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A machine learning approach for health monitoring of a steel frame structure using statistical features of vibration data

A machine learning approach for health monitoring of a steel frame structure using statistical... The main aim of this study is to present a connection damage identification technique in a plane frame structure using statistical features of vibration data and a support vector machine (SVM)-based ML algorithm. For that purpose, a small-scale laboratory-based single-story plane frame is considered. The damage was incorporated into the structure by making a groove at the connection and the base of the frame was excited, and the acceleration responses were collected from various points. From the responses, the standard deviation, median, mean absolute deviation, root mean square, kurtosis, skewness, approximate entropy, Shannon entropy, and Renyi’s entropy were extracted and utilized as input for the SVM algorithm. The training and testing results depict that the technique can differentiate between undamaged and various damaged classes. It indicates its efficacy as an automation tool for the health monitoring of connections in plane frame structures and could be verified for a large-scale structure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

A machine learning approach for health monitoring of a steel frame structure using statistical features of vibration data

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References (37)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-023-00755-6
Publisher site
See Article on Publisher Site

Abstract

The main aim of this study is to present a connection damage identification technique in a plane frame structure using statistical features of vibration data and a support vector machine (SVM)-based ML algorithm. For that purpose, a small-scale laboratory-based single-story plane frame is considered. The damage was incorporated into the structure by making a groove at the connection and the base of the frame was excited, and the acceleration responses were collected from various points. From the responses, the standard deviation, median, mean absolute deviation, root mean square, kurtosis, skewness, approximate entropy, Shannon entropy, and Renyi’s entropy were extracted and utilized as input for the SVM algorithm. The training and testing results depict that the technique can differentiate between undamaged and various damaged classes. It indicates its efficacy as an automation tool for the health monitoring of connections in plane frame structures and could be verified for a large-scale structure.

Journal

Asian Journal of Civil Engineering – Springer Journals

Published: Jun 12, 2023

Keywords: Structural health monitoring; Steel frame structure; Joint damage; Statistical features; Machine learning; SVM

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