@inproceedings{37d425fb2fc94993ba37898cbc1e8d1d,
title = "Deep Learning Model for Railroad Structural Health Monitoring via Distributed Acoustic Sensing",
abstract = "Railway infrastructure plays a vital role in modern transportation systems, facilitating the efficient movement of people and goods. However, the integrity and performance of railroad structures are subject to various external forces and aging processes, which necessitate continuous monitoring to ensure safety and operational efficiency. This research focused on the structural health monitoring of the railroad using Distributed Acoustic Sensing (DAS) data collected from a High Tonnage Loop (HTL). An investigation on applying a deep learning model, long-shot-term memory (LSTM), and gated recurrent Unit(GRU) is presented to identify and classify railroad conditions.",
keywords = "DAS, HTL, HTL Fiber, LSTM, railroad health, railroad monitoring, Structural Health Monitoring",
author = "Rahman, {Md Arifur} and Hossein Taheri and Jongyeop Kim",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023 ; Conference date: 05-07-2023 Through 07-07-2023",
year = "2023",
doi = "10.1109/SNPD-Winter57765.2023.10223971",
language = "English",
series = "2023 26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "274--281",
booktitle = "2023 26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023",
address = "United States",
}