Digital twin-based approaches for structural health monitoring (SHM) and damage prognosis (DP) are emerging as a powerful framework for intelligent maintenance of civil structures and infrastructure systems. Model updating of nonlinear mechanics-based Finite Element (FE) models using input and output measurement data with advanced Bayesian inference methods is an effective way of constructing a digital twin. In this regard, the nonlinear FE model updating of a full-scale reinforced-concrete bridge column subjected to seismic excitations applied by a large shake table is considered in this paper. This bridge column, designed according to US seismic design provisions, was tested on the NEES@UCSD Large High-Performance Outdoor Shake Table (LHPOST). The column was subjected to a sequence of ten recorded earthquake ground motions and was densely instrumented with an array of 278 sensors consisting of strain gauges, linear and string potentiometers, accelerometers and Global Positioning System (GPS) based displacement sensors to measure local and global responses during testing. This heterogeneous dataset is used to estimate/update the material and damping parameters of the developed mechanics-based distributed plasticity FE model of the bridge column. The sequential Monte Carlo (SMC) method (set of advanced simulation-based Bayesian inference methods) is used herein for the model updating process. The inherent architecture of SMC methods allows for parallel model evaluations, which is ideal for updating computationally expensive models.
|Title of host publication||Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics, 2020|
|Number of pages||9|
|State||Published - 2020|
|Event||38th IMAC, A Conference and Exposition on Structural Dynamics, 2020 - Houston, United States|
Duration: 10 Feb 2020 → 13 Feb 2020
|Name||Conference Proceedings of the Society for Experimental Mechanics Series|
|Conference||38th IMAC, A Conference and Exposition on Structural Dynamics, 2020|
|Period||10/02/20 → 13/02/20|
Bibliographical noteFunding Information:
for this work was provided by the U.S. Army Corps of Engineers through the U.S. Army Engineer Research and Development Center Research Cooperative Agreement W912HZ-17-2-0024.
© 2020, The Society for Experimental Mechanics, Inc.
- Bayesian inference
- Digital twin
- Finite element
- Full-scale structural systems
- Model updating
- Sequential Monte Carlo
- Structural health monitoring