Load Identification and Damage Detection by a Digital Twin of a Bridge applying Bayesian Model Calibration
A digital twin for decision making during the life cycle of civil infrastructure is a promising concept combining simulation models with corresponding structure-specific sensor data to support maintenance decisions or to investigate the reliability. The sensor data quality as well as the model quality of the digital twin, comprising on modeling assumptions and correct model parameters strongly influences the prognosis results.
The challenges of applying the digital twin concept (Fig. 1 (a)) using real measurement data and systems are illustrated by this laboratory-scale demonstrator bridge (Fig. 1), 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 scenarios investigated include both the iterative identification of the structural model parameters and scenarios regarding damage identification. All models and data are aimed to be in a reproducible way such that this setup can be used by other researchers to validate their methodologies.
Owner
Focus Area: Transport infrastructures
Department 7: Safety of Structures
Fed. Inst. Of Mat. Res. & Testing (BAM), Berlin
Types of sensing
Laser triangulation displacement sensors; load cells; DIC displacement measurements
Loading type
Small self-driving vehicle with different speed and weight
Access to externals
Please contact: sebastian.degener@bam.de | ralf.herrmann@bam.de | joerg.unger@bam.de
Implementation of additional sensors
Please contact: sebastian.degener@bam.de | ralf.herrmann@bam.de
Type of data available
CAD; FE model; software; measured data sets
Availability of recorded data to externals
Please contact: sebastian.degener@bam.de | ralf.herrmann@bam.de
Please use this publication for further information
Titscher T, van Dijk T, Kadoke D, Robens-Radermacher A, Herrmann R, Unger JF. Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator. Engineering Reports. 2023;5(11):e12669. doi: 10.1002/eng2.12669
https://doi.org/10.1002/eng2.12669
