利用BP神经网络优化石膏基复合胶凝材料强度的影响条件

为掌握不同掺量对石膏胶凝材料强度的影响规律，以建筑脱硫石膏等为原料，采用正交试验的方法制备石膏基复合胶凝材料，并建立影响其7 d抗压强度主要因素的BP神经网络模型，在此基础上对晶须掺量等不同影响因素的条件进行了优化。结果表明，各因素对石膏砌块7 d抗压强度的影响由小到大依次为水泥:矿渣掺量、减水剂掺量、缓凝剂掺量、中和渣掺量和晶须掺量；而利用BP神经网络模型优化后的工艺参数：晶须掺量为6.40%、聚羧酸减水剂掺量为1.28%、水泥:矿渣掺量为1:3、煅烧中和渣掺量为2.00%及柠檬酸缓凝剂掺量为0.114%。在此条件下，所得石膏胶凝材料的7 d抗压强度为14.62 MPa，与正交试验结果相比提高了2.024%；同时利用BP神经网络模型进行优化可在一定程度上降低外加剂的用量，其中聚羧酸减水剂、晶须和柠檬酸缓凝剂的掺量分别减少了0.12%、0.60%和0.006%。研究对石膏类废弃物的回收及其在矿山充填中的应用有一定的参考意义。

Optimization of influence conditions on strength of gypsum-based composite cementitious materials using BP neural network

To grasp the effect of different dosages on the strength of gypsum cementitious material, the gypsum-based composite cementitious materials were prepared by orthogonal test method using building desulfurized gypsum as raw materials, and the BP Neural Network of the main factors affecting 7d compressive strength of desulfurized gypsum block was established. Based on the BP neural network model, the conditions of different influencing factors such as whisker content were optimized. The results show that the effects of various factors on the 7d compressive strength of gypsum block were in descending order cement, slag content, water reducer content, retarder content, neutralization slag content, and whisker content. However, the process parameters optimized by the BP neural network model are that the whisker content is 6.40%, the polycarboxylate superplasticizer content is 1.28%, the ratio of cement to slag content was 1 of 3, the calcination neutralization slag content is 2.00% and the dosage of citric acid retarder is 0.114%. Under this condition, the obtained gypsum-based composite cementitious material which 7d compressive strength is 14.62MPa increased by 2.024% compared with the results of the orthogonal test. And optimization using BP neural network model can reduce the dosage of admixtures to a certain extent, which the dosage of polycarboxylate superplasticizer, whiskers and citric acid retarder decrease by 0.12%, 0.60% and 0.006%, respectively. The research has important reference significance on the recycling of gypsum waste and reducing the cost of mine filling.