Strength Investigation of the Silt-Based Cemented Paste Backfill Using Lab Experiments and Deep Neural Network

Abstract

The cemented paste backfill (CPB) technology has been successfully used for the recycling of mine tailings all around the world. However, its application in coal mines is limited due to the lack of mine tailings that can work as aggregates. In this work, the feasibility of using silts from the Yellow River silts (YRS) as aggregates in CPB was investigated. Cementitious materials were selected to be the ordinary Portland cement (OPC), OPC + coal gangue (CG), and OPC + coal fly ash (CFA). A large number of lab experiments were conducted to investigate the unconfined compressive strength (UCS) of CPB samples. After the discussion of the experimental results, a dataset was prepared after data collection and processing. Deep neural network (DNN) was employed to predict the UCS of CPB from its influencing variables, namely, the proportion of OPC, CG, CFA, and YS, the solids content, and the curing time. The results show the following: (i) The solid content, cement content (cement/sand ratio), and curing time present positive correlation with UCS. The CG can be used as a kind of OPC substitute, while adding CFA increases the UCS of CPB significantly. (ii) The optimum training set size was 80% and the number of runs was 36 to obtain the converged results. (iii) GA was efficient at the DNN architecture tuning with the optimum DNN architecture being found at the 17th iteration. (iv) The optimum DNN had an excellent performance on the UCS prediction of silt-based CPB (correlation coefficient was 0.97 on the training set and 0.99 on the testing set). (v) The curing time, the CFA proportion, and the solids content were the most significant input variables for the silt-based CPB and all of them were positively correlated with the UCS.