Prediction of Dynamic Compressive Strength of Cemented Backfill Based on CSSA-BPNN Model
The stability of cemented backfill is compromised via blasting disturbances in the two-step stoping method.To obtain the dynamic compressive strength of the backfill quickly and accurately,40 sets of uniaxial impact experiments with different strain rates were conducted using the separated Hopkinson pressure bar(SHPB).Input parameters included the lime-sand ratio,backfill density,curing age,and average strain rate,while the dynamic compressive strength of the backfill served as the output parameter.A prediction model,optimized using the backpropagation neural network(BPNN)based on the logistic chaotic sparrow search algorithm(CSSA),was established and compared with the BPNN optimized by the traditional BPNN and sparrow search algorithm.The results demonstrate that the average relative error of the CSSA-BPNN model is 4.11％,with fitting correlation coefficients among the predicted and measured values exceeding 0.96,indicating high prediction accuracy.The root-mean-square error of the CSSA-BPNN model is 0.395 0 MPa,the average absolute error is 0.359 2 MPa,and the coefficient of determination is 0.995 2,all of which outperform the other two prediction models.This enables accurate prediction of the dynamic compressive strength of the backfill,greatly reducing the need for physical experiments and providing a novel approach to the strength design of mine cemented backfill.
万方数据
维普
