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- Publisher Website: 10.1111/mice.12822
- WOS: WOS:000757227400001
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Article: Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference
Title | Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference |
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Authors | |
Issue Date | 2022 |
Publisher | John Wiley & Sons Ltd. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=1093-9687 |
Citation | Computer-Aided Civil and Infrastructure Engineering, 2022 How to Cite? |
Abstract | 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. |
Persistent Identifier | http://hdl.handle.net/10722/319164 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | YUAN, Y | - |
dc.contributor.author | Au, FTK | - |
dc.contributor.author | Yang, DD | - |
dc.contributor.author | Zhang, J | - |
dc.date.accessioned | 2022-10-14T05:08:18Z | - |
dc.date.available | 2022-10-14T05:08:18Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Computer-Aided Civil and Infrastructure Engineering, 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10722/319164 | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.publisher | John Wiley & Sons Ltd. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=1093-9687 | - |
dc.relation.ispartof | Computer-Aided Civil and Infrastructure Engineering | - |
dc.rights | Submitted (preprint) Version This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Accepted (peer-reviewed) Version This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. | - |
dc.title | Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference | - |
dc.type | Article | - |
dc.identifier.email | Au, FTK: francis.au@hku.hk | - |
dc.identifier.authority | Au, FTK=rp00083 | - |
dc.identifier.doi | 10.1111/mice.12822 | - |
dc.identifier.hkuros | 338482 | - |
dc.identifier.isi | WOS:000757227400001 | - |