• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.25-30
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• 2024

Train derailment accidents have been increasing over the past five years. The causes of these railroad derailments were found to be mainly defective track switches that change train tracks, use of old parts, and poor maintenance issues. In this paper, to solve these problems, an intelligent sensor-based automatic railway risk prediction algorithm and hypothesis were established and computer simulation experiments were performed. In particular, research on RFID technology and IoT sensor technology was conducted on a WEB basis. In addition, in this paper, in order to prevent country of origin counterfeiting accidents, a blockchain-based computer simulation to prevent forgery of railway parts was performed using open source.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.375-380
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• 2022

High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.