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Monitoring gross vehicle weight with a probabilistic and influence line-free bridge weight-in-motion scheme based on a transmissibility-like index
Yan, Wang Ji1,2; Hao, Teng Teng1; Yuen, Ka Veng1,2; Papadimitriou, Costas3
2022-09
Source PublicationMechanical Systems and Signal Processing
ISSN0888-3270
Volume177
Abstract

A new transmissibility-like index defined as the ratio of the frequency responses of the same monitoring location under two different loading conditions was proposed for Gross Vehicle Weights (GVWs) monitoring in this study. Based on the theoretical finding that the displacements of a beam subjected to moving loads were the convolution of the load and the influence line, the equivalence between the transmissibility-like index at the zero frequency and the ratio of two GVWs under two-moving-load scenarios was theoretically revealed. Given the reference responses for known moving loads, an influence line-free algorithm was proposed to estimate the GVW of an arbitrary vehicle by making full use of the unique property of the new transmissibility-like index. To accommodate various uncertainties and fuse the measurements of different channels simultaneously, the problem of Bridge Weigh-In-Motion (B-WIM) was formulated in the framework of Bayesian inference with the aid of a complex Gaussian ratio probabilistic model of transmissibility function. The posterior distribution of the GVW was derived analytically. By applying the proposed transmissibility-like index, this method possessed an obvious advantage in achieving robust GVWs without the requirement of any knowledge of the bridge model such as the influence line. Two applications, including a numerical example and an experimental verification, were used to demonstrate the efficiency and accuracy of the statistical and influence line-free B-WIM scheme.

KeywordBayesian Analysis Bridge Weight-in-motion Influence Line Moving Loads Transmissibility
DOI10.1016/j.ymssp.2022.109133
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000799273400003
Scopus ID2-s2.0-85129070956
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Faculty of Science and Technology
Corresponding AuthorYan, Wang Ji
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau, China
2.Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities, University of Macau, China
3.Department of Mechanical Engineering, University of Thessaly, Greece
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yan, Wang Ji,Hao, Teng Teng,Yuen, Ka Veng,et al. Monitoring gross vehicle weight with a probabilistic and influence line-free bridge weight-in-motion scheme based on a transmissibility-like index[J]. Mechanical Systems and Signal Processing,2022,177.
APA Yan, Wang Ji,Hao, Teng Teng,Yuen, Ka Veng,&Papadimitriou, Costas.(2022).Monitoring gross vehicle weight with a probabilistic and influence line-free bridge weight-in-motion scheme based on a transmissibility-like index.Mechanical Systems and Signal Processing,177.
MLA Yan, Wang Ji,et al."Monitoring gross vehicle weight with a probabilistic and influence line-free bridge weight-in-motion scheme based on a transmissibility-like index".Mechanical Systems and Signal Processing 177(2022).
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