Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R2) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.
Key Words
convergence estimation; deep neural network; LSTM; optimization; tunnel wall convergence
Address
Arsalan Mahmoodzadeh — Rock Mechanics Division, School of Engineering, Tarbiat Modares University, Tehran, Iran
Mohammadreza Taghizadeh — Department of Civil engineering, Faculty of engineering, University of Kashan, Kashan, Iran
Adil Hussein Mohammed — Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq
Hawkar Hashim Ibrahim — Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil, 44002 Erbil, Kurdistan Region, Iraq
Hanan Samadi — School of Geology, College of Science, University of Tehran, Tehran, Iran
Mokhtar Mohammadi — Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, 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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