Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference

Model updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for exam...

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Bibliographic Details
Published in:Computer-aided civil and infrastructure engineering 2023-02, Vol.38 (3), p.353-371
Main Authors: Yuan, Ye, Au, Francis T. K., Yang, Dong, Zhang, Jing
Format: Article
Language:English
Subjects:
Active learning
Algorithms
Aluminum
Bayesian analysis
Damage assessment
Damage detection
Machine learning
Mathematical models
Model updating
Numerical models
Robustness (mathematics)
Statistical inference
Structural health monitoring
Structural models
Uncertainty
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Summary:Model updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for example, Kriging approach, to reduce the computations, while others have quantified uncertainties with Bayesian inference. It is desirable to further improve the efficiency and robustness of the Kriging‐based model updating approach and analytically evaluate its uncertainties. An active learning structural model updating method is proposed based on the Kriging method. The expected feasibility learning function is extended for model updating using a Bayesian objective function. The uncertainties can be quantified through a derived likelihood function. The case study for verification involves a multisensory vehicle‐bridge system comprising only two sensors, with one installed on a vehicle parked temporarily on the bridge and another mounted directly on the bridge. The proposed algorithm is utilized for damage detection of two beams numerically and an aluminum model beam experimentally. The proposed method can achieve satisfactory accuracy in identifying damage with much less data, compared with the general Kriging model updating technique. Both the computation and instrumentation can be reduced for structural health monitoring and model updating.
ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12822