Estimation of tunnel boring machine penetration rate: Application of long-short-term memory and meta-heuristic optimization algorithms
Mengran Xu,Arsalan Mahmoodzadeh,Abdelkader Mabrouk,Hawkar Hashim Ibrahim,Yasser Alashker,Adil Hussein Mohammed
Abstract
Accurately estimating the performance of tunnel boring machines (TBMs) is crucial for mitigating the substantial financial risks and complexities associated with tunnel construction. Machine learning (ML) techniques have emerged as powerful tools for predicting non-linear time series data. In this research, six advanced meta-heuristic optimization algorithms based on long short-term memory (LSTM) networks were developed to predict TBM penetration rate (TBM-PR). The study utilized 1125 datasets, partitioned into 20% for testing, 70% for training, and 10% for validation, incorporating six key input parameters influencing TBM-PR. The performances of these LSTM-based models were rigorously compared using a suite of statistical evaluation metrics. The results underscored the profound impact of optimization algorithms on prediction accuracy. Among the models tested, the LSTM optimized by the particle swarm optimization (PSO) algorithm emerged as the most robust predictor of TBM-PR. Sensitivity analysis further revealed that the orientation of discontinuities, specifically the alpha angle (a), exerted the greatest influence on the model's predictions. This research is significant in that it addresses critical concerns of TBM manufacturers and operators, offering a reliable predictive tool adaptable to varying geological conditions.
Mengran Xu — College of Civil Engineering and Architecture, Xinjiang University, Urumqi 830046, Xinjiang, China College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310000, Zhejiang, China
Arsalan Mahmoodzadeh — IRO Civil Engineering Department, University of Halabja, Halabja, 46018, Iraq
Abdelkader Mabrouk — Department of Civil Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
Hawkar Hashim Ibrahim — Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil, 44002 Erbil, Kurdistan Region, Iraq
Yasser Alashker — Department of Civil Engineering, College of Engineering, King Khalid University, PO Box 394, Abha 61411 Kingdom of Saudi Arabia; Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi Arabia
Adil Hussein Mohammed — Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University, Kurdistan Region, Iraq
PDF Viewer
Preview is limited to the first 3 pages. Sign in to access the full PDF.
