Research Papers

Damage Classification and Feature Extraction in Steel Moment-Resisting Frame Using Time-Varying Autoregressive Model

[+] Author and Article Information
Lavish Pamwani, Vikram Agarwal

Department of Civil Engineering,
Indian Institute of Technology Guwahati,
Guwahati 781039, Assam, India

Amit Shelke

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

1Corresponding author.

Manuscript received December 5, 2018; final manuscript received March 3, 2019; published online March 25, 2019. Assoc. Editor: Shiro Biwa.

ASME J Nondestructive Evaluation 2(2), 021002 (Mar 25, 2019) (10 pages) Paper No: NDE-18-1047; doi: 10.1115/1.4043122 History: Received December 05, 2018; Accepted March 05, 2019

In this paper, the time-varying autoregressive (TVAR) model is integrated with the K-means—clustering technique to detect the damage in the steel moment-resisting frame. The damage is detected in the frame using nonstationary acceleration response of the structure excited using ambient white noise. The proposed technique identifies and quantifies the damage in the beam-to-column connection and column-to-column splice plate connection caused due to loosening of the connecting bolts. The algorithm models the nonstationary acceleration time history and evaluates the TVAR coefficients (TVARCs) for pristine and damage states. These coefficients are represented as a cluster in the TVARC subspace and segregated and classified using K-means—segmentation technique. The K-means—approach is adapted to simultaneously perform partition clustering and remove outliers. Eigenstructure evaluation of the segregated TVARC cluster is performed to detect the temporal damage. The topological and statistical parameters of the TVARC clusters are used to quantify the magnitude of the damage. The damage is quantified using the Mahalanobis distance (MD) and the Itakura distance (ID) serving as the statistical distance between the healthy and damage TVARC clusters. MD calculates a multidimensional statistical distance between two clusters using the covariance between the state vectors, whereas ID measures the dissimilarity of the autoregressive (AR) parameter between reference state and unknown states. These statistical distances are used as damage-sensitive feature (DSF) to detect and quantify the initiation and progression of the damage in the structure under ambient vibrations. The outcome of both the DSFs corroborate with the experimental investigation, thereby improving the robustness of the algorithm by avoiding false damage alarms.

Copyright © 2019 by ASME
Topics: Damage , Algorithms , Steel
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Fig. 1

Pictorial representation of K-means—clustering approach

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Fig. 2

(a) Demonstrates inefficient clustering while adopting the K-means approach due to the presence of outliers and (b) tighter clusters are obtained as outliers are removed while adopting the K-means—approach

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Fig. 3

Flowchart of the steps involved in the novel damage detection algorithm

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Fig. 4

(a) Experimental setup and (b) schematic sketch of steel frame

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Fig. 5

(a) Enhanced image of the beam-to-column connection and (b) enhanced image of the column-to-column connection

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Fig. 6

Acceleration time history of the base excitation subjected to the frame structure

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Fig. 7

Acceleration time history of (a) first floor and (b) second floor of two-story frame structure

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Fig. 8

Frequency spectra of dynamic response at (a) first-floor beam and (b) second-floor beam of the steel frame subjected to white noise base excitation

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Fig. 9

Time history representation of time series model coefficients: (a) TVAR coefficient a1(t), (b) TVAR coefficient a2(t), and (c) TVAR coefficient a3(t)

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Fig. 10

Polar plot of damage feature (α12) for (a) coefficient cluster Xc1=[XhXd1] and (b) coefficient cluster Xc2=[XhXd2]

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Fig. 11

Representation of model coefficients in TVAR coefficient subspace for (a) coefficient cluster Xc1=[XhXd1] corresponding to damage-1 and (b) coefficient cluster Xc2=[XhXd2] corresponding to damage-2

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Fig. 12

The K-means—clustering approach-assigned labels to the clusters for (a) damage case-1 and (b) damage case-2

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Fig. 13

The final output after the clusters are processed by K-means—approach for (a) damage case-1 and (b) damage case-2

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Fig. 14

Graphical representation of the (a) DSF based on the Mahalanobis distance and (b) DSF based on the Itakura distance evaluated for all the damage states



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