Modified truncated singular value decomposition method for moving force identification

Chen, Zhen; Deng, Lu; Kong, Xuan

doi:10.1177/13694332221104278

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…In further research, Chen et al compared these four methods to evaluate their overall performance through numerical simulations and laboratory verification [28]. Recently, Chen proposed a modified truncated singular value decomposition (MTSVD) method for moving force identification, aiming to overcome the ill-posed problems, and a comparative study was conducted with its conventional counterparts: the SVD and TSVD methods [29]. Pan and his co-authors proposed MFI methods to address the ill-posed problem [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Numerical Examination of a Novel Method for Identifying Bridge Moving Force Using an Instrumented Vehicle

Dexin

Lundbäck

et al. 2023

Buildings

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This study presents a novel and highly efficient technique to identify moving forces by utilising the acceleration response of an instrumented moving vehicle. The complex task of determining moving forces is transformed into one of solving linear equations, made easier through the Newmark-β method. To overcome the ill-conditioned problem and improve the recognition accuracy, Tikhonov regularisation is employed. Uniquely, this approach necessitates the placement sensors on the vehicle alone to record the responses of the vehicle–bridge coupled system as it traverses the bridge. Once the coupled responses are acquired, the moving force is quickly identified using the proposed method. The reliability of this method is numerically verified using a case study of a single degree-of-freedom vehicle crossing a simply supported beam. The performance of the proposed method is further demonstrated by examining several typical external factors. The results indicate that the method presents a high recognition accuracy, demonstrating good robustness and reliability even amidst substantial environmental noise interference. This proposed method offers a new perspective for identifying the moving force of small- to medium-span bridges.

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Section: Introductionmentioning

confidence: 99%

Theoretical and Numerical Examination of a Novel Method for Identifying Bridge Moving Force Using an Instrumented Vehicle

Dexin

Lundbäck

et al. 2023

Buildings

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“…Therefore, SVD is often used in topic analysis, document classification, and text-based recommendation systems. [17].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning Methods for Topic Extraction and Modeling in Large-scale Text Corpora using LSA and LDA

Henderi

2023

J. Appl. Data Sci.

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This research compares unsupervised learning methods in topic extraction and modeling in large-scale text corpora. The methods used are Singular Value Decomposition (SVD) and Latent Dirichlet Allocation (LDA). SVD is used to extract important features through term-document matrix decomposition, while LDA identifies hidden topics based on the probability distribution of words. The research involves data collection, data exploratory analysis (EDA), topic extraction using SVD, data preprocessing, and topic extraction using LDA. The data used were large-scale text corpora. Data explorative analysis was conducted to understand the characteristics and structure of text corpora before topic extraction was performed. SVD and LDA were used to identify the main topics in the text corpora. The results showed that SVD and LDA were successful in topic extraction and modeling of large-scale text corpora. SVD reveals cohesive patterns and thematically related topics. LDA identifies hidden topics based on the probability distribution of words. These findings have important implications in text processing and analysis. The resulting topic representations can be used for information mining, document categorization, and more in-depth text analysis. The use of SVD and LDA in topic extraction and modeling of large-scale text corpora provides valuable insights in text analysis. However, this research has limitations. The success of the methods depends on the quality and representativeness of the text corpora. Topic interpretation still requires further understanding and analysis. Future research can develop methods and techniques to improve the accuracy and efficiency of topic extraction and text corpora modeling.

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Identification of multi-axle vehicle loads on beam type bridge based on minimal residual norm steepest descent method

Chen

Fang

Kong

et al. 2023

Journal of Sound and Vibration

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Modified truncated singular value decomposition method for moving force identification

Cited by 5 publications

References 44 publications

Theoretical and Numerical Examination of a Novel Method for Identifying Bridge Moving Force Using an Instrumented Vehicle

Theoretical and Numerical Examination of a Novel Method for Identifying Bridge Moving Force Using an Instrumented Vehicle

Unsupervised Learning Methods for Topic Extraction and Modeling in Large-scale Text Corpora using LSA and LDA

Identification of multi-axle vehicle loads on beam type bridge based on minimal residual norm steepest descent method

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