黄晓红, 崔贺佳, 刘志义,等.基于粒子群优化算法的充填体单轴抗压强度预测研究[J].河南理工大学学报（自然科学版）,2022,41(3):32-37.

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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1, 崔贺佳1, 刘志义2,3, 刘利平2,3, 张凯月1

1.华北理工大学 信息工程学院，河北 唐山 063210;2.华北理工大学 矿业工程学院，河北 唐山 063210;3.河北省矿业开发与安全技术 重点实验室（华北理工大学），河北 唐山 063210

摘要:为快速有效确定充填体单轴抗压强度，以灰砂比、固体含量和养护龄期作为输入因子，充填体单轴抗压强度作为输出因子，建立一种粒子群优化算法（PSO），对支持向量机（SVM）参数进行全局优化的预测模型。结果表明，该模型预测性能较好，相关系数高（训练集为0.996，测试集为0.993），均方误差值低（训练集为0.000 393，测试集为0.000 726 13）；通过室内试验对采集的216个试样进行预测与对比，证明模型可以准确地预测充填体单轴抗压强度，大幅度减少物理试验量及缩短试验周期，为矿山充填提供一种新思路。

关键词:粒子群优化算法;单轴抗压强度预测;充填体;支持向量机

doi:10.16186/j.cnki.1673-9787.2020090044

基金项目:国家自然科学基金资助项目（51774137）；河北省高等学校科学技术重点研究项目（ZD2020152）

收稿日期:2020/09/09

修回日期:2021/03/22

出版日期: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