抗压强度是表征充填体力学性质的重要指标，快速、精准地确定充填体抗压强度值，对于保障采场安全意义重大。为了探究多源煤基固废充填体强度影响规律，准确预测煤基固废充填体强度来指导煤矿安全、高效、绿色开采，以煤矸石为粗料，脱硫石膏、气化渣、炉底渣为细料，粉煤灰和水泥为胶凝剂。通过正交试验研究了煤基固废充填体抗压强度的影响因素，采用灰色关联度分析法分析各试验因素与充填体抗压强度之间的关联度，采用5-11-3的三层反向传播（back propagation, BP）神经网络结构开展不同养护龄期煤基固废充填体强度预测。结果表明：浓度、气化渣和脱硫石膏掺量对抗压强度的影响随养护龄期的增加逐渐增大，粉煤灰和炉底渣掺量对抗压强度的影响随养护龄期的增加呈先增后减。而且正交试验协同BP神经网络能减少试验次数又不失一般性，本次煤基固废充填体强度预测相关系数R为0.999 87。可见，高浓度和高掺量气化渣及脱硫石膏对于要求高强度的充填体具有重要意义，同时，正交试验协同BP神经网络可以准确预测充填体强度。

Compressive strength is an important index to characterize the mechanical properties of filling body. It is of great significance to ensure the safety of stope by quickly and accurately determining the compressive strength of filling body. In order to explore the influence law of the strength of multi-source coal-based solid waste filling body and accurately predict the strength of coal-based solid waste filling body to guide the safe, efficient and green mining of coal mine, the influencing factors of the compressive strength of coal-based solid waste filling body were studied by orthogonal test with coal gangue as coarse material, desulfurization gypsum, gasification slag and bottom slag as fine material, fly ash and cement as cementing agent. The grey correlation degree analysis method was used to analyze the correlation between each test factor and the compressive strength of filling body. The strength prediction of coal-based solid waste backfill at different curing ages was carried out by using 5-11-3 three-layer back propagation (BP) neural network structure. The results show that the influence of concentration, gasification slag and desulfurization gypsum content on compressive strength increases with the increase of curing age, and the influence of fly ash and bottom slag content on compressive strength increases first and then decreases with the increase of curing age. Orthogonal test combined with BP neural network can reduce the number of tests without losing generality. The correlation coefficient R of strength prediction of coal-based solid waste backfill is 0.999 87. It can be seen that high concentration and high content of gasification slag and desulfurization gypsum are of great significance for filling body requiring high strength. At the same time, orthogonal test combined with BP neural network can accurately predict the strength of filling body.