Adjustable hybrid resampling approach to computationally efficient probabilistic inference of structural damage based on vibration measurements

Abstract

This paper presents a novel Adjustable Hybrid Resampling (AHR) approach to deriving samples representing probabilistic distributions of unknown damage indexes through Bayesian inference based on vibration measurements. The AHR is motivated by the need for reducing computational cost in identifying damage in real-world structures and evaluating its associated uncertainties, and is developed through alternative implementation of two newly developed resampling processes—the reserved sample resampling (RSR) and the direct resampling (DR)—in the transitional Markov chain Monte Carlo (TMCMC) simulation for Bayesian inference. The AHR is able to rapidly identify important regions where samples highly populate, and have the capacity to transform samples to more optimal positions, achieving the desired trade-off between computational expense and accuracy in an adjustable manner controlled by decision-makers. The proposed AHR approach and its efficiency and accuracy are illustrated and examined through identification of damage in truss joints and members by using simulated measurements. The limitation of this study and future research direction in this regard is also discussed.