Identification of vehicle axle loads from bridge responses using preconditioned least square QR-factorization algorithm

Article

Chen, Zhen, Chan, Tommy H.T., Nguyen, Andy and Yu, Ling. 2019. "Identification of vehicle axle loads from bridge responses using preconditioned least square QR-factorization algorithm." Mechanical Systems and Signal Processing. 128, pp. 479-496. https://doi.org/10.1016/j.ymssp.2019.03.043
Article Title

Identification of vehicle axle loads from bridge responses using preconditioned least square QR-factorization algorithm

ERA Journal ID3705
Article CategoryArticle
AuthorsChen, Zhen (Author), Chan, Tommy H.T. (Author), Nguyen, Andy (Author) and Yu, Ling (Author)
Journal TitleMechanical Systems and Signal Processing
Journal Citation128, pp. 479-496
Number of Pages18
Year2019
Place of PublicationUnited Kingdom
ISSN0888-3270
1096-1216
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ymssp.2019.03.043
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0888327019302183
Abstract

This paper develops a novel method for moving force identification (MFI) called preconditioned least square QR-factorization (PLSQR) method. The algorithm seeks to reduce the impact of identification errors caused by unknown noise. The biaxial moving forces travel on a simply supported bridge at three different speeds is used to generate numerical simulations to assess the effectiveness and applicability of the algorithm. Results indicate that the method is more robust towards ill-posed problem and has higher identification precision than the conventional time domain method (TDM). In addition, the robustness and ill-posed immunity of PLSQR are directly affected by two kinds of regularization parameters, namely, number of iterations j and regularization matrix L. Compared with the standard form of least square QR-factorization (LSQR), i.e., the regularization matrix L being the identity matrix I_n, the PLSQR with the optimal number of iterations j and regularization matrix L has many advantages on MFI and it is more suitable for field trials due to better adaptability with type of sensors and number of sensors.

Keywordsmoving force identification; preconditioned least square QR-factorization; time domain method; regularization parameter; preconditioner
ANZSRC Field of Research 2020400510. Structural engineering
401702. Dynamics, vibration and vibration control
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author.

Byline AffiliationsQueensland University of Technology
Jinan University, China
Institution of OriginUniversity of Southern Queensland
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