Time and cost of construction are key factors in decision-making during a tunnel project's planning and design phase. Estimations of time and cost of tunnel construction projects are subject to significant uncertainties caused by uncertain geotechnical and geological conditions. The Gaussian Process Regression (GPR) technique for predicting ground condition and construction time and cost of mountain tunnel projects is used in this work. The GPR model is trained with data from past mountain tunnel projects. The model is applied to a case study in which the predicted time and cost of tunnel construction using the GPR model are compared with the actual construction time and cost for model validation and reducing the uncertainty for the future projects. In addition, the results obtained from the GPR have been compared with to other models of artificial neural network (ANN) and support vector regression (SVR) that the GPR model provides more accurate results.
Key Words
construction cost; construction time; Gaussian process regression; ground conditions; tunneling
Address
Arsalan Mahmoodzadeh: Rock Mechanics Division, School of Engineering, Tarbiat Modares University, Tehran, Iran;
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
Hunar Farid Hama Ali: Department of Civil Engineering, University of Halabja, Halabja, Kurdistan Region, Iraq
Hamid Reza Nejati: Rock Mechanics Division, School of Engineering, Tarbiat Modares University, Tehran, Iran
Shima Rashidi: Department of Computer Science, College of Science and Technology, University of Human Development,
Sulaymaniyah, Kurdistan Region, Iraq
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