Strength Optimization and Prediction of Cemented Tailings Backfill Under Multi-Factor Coupling  

作　　者：HU Yafei LI Keqing HAN Bin JI Kun 胡亚飞;李克庆;韩斌;吉坤(School of Civil and Resource Engineering,Key Laboratory of Ministry of Education for High-Efficient Mining and Safety of Metal Mines,University of Science and Technology Beijing,Beijing,100083,China)

机构地区：[1]School of Civil and Resource Engineering,Key Laboratory of Ministry of Education for High-Efficient Mining and Safety of Metal Mines,University of Science and Technology Beijing,Beijing,100083,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2024年第5期845-856,共12页上海交通大学学报（英文版）

基　　金：the National Key Technology Research and Development Program of China(Nos.2018YFC1900603 and 2018YFC0604604)。

摘　　要：In order to solve the problem of strength instability of cemented tailings backfill(CTB)under low temperature environment(≤20℃),the strength optimization and prediction of CTB under the influence of multiple factors were carried out.The response surface method(RSM)was used to design the experiment to analyze the development law of backfill strength under the coupling effect of curing temperature,sand-cement ratio and slurry mass fraction,and to optimize the mix proportion;the artificial neural network algorithm(ANN)and particle swarm optimization algorithm(PSO)were used to build the prediction model of backfill strength.According to the experimental results of RSM,the optimal mix proportion under different curing temperatures was obtained.When the curing temperature is 10-15℃,the best mix proportion of sand-cement ratio is 9,and the slurry mass fraction is 71%;when the curing temperature is 15-20℃,the best mix proportion of sand-cement ratio is 8,and the slurry mass fraction is 69%.The ANN-PSO intelligent model can accurately predict the strength of CTB,its mean relative estimation error value and correlation coefficient value are only 1.95%and 0.992,and the strength of CTB under different mix proportion can be predicted quickly and accurately by using this model.

关 键 词：cemented tailings backfill(CTB) response surface method(RSM) multi-factor coupling strength optimization intelligent prediction model 

分 类 号：TD853[矿业工程—金属矿开采]