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.
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The first author wants to take the chance to thank the EM staff of IIT Bombay for their help during the laboratory work.
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MN: methodology, data analysis, writing—original manuscript preparation. VK: supervision, reviewing, and editing, JP: supervision, writing—review and editing, and formatting manuscript.
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Naresh, M., Kumar, V. & Pal, J. A machine learning approach for health monitoring of a steel frame structure using statistical features of vibration data. Asian J Civ Eng 25, 39–49 (2024). https://doi.org/10.1007/s42107-023-00755-6
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DOI: https://doi.org/10.1007/s42107-023-00755-6