@inproceedings{61ee29896c204a87b162c1b90320dfb7,
title = "Deep Learning Model to Improve the Stability of Damage Identification via Output-only Signal",
abstract = "This study utilizes vibration-based signal analysis as a non-destructive testing technique that involves analyzing the vibration signals produced by a structure to detect possible defects or damage. The study aims to employ deep learning models to identify defects in a 3D-printed cantilever beam by analyzing the beam's tip displacement given a random input signal generated by an electromagnetic shaker. This study is focused on the output signal without any information of the random input, which is common for structural health monitoring applications in practice. Additionally, the study has revealed that the number of times the test set is applied to the trained model significantly impacts the accuracy of the model's consistent predictions.",
keywords = "Deep learning, GRU, LSTM, Model Stability, Signal processing, Structural Health Monitoring, Vibrations",
author = "Jongyeop Kim and Jinki Kim and Matthew Sands and Seongsoo Kim",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023 ; Conference date: 23-05-2023 Through 25-05-2023",
year = "2023",
doi = "10.1109/SERA57763.2023.10197684",
language = "English",
series = "Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "201--209",
editor = "Yeong-Tae Song and Junghwan Rhee and Yuseok Jeon",
booktitle = "Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023",
address = "United States",
}
