UM  > Faculty of Science and Technology
Residential Collegefalse
Status已發表Published
Multi-resolution broad learning for model updating using incomplete modal data
Sin-Chi Kuok1,2; Ka-Veng Yuen1
2020-06-01
Source PublicationStructural Control and Health Monitoring
ISSN1545-2255
Volume27Issue:8Pages:c2571
Abstract

A novel multi-resolution broad learning (MRBL) approach is proposed for model updating using identified modal data. Due to measurement noise and limited monitoring locations, the identified modal data are incomplete and noise corrupted. Besides, it is inevitable to have modeling errors in finite element models. Therefore, it is nontrivial to establish simple explicit relationship to represent the nonlinear mapping from modal data to structural model parameters. The proposed approach aims to model this implicit mapping using a nonparametric approach. For this purpose, the nonlinear relationship is learnt based on a multi-resolution recursive procedure with expandable broad learning networks. In contrast to conventional deep learning, the proposed approach is computationally very economical. Instead of requiring large volumes of training data, the multi-resolution approach adaptively zooms into the important region for sampling. Hence, satisfactory accuracy in model updating can be achieved by using a feasible amount of training data. Moreover, the broad learning network is expandable to adopt architectural modification, so it can be reconfigured incrementally based on the inherit information from the trained network. To demonstrate the efficacy of the proposed approach, illustrative examples of a shear building and a three-dimensional braced frame with unobserved torsional mode are presented. Finally, an application using real data of Canton Tower is also presented.

KeywordMachine Learning Modal Measurement Model Updating Multi-resolution Broad Learning Structural Health Monitoring
DOI10.1002/stc.2571
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaConstruction & Building Technology ; Engineering ; Instruments & Instrumentation
WOS SubjectConstruction & Building Technology ; Engineering, Civil ; Instruments & Instrumentation
WOS IDWOS:000538164200001
PublisherJOHN WILEY & SONS LTD, THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND
Scopus ID2-s2.0-85085700620
Fulltext Access
FWCI0.4066716
Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding AuthorKa-Veng Yuen
Affiliation1.State Key Laboratory on Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,Macao
2.Department of Engineering Science,University of Cambridge,Cambridge,United Kingdom
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Sin-Chi Kuok,Ka-Veng Yuen. Multi-resolution broad learning for model updating using incomplete modal data[J]. Structural Control and Health Monitoring,2020,27(8):c2571.
APA Sin-Chi Kuok,&Ka-Veng Yuen.(2020).Multi-resolution broad learning for model updating using incomplete modal data.Structural Control and Health Monitoring,27(8),c2571.
MLA Sin-Chi Kuok,et al."Multi-resolution broad learning for model updating using incomplete modal data".Structural Control and Health Monitoring 27.8(2020):c2571.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sin-Chi Kuok]'s Articles
[Ka-Veng Yuen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sin-Chi Kuok]'s Articles
[Ka-Veng Yuen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sin-Chi Kuok]'s Articles
[Ka-Veng Yuen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.