Geomechanics and Engineering

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Assessment of wall convergence for tunnels using machine learning techniques

  • Mahmoodzadeh, Arsalan (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Nejati, Hamid Reza (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Mohammadi, Mokhtar (Department of Information Technology, College of Engineering and Computer Science, Lebanese French University) ;
  • Ibrahim, Hawkar Hashim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Mohammed, Adil Hussein (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil) ;
  • Rashidi, Shima (Department of Computer Science, College of Science and Technology, University of Human Development)
  • Received : 2021.11.20
  • Accepted : 2022.10.12
  • Published : 2022.11.10
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Abstract

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

Keywords

References

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