Autonomous prediction of rock deformation in fault zones of coal roadways using supervised machine learning

Abstract

Coal roadway fault zones typically rely on multiple re-excavations and repeated reinforcements for control, posing challenges in achieving precise control of the initial support strength of the roadway. Therefore, predicting the deformation of the surrounding rock during the stability phase of fault zone excavation is a prerequisite for the precise control of the initial support strength. This study employs supervised learning algorithms to extract 14 sets of features as input variables for deformation prediction, focusing on the displacement of the roof and ribs in coal roadway fault zones. By constructing a dataset using field data, the reliability of the four different algorithms for predicting the displacement of the roof and rib in fault zones of roadways is validated. This predicts the deformation in the fault zone surrounding the rocks and ultimately categorises the failure types of the fault zone roadways into four classes. The results indicate that the gradient boosting decision tree method exhibits the highest reliability in predicting the roof and rib displacements, with the maximum prediction and absolute mean errors of the surrounding rock deformation controlled within 30.3 mm. This provides a guiding path for the precise control of the surrounding rock in the early excavation stage of fault-zone roadways.