Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator

Abstract

Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to real measurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducible way such that other researcher can use this setup to validate their methodologies.

Data assimilation for online model calibration in discrete event simulation

“…18 The work of Ward et al 19 presented a particle filter-based method for continuous calibration of a digital twin model and compared its performance with static and sequential Bayesian calibration approaches. Another work by Titscher et al 20 developed a Bayesian calibration method and applied it to online model calibration using real measurement data from a lab-based demonstrator bridge. None of these works focused on online model calibration for discrete event simulations.…”

A Bayesian Framework for Simulation‐based Digital Twins of Bridges

“…In cases where the structural model presents a heterogeneous material behaviour, Bayesian inference methods are used to obtain the parameters of the representation of the material variability 14 . For the case of bridges, SHM systems are calibrated following this methodology 15 , bridge model parameters are obtained using data from measurement campaigns 16 and digital twins are tuned to represent the response of a bridge in real time 17 .…”