Prediction of the Penetration Rate and Number of Consumed Disc Cutters of Tunnel Boring Machines (TBMs) Using Artificial Neural Network (ANN) and Support Vector Machine (SVM)—Case Study: Beheshtabad Water Conveyance Tunnel in Iran

Article type: Research Article

Authors: Afradi, Alireza | Ebrahimabadi, Arash^{; *} | Hallajian, Tahereh

Affiliations: Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

Correspondence: [*] Corresponding Author. Arash.xer@gmail.com

Abstract: Tunnel boring machines (TBMs) are designed to excavate underground spaces and widely used in tunneling, civil and mining projects. TBM performance prediction substantially deals with the evaluation of machine’s penetration rate and the number of consumed disc cutters. There are various methods and equations to predict the TBMs performance in the literature. In this paper, we predicted the penetration rate and number of consumed disc cutters in Beheshtabad water conveyance tunneling project, one of the major water conveyance tunneling projects in Iran, using Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. Results showed that both approaches are very effective but SVM yields more precise and realistic findings than ANN.

Keywords: TBM performance prediction, artificial neural network, support vector machine, Beheshtabad water conveyance tunnel

DOI: 10.3233/AJW190006

Journal: Asian Journal of Water, Environment and Pollution, vol. 16, no. 1, pp. 49-57, 2019