Moving force identification based on modified preconditioned conjugate gradient method

Article

Chen, Zhen, Chan, Tommy H. T. and Nguyen, Andy. 2018. "Moving force identification based on modified preconditioned conjugate gradient method." Journal of Sound and Vibration. 423, pp. 100-117. https://doi.org/10.1016/j.jsv.2017.11.034
Article Title

Moving force identification based on modified preconditioned conjugate gradient method

ERA Journal ID1295
Article CategoryArticle
AuthorsChen, Zhen (Author), Chan, Tommy H. T. (Author) and Nguyen, Andy (Author)
Journal TitleJournal of Sound and Vibration
Journal Citation423, pp. 100-117
Number of Pages18
Year2018
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0022-460X
1095-8568
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jsv.2017.11.034
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0022460X17308064
Abstract

This paper develops a modified preconditioned conjugate gradient (M-PCG) method for moving force identification (MFI) by improving the conjugate gradient (CG) and preconditioned conjugate gradient (PCG) methods with a modified Gram-Schmidt algorithm. The method aims to obtain more accurate and more efficient identification results from the responses of bridge deck caused by vehicles passing by, which are known to be sensitive to ill-posed problems that exist in the inverse problem. A simply supported beam model with biaxial time-varying forces is used to generate numerical simulations with various analysis scenarios to assess the effectiveness of the method. Evaluation results show that regularization matrix L and number of iterations j are very important influence factors to identification accuracy and noise immunity of M-PCG. Compared with the conventional counterpart SVD embedded in the time domain method (TDM) and the standard form of CG, the M-PCG with proper regularization matrix has many advantages such as better adaptability and more robust to ill-posed problems. More importantly, it is shown that the average optimal numbers of iterations of M-PCG can be reduced by more than 70% compared with PCG and this apparently makes M-PCG a preferred choice for field MFI applications.

Keywordsmoving force identification; modified preconditioned conjugate gradient; time domain method; regularization matrix; number of iterations; modified Gram-Schmidt algorithm
ANZSRC Field of Research 2020400510. Structural engineering
Public Notes

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

Byline AffiliationsQueensland University of Technology
School of Civil Engineering and Surveying
Institution of OriginUniversity of Southern Queensland
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