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Journal of Soft Computing Paradigm

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Home / Archives / Volume-6 / Issue-1 / Article-2

Volume - 6 | Issue - 1 | march 2024

Deep Learning Network for Remote Monitoring of Thermal Exchange Tunnels
Dr. M. Duraipandian 
Pages: 13-25
Cite this article
Duraipandian, D. M. (2024). Deep Learning Network for Remote Monitoring of Thermal Exchange Tunnels. Journal of Soft Computing Paradigm, 6(1), 13-25. doi:10.36548/jscp.2024.1.002
Published
23 March, 2024
Abstract

Monitoring and predicting ground settlement throughout tunnel construction is critical to ensuring the safe and accurate use of urban tunnel systems. The accurate and efficient diagnosis of such settlement can decrease hazards while improving the safety and dependability of these initiatives. However, typical tunnel inspection procedures are time-consuming, costly, and heavily reliant on human subjectivity. The trained model's accuracy was evaluated by comparing its findings across extended operating durations using the same and different thermal operational patterns as those utilized for training. Deep learning, one of the most powerful Artificial Intelligence approaches, is required for the tunnel's settlement predicting challenge. Nevertheless, deep neural networks frequently want huge quantities of training data. In the method we used, CNN-LSTM models were trained on datasets of various sizes and attributes. The results suggest that both of the proposed models may achieve a little inaccuracy under specific situations.

Keywords

Tunnel Construction Deep learning CNN-LSTM Models Mean Square Errors Evaluation Metrics

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