MOVING FORCE IDENTIFICATION STUDIES, I: THEORY

2001 ◽  
Vol 247 (1) ◽  
pp. 59-76 ◽  
Author(s):  
T.H.T. CHAN ◽  
L. YU ◽  
S.S. LAW ◽  
T.H. YUNG
Keyword(s):  
Force Identification ◽  
Moving Force

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

scholarly journals Toward efficacy of piecewise polynomial truncated singular value decomposition algorithm in moving force identification

2019 ◽  
Vol 22 (12) ◽  
pp. 2687-2698 ◽  
Author(s):  
Zhen Chen ◽  
Lifeng Qin ◽  
Shunbo Zhao ◽  
Tommy HT Chan ◽  
Andy Nguyen
Keyword(s):  
Singular Value Decomposition ◽  
Singular Values ◽  
Singular Value ◽  
Decomposition Algorithm ◽  
Identification Accuracy ◽  
Piecewise Polynomial ◽  
Truncated Singular Value Decomposition ◽  
Force Identification ◽  
Moving Force ◽  
Value Decomposition

This article introduces and evaluates the piecewise polynomial truncated singular value decomposition algorithm toward an effective use for moving force identification. Suffering from numerical non-uniqueness and noise disturbance, the moving force identification is known to be associated with ill-posedness. An important method for solving this problem is the truncated singular value decomposition algorithm, but the truncated small singular values removed by truncated singular value decomposition may contain some useful information. The piecewise polynomial truncated singular value decomposition algorithm extracts the useful responses from truncated small singular values and superposes it into the solution of truncated singular value decomposition, which can be useful in moving force identification. In this article, a comprehensive numerical simulation is set up to evaluate piecewise polynomial truncated singular value decomposition, and compare this technique against truncated singular value decomposition and singular value decomposition. Numerically simulated data are processed to validate the novel method, which show that regularization matrix [Formula: see text] and truncating point [Formula: see text] are the two most important governing factors affecting identification accuracy and ill-posedness immunity of piecewise polynomial truncated singular value decomposition.

Moving Force Identification for Multi-Span Bridges by Using Acceleration Measurements

2001 ◽  
Author(s):  
F. T. K. Au ◽  
R. J. Jiang ◽  
Y. K. Cheung
Keyword(s):  
Inverse Problem ◽  
Least Squares Method ◽  
Identification Problem ◽  
Singular Value ◽  
Robust Method ◽  
Force Identification ◽  
Beam Vibration ◽  
Moving Force ◽  
Value Decomposition ◽  
Pseudo Inverse

Abstract This paper reports some initial findings in the attempt to develop a robust method to identify more than one moving force on multi-span non-uniform continuous bridges. To keep the number of unknowns in the moving force identification problem to a minimum, the modified beam vibration functions are chosen as the assumed modes of a multi-span bridge. These modified beam vibration functions satisfy the zero deflection conditions at all the intermediate supports as well as the boundary conditions at the two ends of the bridge. The least squares method is used to solve the inverse problem to get the closest approximation to the moving forces. The pseudo-inverse to obtain the solution to the inverse problem is obtained by singular value decomposition. Only acceleration measurements are used for the moving force identification. The results show that this method is applicable and robust.

Force identification under unknown initial conditions by using concomitant mapping matrix and sparse regularization

2020 ◽  
pp. 107754632094469
Author(s):  
Xijun Ye ◽  
Chudong Pan
Keyword(s):  
Optimization Problem ◽  
Initial Conditions ◽  
Sparse Regularization ◽  
Force Identification ◽  
Moving Force ◽  
Dynamic Forces ◽  
Force Reconstruction ◽  
Structural Responses ◽  
One Step ◽  
Regularization Strategy

Unknown initial conditions can affect the identified accuracy of dynamic forces. Direct measurement of initial conditions is relatively difficult. This study proposes a sparse regularization–based method for identifying forces considering influences of unknown initial conditions. The initial conditions are embedded in a classical governing equation of force identification. The key idea is to introduce a concept of concomitant mapping matrix for reasonably expressing the initial conditions. First, a dictionary is introduced for expanding the dynamic forces. Then, the concomitant mapping matrix is formulated by using free vibrating responses, which correspond to structural responses happening after the structure is subjected to each atom of the force dictionary. A sparse regularization strategy is applied for solving the ill-conditioned equation. After that, the problem of force identification is converted into an optimization problem, and it can be solved by using a one-step strategy. Numerical simulations are carried out for verifying the feasibility and effectiveness of the proposed method. Illustrated results clearly show the applicability and robustness of the proposed method for dealing with force reconstruction and moving force identification.

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