Evaluation of rheological and mechanical performance of gangue-based cemented backfill material: a novel hybrid machine learning approach.

Waste discharge and surface damage are the unavoidable consequences of coal mining. However, filling waste into goaf can help reuse waste materials and protect the surface environment. In this paper, it is proposed to fill coal mine goaf with gangue-based cemented backfill material (GCBM), while the rheological and mechanical performances of GCBM influence the filling effect. A method that combines laboratory experiments and machine learning is proposed to predict the GCBM performance. The correlation and significance of eleven factors that affect GCBM are analyzed using random forest method, and the nonlinear effects of the main factors on the slump and uniaxial compressive strength (UCS) are analyzed. The optimization algorithm is improved, and the improved algorithm is combined with a support vector machine to build a hybrid model. The hybrid model is systematically verified and analyzed using predictions and convergence performances. The results demonstrate that the R of the predicted and measured values is 0.93 and the root mean square error is 0.1912, indicating that the improved hybrid model can effectively predict the slump and UCS and can promote sustainable waste use.