Techno Press
You logged in as Techno Press

Geomechanics and Engineering
  Volume 37, Number 4, May25 2024 , pages 307-321

In-depth exploration of machine learning algorithms for predicting sidewall displacement in underground caverns
Hanan Samadi, Abed Alanazi, Sabih Hashim Muhodir, Shtwai Alsubai,Abdullah Alqahtani and Mehrez Marzougui

    This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.
Key Words
    importance ranking; machine learning algorithms; sidewall displacement; underground caverns
Hanan Samadi: IRO, Civil Engineering Department, University of Halabja, Halabja, 46018, Iraq
Abed Alanazi, Shtwai Alsubai and Abdullah Alqahtani: Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University,
P.O. Box 151, Al-Kharj 11942, Saudi Arabia
Sabih Hashim Muhodir: Department of Architectural Engineering, Cihan University-Erbil, Kurdistan Region, Iraq
Mehrez Marzougui: College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2024 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: