Effect of Computational Patterns of PCA on Moving Force Identification

Ling Yu; Jun Hua Zhu

doi:10.4028/www.scientific.net/AMR.163-167.2678

Paper Titles

Characteristics and Application of BOTDR in Distributed Detection of Pile Foundation
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Application of Neural Network in Displacement Prediction of Dongping Tunnel
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Study of Load Test of Pedestrian Suspension Bridge
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Application Analysis of Structure Deformation Monitoring Prediction Methods
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Effect of Computational Patterns of PCA on Moving Force Identification
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A Technique Based on Kalman Filter and Least Square Estimation for Time Domain Structural Damage Detection
p.2683

Investigation Concerning Vibration Reduction of Self-Anchored Suspension Bridge under Vehicle Braking Forces
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Study on Parameters for Identification of Wavelet Packet Energy Spectrum for Structural Damage Alarming
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Bidirectional Moving Vehicle Load Identification from Bridge Responses
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 163-167Effect of Computational Patterns of PCA on Moving...

Effect of Computational Patterns of PCA on Moving Force Identification

Abstract:

Effect of computational patterns of principle component analysis (PCA) on moving force identification (MFI) is studied in this paper. The motion equation of bridge due to moving vehicles are formed, the relationship between moving axle loads and caused bridge responses are established for the PCA-based MFI method in time domain. The measured bridge responses are rearranged in a matrix form for easily performing PCA and are adopted for obtaining an acceptable solution to the MFI problem. A laboratory experimental study was conducted to assess effectiveness and robustness of the PCA-based MFI method. The illustrated results show that the PCA-based method is an easy executive and more effective method for the MFI problem. The PCA computational patterns should be appropriately considered due to its higher sensitivity on response catalogues.

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Periodical:

Advanced Materials Research (Volumes 163-167)

Pages:

2678-2682

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.163-167.2678

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Online since:

December 2010

Authors:

Ling Yu, Jun Hua Zhu

Keywords:

Moving Force Identification (MFI), Principal Component Analysis (PCA), Structural Health Monitoring (SHM), Time Domain Method (TDM)

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