基于ALA-SVR的爆破块度分布预测OA

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.