Scholarlink

<p>The rock mass rating (RMR) system is a widely used tool for assessing rock quality and recommending support, relying on six parameters: rock quality designation, uniaxial compressive strength, groundwater conditions, discontinuity spacing, condition, and orientation. The conventional RMR classification system necessitates the presence of all parameters. This study introduces a machine learning (ML) approach utilizing the random forest (RF) algorithm to predict rock mass classification with a reduced set of easily accessible parameters. A synthetic database of RMR parameters was generated to train the RF model, with Bayesian optimization applied to refine key settings such as learning rate, ensemble cycles, and maximum splits. The ML model was validated for accuracy and reliability through several performance metrics. Predictions of the proposed ML model using data from 41 real-world field cases demonstrate a high accuracy of 100%. With the advantages of the artificial intelligence (AI), the proposed ML model maintained over 90% accuracy even when key parameters such as discontinuity length, separation, or infilling were unavailable. This AI-powered approach offers a significant improvement over traditional methods, providing superior accuracy, adaptability, and reliability for rock quality assessment and support recommendations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advancing rock mass classification using machine learning approach

  • 期刊论文

  • 发表日期：

    2025年11月20日

查看全文
  • Chih-Yu Liu,
  • Cheng-Yu Ku,
  • Ting-Yuan Wu

摘要

The rock mass rating (RMR) system is a widely used tool for assessing rock quality and recommending support, relying on six parameters: rock quality designation, uniaxial compressive strength, groundwater conditions, discontinuity spacing, condition, and orientation. The conventional RMR classification system necessitates the presence of all parameters. This study introduces a machine learning (ML) approach utilizing the random forest (RF) algorithm to predict rock mass classification with a reduced set of easily accessible parameters. A synthetic database of RMR parameters was generated to train the RF model, with Bayesian optimization applied to refine key settings such as learning rate, ensemble cycles, and maximum splits. The ML model was validated for accuracy and reliability through several performance metrics. Predictions of the proposed ML model using data from 41 real-world field cases demonstrate a high accuracy of 100%. With the advantages of the artificial intelligence (AI), the proposed ML model maintained over 90% accuracy even when key parameters such as discontinuity length, separation, or infilling were unavailable. This AI-powered approach offers a significant improvement over traditional methods, providing superior accuracy, adaptability, and reliability for rock quality assessment and support recommendations.