摘要
以页岩为研究对象,分别采用多元线性回归(MLR)及最小二乘支持向量机(LS-SVM)建立了页岩的单轴抗压强度及抗拉强度预测模型,考虑的间接指标包括:岩石密度、点荷载强度及纵波波速,并对上述两种预测模型进行了性能检验及比较。结果表明:页岩强度与密度、点荷载强度、纵波波速呈较好的线性关系,相关系数均大于0.89;MLR和LS-SVM方法均可得到较高精度的强度值,但单轴抗压强度的预测精度比抗拉强度高,更适合于抗压强度的预测。两类模型在预测岩石单轴抗压强度时效果相当,但LS-SVM方法更适合于抗拉强度的预测。
Taking shale as the research object, considering the rock density, point load strength and P--wave velocity, pre- diction models of uniaxial compressive strength and tensile strength were built by multiple linear regression （MLR） and least squares support vector machine （LS--SVM）. And their performances were tested and compared. The results showed that the strength of shale had good linear relations with rock density, point load strength and P--wave velocity, and the correlation coefficients were all greater than 0.89. MLR and LS--SVM could both obtain strength values with high accu- racy. But the prediction accuracy of uniaxial compressive strength was higher than that of tensile strength, which proved that MLR and LS--SVM was much more suitable to predict compressive strength. The performances of two methods were equivalent for predicting compressive strength, while LS--SVM method was much more suitable to predict tensile strength.
作者
李文
谭卓英
LI Wen TAN Zhuoying(School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)
出处
《矿业研究与开发》
CAS
北大核心
2016年第11期36-40,共5页
Mining Research and Development
基金
国家自然科学基金资助项目(51574015)
关键词
页岩
强度
预测模型
多元线性回归
最小二乘支持
向量机
Shale , Strength, Prediction model, Multivariable linear regression （MLR）, Least squares support vector ma- chine （LS- SVM）
