Abstract:
Existing methods for predicting hydraulic support loads in mine roadways usually assume a static spatio-temporal mining arrangement, which ignores the dynamic loads of the far-field surrounding rocks and affects the accuracy of rockburst prediction. In order to ensure safe mining, real-time and accurate predictive assessment of potential rockburst is necessary. In this paper, a Sparrow Search Algorithm-Random Forest (SSA–RF) prediction method based on digital twin and machine learning is proposed. By analyzing the interaction between the support system and the surrounding rock, a digital twin model of the two-column support is established, and the interaction mapping and synchronous feedback between the physical entity and its digital twin are realized based on data driving. By comparing and analyzing the calculated and real values of the attitude variables during the column lifting process of the two-column support, it is found that compared with the physical entity of the support, the digital twin model has an average error of 0.14° in angle and 6.15 mm in length, which is in line with the accuracy requirements. In addition, the Sparrow Search Algorithm was used to optimize the number of decision trees and node features in the Random Forest. Compared with using a single prediction model, the SSA–RF prediction modeling improves the convergence speed and optimization ability. The experimental results show that the SSA–RF method proposed in this paper performs optimally compared with prediction algorithms such as Long Short-Term Memory (LSTM), Random Forest (RF) and Support Vector Machine (SVM), and its prediction accuracy reaches 85.89% and 91.09% on the central support and end support data sets, respectively. In addition, it is found that the roof in the area of the central support is prone to fracture instability, which will destroy the vertical stress support conditions in the central area of the working face, thus leading to a larger range of load variations in the central support with a slightly lower prediction accuracy than that of the end support. The above results provide some theoretical reference for further research on the occurrence mechanism of rockburst in coal mine and accurate prediction of potential rockburst.