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Tunnel Boring Machine Performance Prediction Using Supervised Learning Method and Swarm Intelligence Algorithm

Author

Listed:
  • Zhi Yu

    (Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350116, China)

  • Chuanqi Li

    (Laboratory 3SR, CNRS UMR 5521, Grenoble Alpes University, 38000 Grenoble, France)

  • Jian Zhou

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

This study employs a supervised learning method to predict the tunnel boring machine (TBM) penetration rate (PR) with high accuracy. To this end, the extreme gradient boosting (XGBoost) model is optimized based on two swarm intelligence algorithms, i.e., the sparrow search algorithm (SSA) and the whale optimization algorithm (WOA). Three other machine learning models, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models, are also developed as the drawback. A database created in Shenzhen (China), comprising 503 entries and featuring 10 input variables and 1 output variable, was utilized to train and test the prediction models. The model development results indicate that the use of SSA and WOA has the potential to improve the XGBoost model performance in predicting the TBM performance. The performance evaluation results show that the proposed WOA-XGBoost model has achieved the most satisfactory performance by resulting in the most reliable prediction accuracy of the four performance indices. This research serves as a compelling illustration of how combined approaches, such as supervised learning methods and swarm intelligence algorithms, can enhance TBM prediction performance and can provide a reference when solving other related engineering problems.

Suggested Citation

  • Zhi Yu & Chuanqi Li & Jian Zhou, 2023. "Tunnel Boring Machine Performance Prediction Using Supervised Learning Method and Swarm Intelligence Algorithm," Mathematics, MDPI, vol. 11(20), pages 1-16, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4237-:d:1257083
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    References listed on IDEAS

    as
    1. Xin Wei & Niaz Muhammad Shahani & Xigui Zheng, 2023. "Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach," Mathematics, MDPI, vol. 11(7), pages 1-17, March.
    2. Niaz Muhammad Shahani & Xigui Zheng & Xiaowei Guo & Xin Wei, 2022. "Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    3. Yan Zhang & Mingdong Wei & Guoshao Su & Yao Li & Jianbin Zeng & Xueqin Deng, 2020. "A Novel Intelligent Method for Predicting the Penetration Rate of the Tunnel Boring Machine in Rocks," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-15, September.
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