TY -的A2 Yilmaz Erol AU -肖,Chongchun AU -王,新民AU - Chen Qiusong AU - Bin,冯AU - Wang Yihan AU -魏,魏PY - 2020 DA - 2020/12/24 TI -强度调查Silt-Based巩固了粘贴回填使用实验室的实验和深层神经网络SP - 6695539六世- 2020 AB -巩固了粘贴回填(CPB)技术已成功用于世界各地的矿山尾矿的回收。然而,在煤矿中的应用是有限的矿山尾矿由于缺乏工作总量。在这部作品中,利用黄河泥沙的淤积的可行性(年)聚集在心脏调查。胶结材料选择的普通硅酸盐水泥(OPC)、OPC +煤矸石(CG)和OPC +粉煤灰(CFA)。进行了大量的实验研究无侧限抗压强度(UCS)体的样品。实验结果的讨论后,数据收集和处理后的数据准备。深层神经网络(款)是用来预测心脏的UCS的影响变量,也就是说,OPC的比例,CG, CFA, y,固体含量、固化时间。结果显示如下:(i)固体含量、水泥内容(水泥/砂率)和固化时间与UCS呈现正相关。可以使用CG作为一种OPC的替代品,而添加CFA显著增加心脏的UCS。(2)最优训练集规模80%,运行的数量是36获得聚合的结果。 (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. SN - 1687-8434 UR - https://doi.org/10.1155/2020/6695539 DO - 10.1155/2020/6695539 JF - Advances in Materials Science and Engineering PB - Hindawi KW - ER -