Fard Hadi S, Parvin Hamid, Mahmoudi Mohammadreza
Frontop Engineering Ltd., Markham, Canada.
Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad university, Nourabad Mamasani, Fars, Iran.
Sci Rep. 2024 Oct 2;14(1):22885. doi: 10.1038/s41598-024-72998-5.
Predicting rock tunnel squeezing in underground projects is challenging due to its intricate and unpredictable nature. This study proposes an innovative approach to enhance the accuracy and reliability of tunnel squeezing prediction. The proposed method combines ensemble learning techniques with Q-learning and online Markov chain integration. A deep learning model is trained on a comprehensive database comprising tunnel parameters including diameter (D), burial depth (H), support stiffness (K), and tunneling quality index (Q). Multiple deep learning models are trained concurrently, leveraging ensemble learning to capture diverse patterns and improve prediction performance. Integration of the Q-learning-Online Markov Chain further refines predictions. The online Markov chain analyzes historical sequences of tunnel parameters and squeezing class transitions, establishing transition probabilities between different squeezing classes. The Q-learning algorithm optimizes decision-making by learning the optimal policy for transitioning between tunnel states. The proposed model is evaluated using a dataset from various tunnel construction projects, assessing performance through metrics like accuracy, precision, recall, and F1-score. Results demonstrate the efficiency of the ensemble deep learning model combined with Q-learning-Online Markov Chain in predicting surrounding rock tunnel squeezing. This approach offers insights into parameter interrelationships and dynamic squeezing characteristics, enabling proactive planning and support measures implementation to mitigate tunnel squeezing hazards and ensure underground structure safety. Experimental results show the model achieves a prediction accuracy of 98.11%, surpassing individual CNN and RNN models, with an AUC value of 0.98.
由于岩石隧道挤压现象复杂且不可预测，因此预测地下工程中的岩石隧道挤压具有挑战性。本研究提出了一种创新方法，以提高隧道挤压预测的准确性和可靠性。所提出的方法将集成学习技术与Q学习和在线马尔可夫链相结合。在一个包含隧道参数（包括直径（D）、埋深（H）、支护刚度（K）和掘进质量指标（Q））的综合数据库上训练一个深度学习模型。同时训练多个深度学习模型，利用集成学习来捕捉不同模式并提高预测性能。Q学习-在线马尔可夫链的集成进一步优化了预测。在线马尔可夫链分析隧道参数的历史序列和挤压类别转换，建立不同挤压类别之间的转换概率。Q学习算法通过学习在隧道状态之间转换的最优策略来优化决策。使用来自各种隧道建设项目的数据集对所提出的模型进行评估，通过准确率、精确率、召回率和F1分数等指标评估性能。结果表明，集成深度学习模型与Q学习-在线马尔可夫链相结合在预测围岩隧道挤压方面具有有效性。这种方法提供了对参数相互关系和动态挤压特征的见解，能够进行主动规划并实施支护措施，以减轻隧道挤压危害并确保地下结构安全。实验结果表明，该模型的预测准确率达到98.11%，超过了单个卷积神经网络（CNN）和循环神经网络（RNN）模型，AUC值为0.98。
