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research-article

Damage detection using dissimilarity in phase space topology of dynamic response of structure subjected to shock wave loading

[+] Author and Article Information
Lavish Pamwani

Department of Civil Engineering, Indian Institute of Technology Guwahati, Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, Assam, India
lavish.p@gmail.com

Amit Shelke

Assistant Professor, Department of Civil Engineering, Indian Institute of Technology Guwahati, Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, Assam, India
amitsh@iitg.ernet.in

1Corresponding author.

ASME doi:10.1115/1.4040472 History: Received December 18, 2017; Revised May 30, 2018

Abstract

Shockwave is a high pressure and short duration pulse that induce damage and lead to the progressive collapse of the structure. The shock load excites high-frequency vibrational modes and causes failure due to large deformation in the structure. Shockwave experiments were conducted by imparting repetitive localized shock loads to create progressive damage states in the structure. Two-phase novel damage detection algorithm is proposed, that quantify and segregate perturbative damage from microscale damage. The first phase performs dimension reduction and damage state segregation using principal component analysis. In the second phase, the embedding dimension was reduced through Empirical Mode Decomposition. The embedding parameters were derived using singular system analysis and average mutual information function. Based, on Takens theorem and embedding parameters, the response was represented in a multi-dimensional phase space trajectory (PST). The dissimilarity in the multi-dimensional PST was used to derive the damage sensitive features (DSFs). The DSFs namely: (i) Change in phase space topology (CPST) and (ii) Mahalanobis distance between phase space topology (MDPST) are evaluated to quantify progressive damage states. The DSFs are able to quantify the occurrence, magnitude, and localization of progressive damage state in the structure. The proposed algorithm is robust and efficient to detect and quantify the evolution of damage state for extreme loading scenarios.

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