Geomechanics and Engineering

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Estimation of tunnel boring machine penetration rate: Application of long-short-term memory and meta-heuristic optimization algorithms

  • Mengran Xu (College of Civil Engineering and Architecture, Xinjiang University) ;
  • Arsalan Mahmoodzadeh (IRO, Civil Engineering Department, University of Halabja) ;
  • Abdelkader Mabrouk (Department of Civil Engineering, College of Engineering, Northern Border University) ;
  • Hawkar Hashim Ibrahim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Yasser Alashker (Department of Civil Engineering, College of Engineering, King Khalid University) ;
  • Adil Hussein Mohammed (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University)
  • Received : 2023.02.14
  • Accepted : 2024.08.27
  • Published : 2024.10.10
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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 (α), 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.

Keywords

Acknowledgement

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP.2/163/45. The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number "NBU-FFR2024-2507-03".

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