南华大学学报（自然科学版）2026，Vol.40Issue(1)：16-23,8.DOI:10.19431/j.cnki.1673-0062.2026.01.003
基于ALA-SVR的爆破块度分布预测
ALA-SVR-based prediction of blast block size distribution
摘要
Abstract
To enhance the prediction accuracy of blasting fragmentation distribution,the artificial lemming algorithm(ALA)is employed to optimize the hyperparameters of support vector regression(SVR),thereby constructing the ALA-SVR model.Bench height,bur-den,stemming length,row spacing,hole spacing,and rock hardness are selected as input parameters.The R-R function is utilized to characterize the distribution of blasting frag-mentation,with the parameters n and x0 governing the R-R function designated as output parameters.Data collected from 20 actual blasting operations at a limestone quarry are used to train and test the ALA-SVR model.The results indicate that the predictive perform-ance of the SVR model is significantly improved following optimization by metaheuristic algo-rithms.Specifically,the mean relative error(MRE),root mean square error(RMSE),and correlation coefficient(R2)of the ALA-SVR predictions are 3.9832%,0.8374,and 99.9426%,respectively.These metrics outperform the HFOA-SVR and SDO-SVR predictive models established under identical conditions,demonstrating that the proposed ALA-SVR model possesses high prediction accuracy and broad applicability.关键词
爆破块度/机器学习/SVR/优化预测Key words
blast blockiness/machine learning/SVR(support vector regression)/optimized pre-diction分类
矿业与冶金引用本文复制引用
杨仕教,张仕豪,郭钦鹏,王之鹏..基于ALA-SVR的爆破块度分布预测[J].南华大学学报（自然科学版）,2026,40(1):16-23,8.基金项目
南华大学研究生科研创新项目(253YXC009) （253YXC009）
