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Efficacy of Sparse Regression for Li...

Katwal, Sadiksha,

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Efficacy of Sparse Regression for Linear Structural System Identification /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Efficacy of Sparse Regression for Linear Structural System Identification // Sadiksha Katwal.
作者:	Katwal, Sadiksha,
面頁冊數:	1 electronic resource (104 pages)
附註:	Source: Masters Abstracts International, Volume: 86-04.
Contained By:	Masters Abstracts International86-04.
標題:	Computer engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332516
ISBN:	9798384465423

Efficacy of Sparse Regression for Linear Structural System Identification /
Katwal, Sadiksha,

Efficacy of Sparse Regression for Linear Structural System Identification /Sadiksha Katwal. - 1 electronic resource (104 pages)

Source: Masters Abstracts International, Volume: 86-04.

The capability of sparse regression with Least Absolute Shrinkage and Selection Operator (LASSO) in modal identification of a simple system and predicting system response is remarkable. However, it has limitations when applied to more complex structure, particularly in equation discovery and response prediction. Despite these challenges, sparse regression demonstrates superior performance in linear system identification compared to Natural Excitation Technique (NExT) coupled with Eigensystem Realization Algorithm (ERA), especially in identifying higher modes and estimating damping ratios with reduced error. Findings indicate that while sparse regression is highly effective for simple systems, its application to real-world structures requires further exploration. The thesis concludes with recommendations for practical validation of sparse regression on actual structures and its comparison with alternative methods to assess its real-world efficacy in structural health monitoring.

English

ISBN: 9798384465423Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Equation discovery

Efficacy of Sparse Regression for Linear Structural System Identification /
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100 1 $a Katwal, Sadiksha, $e author. $3 3830247
245 1 0 $a Efficacy of Sparse Regression for Linear Structural System Identification / $c Sadiksha Katwal.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (104 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 86-04.
500 $a Advisors: Sen, Debarshi Committee members: Tezcan, Jale; Kolay, Prabir.
502 $b M.S. $c Southern Illinois University at Carbondale $d 2024.
520 $a The capability of sparse regression with Least Absolute Shrinkage and Selection Operator (LASSO) in modal identification of a simple system and predicting system response is remarkable. However, it has limitations when applied to more complex structure, particularly in equation discovery and response prediction. Despite these challenges, sparse regression demonstrates superior performance in linear system identification compared to Natural Excitation Technique (NExT) coupled with Eigensystem Realization Algorithm (ERA), especially in identifying higher modes and estimating damping ratios with reduced error. Findings indicate that while sparse regression is highly effective for simple systems, its application to real-world structures requires further exploration. The thesis concludes with recommendations for practical validation of sparse regression on actual structures and its comparison with alternative methods to assess its real-world efficacy in structural health monitoring.
546 $a English
590 $a School code: 0209
650 4 $a Computer engineering. $3 621879
650 4 $a Computer science. $3 523869
650 4 $a Architectural engineering. $3 3174102
653 $a Equation discovery
653 $a LASSO
653 $a Linear structural systems
653 $a Modal identification
653 $a Sparse regression
690 $a 0543
690 $a 0984
690 $a 0464
690 $a 0462
710 2 $a Southern Illinois University at Carbondale. $b Civil Engineering. $e degree granting institution. $3 3830243
720 1 $a Sen, Debarshi $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-04.
790 $a 0209
791 $a M.S.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332516