Improving predictions of rock tunnel squeezing with ensemble Q-learning and online Markov chain.

ABSTRACT: 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 histor

SUBMITTER: Fard HS 

PROVIDER: S-EPMC11447021 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature