Moving force identification based on sparse regularization combined with moving average constraint

2021 ◽  
Vol 515 ◽  
pp. 116496
Author(s):  
Chudong Pan ◽  
Zhenjie Huang ◽  
Junda You ◽  
Yisha Li ◽  
Lihua Yang
Keyword(s):  
Moving Average ◽  
Sparse Regularization ◽  
Force Identification ◽  
Moving Force

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.

Estimation of bridge surface profile from moving vehicle accelerations by means of moving force identification - an experimental field study

2019 ◽  
Vol 3 (3/4) ◽  
pp. 289-309
Author(s):  
Kai Chun Chang ◽  
Chul Woo Kim ◽  
Souichiro Hasegawa ◽  
Shunsuke Nakajima ◽  
Patrick J. McGetrick
Keyword(s):  
Field Study ◽  
Surface Profile ◽  
Force Identification ◽  
Moving Vehicle ◽  
Moving Force ◽  
Experimental Field

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

Moving force identification based on learning dictionary with double sparsity

2022 ◽  
Vol 170 ◽  
pp. 108811
Author(s):  
Zi-Hang Zhang ◽  
Wen-Yu He ◽  
Wei-Xin Ren
Keyword(s):  
Force Identification ◽  
Moving Force

Compressed sensing for moving force identification using redundant dictionaries

2020 ◽  
Vol 138 ◽  
pp. 106535 ◽  
Author(s):  
Huanlin Liu ◽  
Ling Yu ◽  
Ziwei Luo ◽  
Chudong Pan
Keyword(s):  
Compressed Sensing ◽  
Force Identification ◽  
Moving Force ◽  
Redundant Dictionaries

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.

Group sparse regularization for impact force identification in time domain

2019 ◽  
Vol 445 ◽  
pp. 44-63 ◽  
Author(s):  
Baijie Qiao ◽  
Zhu Mao ◽  
Jinxin Liu ◽  
Zhibin Zhao ◽  
Xuefeng Chen
Keyword(s):  
Time Domain ◽  
Impact Force ◽  
Sparse Regularization ◽  
Force Identification

An enhanced sparse regularization method for impact force identification

2019 ◽  
Vol 126 ◽  
pp. 341-367 ◽  
Author(s):  
Baijie Qiao ◽  
Junjiang Liu ◽  
Jinxin Liu ◽  
Zhibo Yang ◽  
Xuefeng Chen
Keyword(s):  
Regularization Method ◽  
Impact Force ◽  
Sparse Regularization ◽  
Force Identification
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