Structural damage prediction based on Bayesian updating with Monte Carlo sampling

Abstract

A series of engineering accidents in recent years have caused people to worry about the safety of engineering structures. As buildings continue to age, engineering accidents are increasing, and it is necessary to monitor their health. Bayesian updating can determine the probability of damage to a structure by collecting a posteriori information, and can more accurately estimate the location and extent of damage, thereby predicting the risk of structural damage and repairing the damage in time, significantly improving engineering efficiency. This paper attempts to update the information of the simulated structure based on the Bayesian updating method to obtain a more accurate posterior distribution of the potentially damaged components to demonstrate the feasibility and superiority of the technique. This paper finds that Bayesian updating can be used in conjunction with modeling software such as SMsolver. By constructing a structural model with SMsolver and processing the data with Bayesian updating, it is possible to calculate the damage problems of the structure accurately. At the same time, this paper also uses Monte Carlo sampling technology in combination with the Bayesian updating method to explore the prediction effect of structural damage. Using Monte Carlo sampling methods, the failure probability and damage location of the structure can be effectively estimated under the condition of known prior information, thereby improving production efficiency and the accuracy of structural inspection.