当前位置: X-MOL 学术Transp. Geotech. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Artificial intelligence forecasting models of uniaxial compressive strength
Transportation Geotechnics ( IF 5.3 ) Pub Date : 2020-12-17 , DOI: 10.1016/j.trgeo.2020.100499
Arsalan Mahmoodzadeh , Mokhtar Mohammadi , Hawkar Hashim Ibrahim , Sazan Nariman Abdulhamid , Sirwan Ghafoor Salim , Hunar Farid Hama Ali , Mohammed Kamal Majeed

The uniaxial compressive strength (UCS) is a vital rock geomechanical parameter widely used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the acquisition of high-quality core samples is not always possible, researchers often indirectly estimate these parameters. The main objective of the present study is to evaluate the performance of the long short term memory (LSTM), deep neural networks (DNN), K-nearest neighbor (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision tree (DT) to predict the UCS of different rock types of Claystone, Granite, Schist and Sandstone, Travertine, Limestone, Slate, Dolomite and Marl acquired from almost all quarry locations of Iran. 170 data sets, including porosity (n), Schmidt hammer (SH), P-wave velocity (Vp), and point load index (Is(50)) were applied in the methods. Finally, a comparison was made between the results made by the prediction methods. To assess the performance ability of the applied methods, the 5-fold cross-validation (CV) was considered. The results proved that computational intelligence approaches are capable of predicting UCS. On the whole, the GPR with a correlation coefficient (R2) of 0.9955 and a route mean square error (RMSE) of 0.52169, performs best. Lastly, the UCS prediction intelligence methods were ordered as GPR, DT, SVR, LSTM, DNN and KNN, respectively.



中文翻译:

单轴抗压强度的人工智能预测模型

单轴抗压强度(UCS)是重要的岩石地质力学参数,广泛用于诸如隧道,大坝和岩石边坡稳定性等岩石工程项目中。由于并非总是能够获得高质量的核心样品,因此研究人员经常间接估计这些参数。本研究的主要目的是评估长期短期记忆(LSTM),深度神经网络(DNN),K近邻(KNN),高斯过程回归(GPR),支持向量回归(SVR)的性能以及决策树(DT)来预测从伊朗几乎所有采石场获得的粘土岩,花岗岩,片岩和砂岩,石灰华,石灰石,板岩,白云岩和泥灰岩的不同岩石类型的UCS。170个数据集,包括孔隙率(n),施密特锤(SH),P波速度(V p),并在方法中应用点载荷指数(Is (50))。最后,比较了预测方法的结果。为了评估所应用方法的性能,考虑了5倍交叉验证(CV)。结果证明,计算智能方法能够预测UCS。总体而言,GPR的相关系数(R 2)为0.9955,路径均方误差(RMSE)为0.52169,效果最佳。最后,UCS预测智能方法的顺序分别为GPR,DT,SVR,LSTM,DNN和KNN。

更新日期:2020-12-31
down
wechat
bug