Thermal strain extraction methodologies for bridge structural condition assessment

Zhu, Yanjie; Ni, Yi Qing; Jesus, Andre; Liu, Jingliang; Laory, Irwanda

Abstract

This paper presents a feature extraction method to uncover the temperature effects on bridge responses, which combines mode decomposition, data reduction, and blind separation. For mode decomposition, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) have been executed, followed by principal component analysis (PCA) for data size compression. Independent component analysis (ICA) is then employed for blind separation. The unique feature of the proposed method is the blind separation, which enables temperature-induced response to be extracted from the mixed structural responses without any prior information of the loading conditions and structural physical models. This study further evaluates the effects of extracting a temperature-induced response on damage detectability when using Moving Principal Component Analysis (MPCA). Numerical analysis for a truss bridge model is first conducted to evaluate the proposed method for thermal feature extraction, followed by a real truss bridge test in the structural laboratory at the University of Warwick. Results from the numerical study show that the method enables the separation of a temperature-induced response; and furthermore, the EEMD, utilized in mode decomposition, has a positive influence on the blind separation compared with EMD when combined with PCA and ICA. Finally, the real truss bridge test demonstrates that the feature extraction method can enhance the probability of MPCA to uncover the damage as the MPCA fails to discern damage without the proposed method.

• Journal Article Type: Article
• Acceptance Date: Jul 26, 2018
• Online Publication Date: Jul 26, 2019
• Publication Date: Sep 21, 2018
• Deposit Date: Aug 21, 2019
• Journal: Smart Materials and Structures
• Print ISSN: 0964-1726
• Electronic ISSN: 1361-665X
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 10
• Article Number: 105051
• DOI: https://doi.org/10.1088/1361-665X/aad5fb
• Public URL: https://uwe-repository.worktribe.com/output/2127125
• Publisher URL: https://doi.org/10.1088/1361-665X/aad5fb