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<p>This study examines the intricate task of predicting construction duration for drill-and-blast tunnels utilizing machine learning (ML) techniques. First, a comprehensive dataset (500 data points) encompassing 20 diverse parameters was compiled by constructing eight tunnels. After meticulous analysis, 17 of the 20 parameters were identified as crucial for training the algorithms. The overbreak and tunnel cross-section parameters were found to exert a significant influence on the tunnel construction duration. To enhance the predictive accuracy of the ML models, an intensive hyperparameter tuning process was conducted. The findings underscored the effectiveness of the Gaussian process regression model in capturing complex and nonlinear relationships, achieving an average R-squared of 0.89. Additionally, an ML-based graphical user interface (GUI) was developed to facilitate real‒time estimation of tunnel construction duration. This GUI not only enables initial predictions but also allows for dynamic updates throughout the construction phase, enhancing its practical utility.</p>

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Machine learning approaches for predicting the construction time of drill-and-blast tunnels

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月29日

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  • Arsalan Mahmoodzadeh,
  • Hamid Reza Nejati,
  • Nejib Ghazouani,
  • Abdulaziz Alghamdi

摘要

This study examines the intricate task of predicting construction duration for drill-and-blast tunnels utilizing machine learning (ML) techniques. First, a comprehensive dataset (500 data points) encompassing 20 diverse parameters was compiled by constructing eight tunnels. After meticulous analysis, 17 of the 20 parameters were identified as crucial for training the algorithms. The overbreak and tunnel cross-section parameters were found to exert a significant influence on the tunnel construction duration. To enhance the predictive accuracy of the ML models, an intensive hyperparameter tuning process was conducted. The findings underscored the effectiveness of the Gaussian process regression model in capturing complex and nonlinear relationships, achieving an average R-squared of 0.89. Additionally, an ML-based graphical user interface (GUI) was developed to facilitate real‒time estimation of tunnel construction duration. This GUI not only enables initial predictions but also allows for dynamic updates throughout the construction phase, enhancing its practical utility.