Abstract
In this paper, a convolutional neural network (CNN)-based deep learning architecture is proposed to identify joint damage in a steel plane frame structure with welded connections under temperature variability. For that purpose, a laboratory-based, single-story steel plane frame is considered. A base excitation is utilized to vibrate the structure and collect the time-domain acceleration response from various points under healthy and damaged conditions. From the responses, time–frequency-domain scalogram images are generated and fed into the CNN architecture. Initially, the study was carried out without considering the temperature changes in the data, and the average training, validation and testing accuracy were found to be 100%, 94.88%, and 94.07%, respectively. Then, the temperature variability is considered in the data, and the average training, validation, and testing accuracy were found to be 100%, 94.33%, and 91.85%, respectively, to identify the location and quantification of the damage. Finally, the architecture is tested with the data obtained from different locations (undamaged case), and different damaged conditions are tested with the CNN architecture, and the testing accuracy was found to be 90.37%. This paper also implemented the idea of class activation maps, which are visual representations of the input image’s regions that primarily contribute to a class’s classification score. The proposed CNN-based DL architecture can accurately distinguish the healthy and damaged classes, which indicates its efficiency as an automation tool for joint damage detection in a plane frame structure.
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The first author would like to express gratitude to the IIT Bombay EM staff for their assistance during the laboratory work.
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Naresh, M., Kumar, V. & Pal, J. A convolutional neural network-based architecture for health monitoring of joint damages in a steel plane frame structure under temperature variability. Asian J Civ Eng 25, 2077–2089 (2024). https://doi.org/10.1007/s42107-023-00895-9
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DOI: https://doi.org/10.1007/s42107-023-00895-9