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.

中文翻译：

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基于实验室实验和深层神经网络的粉砂基水泥浆回填强度研究

水泥浆回填（CPB）技术已成功用于世界各地矿山尾矿的回收。然而，由于缺乏可作为骨料的矿渣，其在煤矿中的应用受到限制。在这项工作中，研究了将黄河淤泥（YRS）中的淤泥用作CPB中的骨料的可行性。选择的水泥材料为普通波特兰水泥（OPC），OPC +煤石（CG）和OPC +粉煤灰（CFA）。进行了大量的实验室实验以研究CPB样品的无侧限抗压强度（UCS）。在讨论了实验结果之后，在数据收集和处理之后准备了一个数据集。使用深度神经网络（DNN）从其影响变量即OPC，CG，CFA和YS，固含量和固化时间。结果表明：（i）固体含量，水泥含量（水泥/砂比）和固化时间与UCS呈正相关。CG可以用作OPC的一种替代品，而添加CFA可以显着增加CPB的UCS。（ii）最佳训练集大小为80％，运行次数为36，以获得收敛结果。（iii）GA在DNN架构调整方面非常有效，在第17次迭代中发现了最佳DNN架构。（iv）最佳DNN在基于淤泥的CPB的UCS预测中具有出色的性能（训练集的相关系数为0.97，测试集的相关系数为0.99）。（v）固化时间，CFA比例，