Bayesian model-based damage detection in engineering systems using Invertible Neural Networks

Abstract

This study explores the application of BayesFlow, a state-of-the-art deep learning technique for approximate Bayesian inference, in the context of Structural Health Monitoring (SHM), and specifically, model-based damage detection. BayesFlow diverges from other likelihood-free methods by not relying on predefined summary statistics, but learning optimal ones from the simulations via invertible neural networks. It is also capable of performing almost instantaneous inference for an arbitrary number of datasets, which is ideal for real-time applications such as bridge monitoring systems. The effectiveness of the proposed approach is demonstrated with a small scale bridge use case with synthetic measurements for different testing scenarios. Across all sub-cases, BayesFlow is able to determine both the damage location and severity with a high accuracy, while the credible intervals are close to those obtained under an exact Bayesian inference approach with the true likelihood.