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

Abstract

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.

Keywords:

• Sub-interval similarity
• back propagation neural network
• stochastic model updating
• sparrow search algorithm
• uncertainty quantification
• model calibration

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the Postdoctoral Research Foundation of Shunde Graduate School of the University of Science and Technology Beijing (2021BH012) and the Fundamental Technical Project (JSZL2020203B001).