Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Cemented paste backfill (CPB) is wildly used in mines production practices around the world. The strength of CPB is the core of research which is affected by factors such as slurry concentration and cement content. In this paper, a research on the UCS is conducted by means of a combination of laboratory experiments and machine learning. BPNN, RBFNN, GRNN and LSTM are trained and used for UCS prediction based on 180 sets of experimental UCS data. The simulation results show that LSTM is the neural network with the optimal prediction performance (The total rank is 11). The trial-and-error, PSO, GWO and SSA are used to optimize the learning rate and the hidden layer nodes for LSTM. The comparison results show that GWO-LSTM is the optimal model which can effectively express the non-linear relationship between underflow productivity, slurry concentration, cement content and UCS in experiments (R=0.9915, RMSE = 0.0204, VAF = 98.2847 and T = 16.37 s). The correction coefficient (k) is defined to adjust the error between predicted UCS in laboratory (UCSM) and predicted UCS in actual engineering (UCSA) based on extensive engineering and experimental experience. Using GWO-LSTM combined with k, the strength of the filling body is successfully predicted for 153 different filled stopes with different stowing gradient at different curing times. This study provides both effective guidance and a new intelligent method for the support of safety mining. Backfill; Uniaxial compressive strength; Cemented paste backfill; Neural network; Optimization