The utilization of solid waste for filling mining presents substantial economic and environmental advantages, making it the primary focus of current filling mining technology development. To enhance the mechanical properties of superfine tailings cemented paste backfill (SCPB), this study conducted response surface methodology experiments to investigate the impact of various factors on the strength of SCPB, including the composite cementitious material, consisting of cement and slag powder, and the tailings' grain size. Additionally, various microanalysis techniques were used to investigate the microstructure of SCPB and the development mechanisms of its hydration products. Furthermore, machine learning was utilized to predict the strength of SCPB under multi-factor effects. The findings reveal that the combined effect of slag powder dosage and slurry mass fraction has the most significant influence on strength, while the coupling effect of slurry mass fraction and underflow productivity has the lowest impact on strength. Moreover, SCPB with 20% slag powder has the highest amount of hydration products and the most complete structure. When compared to other commonly used prediction models, the long-short term memory neural network (LSTM) constructed in this study had the highest prediction accuracy for SCPB strength under multi-factor conditions, with root mean square error (RMSE), correlation coefficient (R), and variance account for (VAF) reaching 0.1396, 0.9131, and 81.8747, respectively. By optimizing the LSTM using the sparrow search algorithm (SSA), the RMSE, R, and VAF improved by 88.6%, 9.4%, and 21.9%, respectively. The research results can provide guidance for the efficient filling of superfine tailings.
Keywords: filling mining; machine learning; strength prediction; superfine tailings; underflow productivity.