Prediction of surrounding rock grade for ultra-deep roadway engineering using BPNN combined with optimal algorithms

Purpose This paper attempts to combine the application of artificial intelligence in predicting and evaluating the classification of surrounding rock grades and provides guidance for subsequent support design and reinforcement support operations. Design/methodology/approach This paper discusses the use of BPNN as the primary tool, combined with three swarm bionic optimization algorithms (GA, PSO, GWO), to solve stability evaluation and grade prediction of surrounding rock in ultra-deep roadway excavation. Findings Taking the Great Wall ore group as the core and the Shanghaimiao mining area as the extension, the optimal model is applied to the classification of surrounding rock grade in ultra-deep roadway engineering. Prediction results show that the performance of BPNN models is excellent. Research limitations/implications Due to the limitations of geological conditions and construction environment in deep coal mines, the period of roadway excavation is too long, resulting in less data collection. Practical implications The prediction results can provide guidance for the excavation method, support scheme correction and reinforcement support scheme design of deep coal mine roadway engineering. Social implications It provides guidance for deep mining of coal mine (the premise of surrounding rock support stability), so as to ensure the economic and safety benefits of coal enterprises. Originality/value The neural network is applied to rock mechanics in a deep site for the first time, which is used to solve the prediction direction of surrounding rock grade evaluation. The index of the input layer is determined by combining the “three high and one disturbance” with the on-site construction situation, which is closer to the actual project. The swarm intelligent bionic algorithms are selected to optimize the hyperparameters of back propagation neural network, so as to improve the accuracy of the models. The classification and evaluation system of surrounding rock for the Great Wall ore group is constructed, which is the core of Shanghaimiao mining area in the northwest of China, guiding the dynamic adjustment of on-site excavation and support operations.