Effect of Computational Patterns of PCA on Moving Force Identification

2010 ◽  
Vol 163-167 ◽  
pp. 2678-2682
Author(s):  
Ling Yu ◽  
Jun Hua Zhu
Keyword(s):  
Experimental Study ◽  
Time Domain ◽  
Principle Component Analysis ◽  
Acceptable Solution ◽  
Force Identification ◽  
Moving Force ◽  
Moving Vehicles ◽  
Bridge Responses ◽  
The Relationship ◽  
Higher Sensitivity

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.

MOVING FORCE IDENTIFICATION: A TIME DOMAIN METHOD

1997 ◽  
Vol 201 (1) ◽  
pp. 1-22 ◽  
Author(s):  
S.S. Law ◽  
T.H.T. Chan ◽  
Q.H. Zeng
Keyword(s):  
Time Domain ◽  
Time Domain Method ◽  
Force Identification ◽  
Moving Force ◽  
Domain Method

A MOM-Based Algorithm for Identification of Moving Vehicle Loads on Bridges

2006 ◽  
Author(s):  
L. Yu ◽  
T. H. T. Chan ◽  
J. H. Zhu ◽  
M. Z. Chen
Keyword(s):  
Time Domain ◽  
Basis Functions ◽  
Time Domain Method ◽  
Acceptable Solution ◽  
Force Identification ◽  
Moving Vehicle ◽  
Moving Force ◽  
Vehicle Loads ◽  
Ill Conditioning ◽  
Bridge Responses

An improved time domain method (ITDM) is proposed for moving force identification using bridge responses, which aims at an acceptable solution to the ill-conditioning problem that often exists in the inverse problem of moving force identification. Based on the method of moments (MOM) and the theory of moving force identification, the moving forces were described as a combination of whole basis functions, such as orthogonal Legendre polynomials or Fourier series, and were then estimated by solving the new system equations developed based on the basis functions. Under a number of response combination cases, the moving vehicle loads are identified using the ITDM and compared with the existing time domain method (TDM). Further a laboratory study was conducted to evaluate the effect of various parameters on the ITDM. Those parameters include basis function number, mode number, number of measured stations, and CPU executive time of the ITDM. Simulation and experiment results show that the ITDM has higher identification accuracy and robust noise immunity as well as being able to generate an acceptable solution to the ill-conditioning problem to some extent when it is used to identify the moving forces from bridge responses. Meanwhile, the ITDM can lessen the executive CPU time as well as being more flexible when compared with the TDM. This is beneficial to real time analysis of moving force identification in field.

An Improved Algorithm for Moving Force Identification

2013 ◽  
Vol 438-439 ◽  
pp. 935-938
Author(s):  
Zhen Chen ◽  
Jun Ling Han ◽  
Jun Jie Li
Keyword(s):  
Time Domain ◽  
Noise Immunity ◽  
Bending Moment ◽  
Identification Accuracy ◽  
Time Varying ◽  
Force Identification ◽  
Moving Force ◽  
Ill Posed ◽  
Preconditioned Matrix ◽  
The Time Domain

The pretreatment conjugate gradient method (PCGM) is proposed to improve the ill-posed problem of moving force identification. Compared with the time domain method (TDM), the identification accuracy of the time-varying bending moment responses is more obviously improved. Meanwhile, the pretreatment matrix is very important to the PCGM because it affects the identification accuracy and robust noise immunity as well as ill-posed problems identification to some extent, choosing proper preconditioned matrix can effectively improve both of identification accuracy and efficiency of the PCGM.

Theory Study by CGM for Moving Force Identification

2012 ◽  
Vol 238 ◽  
pp. 823-825
Author(s):  
Zhen Chen ◽  
Jun Ling Han
Keyword(s):  
Time Domain ◽  
Noise Immunity ◽  
Theoretical Study ◽  
Identification Accuracy ◽  
Acceptable Solution ◽  
Force Identification ◽  
Moving Force ◽  
Study Results ◽  
Ill Posed ◽  
The Time Domain

The conjugate gradient method (CGM) is proposed in this paper to improve the ill-posed problem of moving force identification. Based on theoretical analysis, the CGM formula is deduced and applied in moving force identification. Related research shows that the CGM have higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-posed cases to some extent comparing with the time domain method (TDM), when they are used to identify the moving force. The theoretical study results by CGM are practical significant to selection properly method for moving force identification.

Moving force identification based on redundant concatenated dictionary and weighted l1-norm regularization

2018 ◽  
Vol 98 ◽  
pp. 32-49 ◽  
Author(s):  
Chu-Dong Pan ◽  
Ling Yu ◽  
Huan-Lin Liu ◽  
Ze-Peng Chen ◽  
Wen-Feng Luo
Keyword(s):  
L1 Norm ◽  
Force Identification ◽  
Moving Force
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