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Model-free data reconstruction of structural response and excitation via sequential broad learning
Sin-Chi Kuok1,2; Ka-Veng Yuen1
2020-02-21
Source PublicationMechanical Systems and Signal Processing
ISSN0888-3270
Volume141Pages:106738
Abstract

In this study, a novel sequential broad learning (SBL) approach is proposed to reconstruct the missing signal of damaged sensors in structural health monitoring (SHM) sensory networks. It is capable to reconstruct the structural response and external excitation of linear/nonlinear time-varying dynamical systems under stationary/nonstationary excitation for sensory networks. The proposed approach is a model-free data-driven machine learning methodology and the data reconstruction is executed sequentially with moving time windows. The learning algorithm is developed by adopting the recently developed broad learning system (BLS) (Chen and Liu, 2018). In contrast to deep learning that suffers from excessive computational cost for training the stacks of hierarchical layers, BLS is established with a broadly expandable network and can be modified incrementally based on the inherited results from the previous trained architecture. Therefore, BLS provides a computationally very efficient alternative to deep learning. Taking the benefit of BLS, the proposed SBL approach can efficiently handle the massive data stream generated in long-term monitoring. To demonstrate the efficacy and applicability of the proposed approach, simulated examples that cover linear and nonlinear time-varying dynamical systems subjected to stationary/nonstationary wind-load/base excitation with different types of sensing devices are discussed. Moreover, the SHM database of the field measurement monitored from the MIT Green Building is utilized to examine the performance of the proposed approach in realistic application. It is demonstrated that the proposed approach offers a powerful data reconstruction tool for challenging data missing situations encountered in SHM.

KeywordData Reconstruction Sequential Broad Learning Structural Response And Excitation Linear And nonLinear Stationary And nonStationary
DOI10.1016/j.ymssp.2020.106738
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000529084500037
PublisherELSEVIER LTD
Scopus ID2-s2.0-85080026980
Fulltext Access
FWCI2.009321
Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
THE STATE KEY LABORATORY OF ANALOG AND MIXED-SIGNAL VLSI (UNIVERSITY OF MACAU)
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,Macau,China
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. Model-free data reconstruction of structural response and excitation via sequential broad learning[J]. Mechanical Systems and Signal Processing,2020,141:106738.
APA Sin-Chi Kuok,&Ka-Veng Yuen.(2020).Model-free data reconstruction of structural response and excitation via sequential broad learning.Mechanical Systems and Signal Processing,141,106738.
MLA Sin-Chi Kuok,et al."Model-free data reconstruction of structural response and excitation via sequential broad learning".Mechanical Systems and Signal Processing 141(2020):106738.
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