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Research Papers

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,
Guwahati 781039, Assam, India
e-mail: lavish.p@gmail.com

Amit Shelke

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

1Corresponding author.

Manuscript received December 18, 2017; final manuscript received May 30, 2018; published online June 26, 2018. Assoc. Editor: Hoon Sohn.

ASME J Nondestructive Evaluation 1(4), 041004 (Jun 26, 2018) (13 pages) Paper No: NDE-17-1116; doi: 10.1115/1.4040472 History: Received December 18, 2017; Revised May 30, 2018

Shockwave is a high pressure and short duration pulse that induce damage and lead to 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 (PCA). In the second phase, the embedding dimension was reduced through empirical mode decomposition (EMD). The embedding parameters were derived using singular system analysis (SSA) and average mutual information function (AMIF). Based, on Takens theorem and embedding parameters, the response was represented in a multidimensional phase space trajectory (PST). The dissimilarity in the multidimensional 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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Figures

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Fig. 2

Flowchart of the proposed damage detection algorithm that explores dissimilarity in phase space trajectory

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Fig. 1

Schematic diagram of phase space trajectory for pristine state and damage state to evaluate damage sensitive feature

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Fig. 3

(a) Schematic diagram and (b) experimental model of frame structure

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Fig. 6

Acceleration time history of shear building for stage I shock loading

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Fig. 7

Acceleration time history of shear building for stage II shock loading

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Fig. 4

Shock tube experimental facility

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Fig. 5

Reflected shock pressure profile from frame structure at standoff distance of 1 m

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Fig. 11

Maximum displacement profile along the height of a structure for: (a) stage-I and (b) stage-II shock loading experiment

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Fig. 8

Frequency spectra of dynamic response of the shear building subjected to stage-I shock loading

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Fig. 9

Frequency spectra of dynamic response of the shear building subjected to stage-II shock loading

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Fig. 10

Plastic deformed shape of shear structure damage due to shock loading

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Fig. 12

Eigenvalues of dynamic response of the structure in E–W and N–S direction for: (a) stage-I and (b) stage-II shock loading experiment

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Fig. 16

Correlation between IMFs and its corresponding dynamic response for: (a) stage-I experiment and (b) stage-II shock loading experiment

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Fig. 19

PST of dominant IMF for all DOF for stage-I shock loading experiment

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Fig. 20

PST of dominant IMF for all DOF for stage-II shock loading experiment

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Fig. 21

Three dimension bar chart showing the variation of: (a) normalized CPST, (b) normalized MDPST, and (c) drift ratio with respect to various experiments and location of damage for stage I experiments (note: the experiment-1 corresponds to baseline (pristine) data)

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Fig. 22

Three dimension bar chart showing the variation of: (a) normalized CPST, (b) normalized MDPST, and (c) drift ratio with respect to various experiments and location of damage for stage II experiments

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Fig. 17

Normalized singular values with respect to embedded dimension of dynamic response for: (a) stage-I and (b) stage-II shock loading

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Fig. 18

Variation of AMIF with lag for the dynamic response of a shear building for: (a) stage-I and (b) stage-II shock loading experiments

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Fig. 13

Progressive changes in the direction of POC for stage-I shock loading

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Fig. 14

Progressive changes in the direction of POC for stage-II shock loading

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Fig. 15

IMF obtained by EMD of first floor's acceleration response for the stage-I shock loading

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