Research on railway tunnel risk control method based on data minings

Jie Chen

doi:10.1117/12.3004917

16 August 2023 Research on railway tunnel risk control method based on data minings

Jie Chen

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870C (2023) https://doi.org/10.1117/12.3004917
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

This paper considers the risk assessment of railway tunnels and proposes a method based on deep learning in the context of artificial intelligence. Specifically, we use convolutional neural networks (CNN) to process and analyze sensor data inside the tunnel, and establish a supervised learning model to predict the health status of the tunnel. We also introduced a Bayesian optimization algorithm to optimize the model's super parameters, and used different evaluation indicators to evaluate the performance of the model. The experimental results show that our method is superior to traditional methods in terms of accuracy, robustness, and interpretability, and can help railway companies better manage tunnel risks.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jie Chen "Research on railway tunnel risk control method based on data minings", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870C (16 August 2023); https://doi.org/10.1117/12.3004917

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KEYWORDS

Data modeling

Solar radiation models

Safety

Education and training

Environmental monitoring

Image enhancement

Image processing

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