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Application of couple sparse coding in smart damage detection of truss bridges

Application of couple sparse coding in smart damage detection of truss bridges
Application of couple sparse coding in smart damage detection of truss bridges
Damage detection of bridge structures plays a crucial role in in-time maintenance of such structures, which subsequently prevents further propagation of the damage, and likely collapse of the structure. Currently, the application of machine learning algorithms are growing in smart damage detection of structures. This work focuses on application of a new machine learning algorithm to identify the location and severity of damage in truss bridges. Frequency Response Functions (FRFs) are used as damage features, and are compressed using Principal Component Analysis (PCA). Couple Sparse Coding (CSC) is adopted as a classification method to learn the relationship between the bridge damage features and its damage states. Two truss bridges are used to test the proposed method and determine its accuracy in damage detection of truss bridges. It is found that the proposed method provides a reliable detection of damage location and severity in truss bridges.
Smart Damage Detection, Frequency Response Function (FRF), Principal Component Analysis (PCA), Couple Sparse Coding (CSC), Truss Bridges
1478-4637
Fallahian, Milad
efaa53d3-5e47-4392-b29d-67d5efcfb762
Ahmadi, Ehsan
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Talaei, Saeid
bebb3bcd-003b-4835-92c7-55c80b87be55
Khoshnoudian, Faramarz
af005906-c12c-4d74-9361-6c074ab5d67b
Kashani, Mohammad
d1074b3a-5853-4eb5-a4ef-7d741b1c025d
Fallahian, Milad
efaa53d3-5e47-4392-b29d-67d5efcfb762
Ahmadi, Ehsan
f1994ae0-2b3e-43c9-a595-032e801aae70
Talaei, Saeid
bebb3bcd-003b-4835-92c7-55c80b87be55
Khoshnoudian, Faramarz
af005906-c12c-4d74-9361-6c074ab5d67b
Kashani, Mohammad
d1074b3a-5853-4eb5-a4ef-7d741b1c025d

Fallahian, Milad, Ahmadi, Ehsan, Talaei, Saeid, Khoshnoudian, Faramarz and Kashani, Mohammad (2022) Application of couple sparse coding in smart damage detection of truss bridges. Proceedings of the Institution of Civil Engineers - Bridge Engineering. (doi:10.1680/jbren.22.00017).

Record type: Article

Abstract

Damage detection of bridge structures plays a crucial role in in-time maintenance of such structures, which subsequently prevents further propagation of the damage, and likely collapse of the structure. Currently, the application of machine learning algorithms are growing in smart damage detection of structures. This work focuses on application of a new machine learning algorithm to identify the location and severity of damage in truss bridges. Frequency Response Functions (FRFs) are used as damage features, and are compressed using Principal Component Analysis (PCA). Couple Sparse Coding (CSC) is adopted as a classification method to learn the relationship between the bridge damage features and its damage states. Two truss bridges are used to test the proposed method and determine its accuracy in damage detection of truss bridges. It is found that the proposed method provides a reliable detection of damage location and severity in truss bridges.

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e-pub ahead of print date: 5 October 2022
Keywords: Smart Damage Detection, Frequency Response Function (FRF), Principal Component Analysis (PCA), Couple Sparse Coding (CSC), Truss Bridges

Identifiers

Local EPrints ID: 470653
URI: http://eprints.soton.ac.uk/id/eprint/470653
ISSN: 1478-4637
PURE UUID: a4a01c2d-7a1c-497f-900f-8fe26e718423
ORCID for Mohammad Kashani: ORCID iD orcid.org/0000-0003-0008-0007

Catalogue record

Date deposited: 17 Oct 2022 16:45
Last modified: 18 Oct 2022 01:50

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Contributors

Author: Milad Fallahian
Author: Ehsan Ahmadi
Author: Saeid Talaei
Author: Faramarz Khoshnoudian

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