Deformation extent prediction of roadway roof during non-support period using support vector regression combined with swarm intelligent bionic optimization algorithms

Abstract

Settlement deformation of the roof during non-support period in deep roadway excavation poses the most immediate hazard to coal mine roadway engineering. Thus, accurately predicting the roof’s displacement during this period is crucial in minimizing potential risks. Considering the intricate nature of factors that influence deep roadway engineering, this research employs Support Vector Regression (SVR) as the primary tool, coupled with four swarm intelligent bionic optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO) and Grasshopper Optimization Algorithm (GOA) to enhance the prediction performance and optimize hyper-parameters. The prediction models involve seven influential factors that are selected from the underground test roadway. These factors form a comprehensive evaluation system which is based on artificial intelligence techniques. To evaluate the performance of different prediction models, three error correlation coefficient indices are used, include root mean square error (RMSE), variance accounted for (VAF) and coefficient of determination (R2). Of all the models, the GWO-e-SVR model exhibits the best overall performance in predicting deformation extent of the roof during non-support period. With a swarm size of 100, the RMSE, VAF and R2 of training datasets are 0.3126, 15.05% and 0.8767, respectively. The RMSE, VAF and R2 of testing datasets are 0.3245, 16.43% and 0.8655, respectively. The model demonstrated outstanding predictive accuracy in small-scale experiments, signifying its strong generalization and robustness. Subsequently, a sensitivity analysis was conducted to expound on the impact of conclusive input parameters on the end target output, specifically the duration of non-support in the space–time effect concerning roof’s settlement during the non-supported state. These findings hold significant implications for the upcoming support construction scheme design and reinforcement support operation.