Date of Award
8-1-2024
Degree Name
Master of Science
Department
Civil Engineering
First Advisor
Sen, Debarshi
Abstract
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.
Access
This thesis is only available for download to the SIUC community. Current SIUC affiliates may also access this paper off campus by searching Dissertations & Theses @ Southern Illinois University Carbondale from ProQuest. Others should contact the interlibrary loan department of your local library or contact ProQuest's Dissertation Express service.