Stochastic model updating based on sub-interval similarity and BP neural network

A stochastic model updating framework is proposed in this work to address the problem of uncertain model calibration. This framework includes an effective uncertainty quantification metric of sub-interval similarity to measure the discrepancy between model predictions and experimental observations. A back propagation neural network is employed as a surrogate model for finite element method models, and a sparrow search algorithm is introduced as an optimization operator. Two typical numerical examples of a 3-degree-of-freedom mass-spring system and a satellite finite element model have been presented to demonstrate the feasibility and the effectiveness of the proposed stochastic model updating algorithm.