Evaluation of Improved Mode Shape Curvature-Based Damage Detection using Robust Regression Method

• Published in: IOP conference series. Materials Science and Engineering Vol. 1092; no. 1; p. 12036
• Main Authors: Man, M H C, Amiruddin, M A, Fawazi, N
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2021
• Subjects: Algorithms; Aluminum; Curvature; Damage detection; Damage localization; Evaluation; Outliers (statistics); Regression; Reliability analysis; Robustness (mathematics); Structural damage; Structural health monitoring
• ISSN: 1757-8981; 1757-899X; 1757-899X
• DOI: 10.1088/1757-899X/1092/1/012036

Structural Health Monitoring (SHM) is an important tool to maintain structures integrity and safety. Gapped Smoothing Method (GSM) is a high sensitivity algorithm for damage detection using mode shape curvature (MSC) to identify small size of damage. However, it has limited capability to localize wide size of damage with decent accuracy. The objective of this study is to improve GSM algorithm using Robust Regression i.e., Iteratively Re-weighted Least Square (IRLS) methods. Subsequently, evaluate the proposed algorithm’s capability to localize damage with different size and number of measurements of an aluminum beam numerically. Damage Estimate Reliability (DER) was used to evaluate effectiveness of the improved GSM algorithm. The obtained result shows inclusion of robust regression in the original GSM algorithm increase Damage Estimate Reliability (DER) for 50mm damage size with damage level 20% by 11.6%. IRLS reduces influence of an outlier towards estimation of undamaged MSC that cause noise around damage area (in damage index plot) therefore produce more accurate estimation of damage size. Improvement of GSM allows this method to localize different level of damage in structures.