doi:10.16186/j.cnki.1673-9787.2020090044

Received:2020/09/09

Revised:2021/03/22

Published:2022/05/15

Research on prediction of backfill strength based on particle swarm optimization algorithm

HUANG Xiaohong1, CUI Hejia1, LIU Zhiyi2,3, LIU Liping2,3, ZHANG Kaiyue1

1.College of Information Engineering， North China University of Science and Technology， Tangshan  063210 ， Hebei， China;2.College of Mining Engineering ，North China University of Science and Technology， Tangshan  063210 ，Hebei， China;3.Development and Safety Key Lab of Hebei Province，Tangshan 063210，Hebei，China

Abstract: In order to quickly and effectively determine the strength of the backfill ,a particle swarm optimiza- tion algorithm(PSO)was established for the global optimization of the support vector machine parameters by taking the ratio of lime to sand , solid content and curing age as input factors , and the output factor as the uniax- ial compressive strength of the backfill. The research results show that the model had good prediction perform- ance , achieving high correlation coefficients ( training set 0.996 , test set 0. 993) , and low mean square error value ( training set is 0.000 393 , test set is 0.000 726 13). The comparison and prediction of 216 samples col- lected through indoor experiments proved that the model could accurately predict the strength of the filling body , greatly reduce the amount of physical test and the test period,and provide a new idea for mine filling.

Key words:particle swarm optimization algorithm;prediction of the uniaxial compressive strength;backfill;supportvectormachine

基于粒子群优化算法的充填体单轴抗压强度预测研究_黄晓红.pdf