Geology conditions are crucial in decision-making during the planning and design phase of a tunnel project. Estimation of the geology conditions of road tunnels is subject to significant uncertainties. In this work, the effectiveness of a novel regression method in estimating geological or geotechnical parameters of road tunnel projects was explored. This method, called Gaussian process regression (GPR), formulates the learning of the regressor within a Bayesian framework. The GPR model was trained with data of old tunnel projects. To verify its feasibility, the GPR technique was applied to a road tunnel to predict the state of three geological/geomechanical parameters of Rock Mass Rating (RMR), Rock Structure Rating (RSR) and Q-value. Finally, in order to validate the GPR approach, the forecasted results were compared to the field-observed results. From this comparison, it was concluded that, the GPR is presented very good predictions. The R-squared values between the predicted results of the GPR vs. field-observed results for the RMR, RSR and Q-value were obtained equal to 0.8581, 0.8148 and 0.8788, respectively.
Key Words
engineering geology; Gaussian process regression; geomechanical parameters; tunneling; tunnel geology
Address
Arsalan Mahmoodzadeh and Hunar Farid Hama Ali — Department of Civil Engineering, University of Halabja, Halabja, Kurdistan Region, Iraq
Mokhtar Mohammadi — Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
Sazan Nariman Abdulhamid and Hawkar Hashim Ibrahim — Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil, 44002 Erbil, Kurdistan Region, Iraq
Shima Rashidi — Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
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