Prediction of Backfill Strength Based on Support Vector Regression Improved by Grey Wolf Optimization

• Published in: Shanghai jiao tong da xue xue bao Vol. 28; no. 5; pp. 686 - 694
• Main Authors: Zhang, Bo, Li, Keqing, Hu, Yafei, Ji, Kun, Han, Bin
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.10.2023; Springer Nature B.V
• Subjects: Architecture; Backfill; Compressive strength; Computer Science; Electrical Engineering; Engineering; Kernel functions; Life Sciences; Materials Science; Mine tailings; Model accuracy; Optimization; Parameters; Prediction models; Regression models; Slurries; Support vector machines; Technical services; 支持向量回归; A; backfill strength; TD 853; prediction model; underground mining; support vector regression (SVR); 地下采矿; 充填体强度; 预测模型; grey wolf optimization (GWO); 灰狼优化算法; grey wolf optimization(GWO); support vector regression(SVR)
• ISSN: 1007-1172; 1995-8188
• DOI: 10.1007/s12204-022-2408-7

In order to predict backfill strength rapidly with high accuracy and provide a new technical support for digitization and intelligentization of mine, a support vector regression (SVR) model improved by grey wolf optimization (GWO), GWO-SVR model, is established. First, GWO is used to optimize penalty term and kernel function parameter in SVR model with high accuracy based on the experimental data of uniaxial compressive strength of filling body. Subsequently, a prediction model which uses the best two parameters: best_ c and best_ g is established with the slurry density, cement dosage, ratio of artificial aggregate and tailings and curing time taken as input factors, and uniaxial compressive strength of backfill as output factors. The root mean square error of this GWO-SVR model in predicting backfill strength is 0.143 and the coefficient of determination is 0.983, which means that the predictive effect of this model is accurate and reliable. Compared with the original SVR model without the optimization of GWO and particle swam optimization (PSO)-SVR model, the performance of GWO-SVR model is greatly promoted. The establishment of GWO-SVR model provides a new tool for predicting backfill strength scientifically.