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Real-Time Rock Mass Prediction in TBM Tunneling by godaranhpc is a document available to read on EtoBox.

This study presents a novel TBM-rock mutual feedback perception method leveraging data mining to predict rock mass conditions in real-time during tunnel boring machine (TBM) operations. By analyzing a database of 10,807 tunneling cycles, the method employs spectral clustering and deep neural networks to classify rock conditions and dynamically adjust tunneling parameters, enhancing efficiency and safety. The proposed approach demonstrates superior prediction accuracy compared to traditional machine learning

Author

godaranhpc

Language

EN