method, and the results demonstrate its effectiveness.