Prediction of hard rock TBM penetration rate using least square support vector machine

2013 ◽  
Vol 46 (13) ◽  
pp. 347-352 ◽  
Author(s):  
Yang Ge ◽  
Jingcheng Wang ◽  
Kang Li
Keyword(s):  
Support Vector Machine ◽  
Hard Rock ◽  
Penetration Rate ◽  
Least Square ◽  
Support Vector ◽  
Hard Rock Tbm ◽  
Tbm Penetration Rate

scholarly journals PREDICTION OF TBM PENETRATION RATE USING SUPPORT VECTOR MACHINE

Geosaberes ◽  
2020 ◽  
Vol 11 ◽  
pp. 467
Author(s):  
Alireza Afradi ◽  
Arash Ebrahimabadi ◽  
Tahereh Hallajian
Keyword(s):  
Support Vector Machine ◽  
Penetration Rate ◽  
Alpha Angle ◽  
High Accuracy ◽  
Operating Costs ◽  
Accurate Estimation ◽  
Support Vector ◽  
Brazilian Tensile Strength ◽  
Rate Of Penetration ◽  
Tbm Penetration Rate

One of the most important issues in mechanized excavating is to predict the TBM penetration rate. Understanding the factors influencing the rate of penetration is important, which allows for a more accurate estimation of the stopping and excavating times and operating costs. In this study, Input and output parameters including Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), Peak Slope Index (PSI), Distance between Planes of Weakness (DPW), Alpha angle and Rate of Penetration (ROP) (m/hr) in the Queens Water Tunnel using support vector machine .Results showed that prediction of penetration rate for Support Vector Machine (SVM) method is R2 = 0.9678 and RMSE = 0.064778, According to the results, Support Vector Machine (SVM) is effective and has high accuracy.

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