Back-filling mining method with whole tailings as material can not only reduce the mining cost

but also recycle solid wastes. At the same time

filling solid waste underground protects environment and maintains ecological balance. Since whole tailing’s particle size is small

whole tailings shall be mix with rod-mill tailings and Gobi sand for mine filling. It is necessary to research the optimal proportion of mixed filling aggregate. First

we tested the particle-size gradation and the nonuniform coefficient of rod-mill tailings

Gobi sand and whole tailings. Then we carried out 9 sets of strength tests of cemented fillings with different mixing ratios. On this base

we built the artificial neural network model for strength predictions and trained it with the experimental samples. Finally

we predicted the strength of mixed filling aggregate of orthogonal design by using the prediction model

and we revealed the relationship between filling body’s strength and characteristic value of mixed filling aggregate by using range analysis and regression analysis. The research results show that

with different mixed aggregate’s average sizes and nonuniform coefficients

the filling body has significant strength differences between early and late stage. Mixed aggregate with smaller average particle size has a higher strength in early stage; while the larger is more inclined to increase the filling body’s strength in late stage.