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<p>Geological detection ahead of shield cutterhead is a challenge in the construction of large-diameter shield tunnels. This study proposed a framework for geological conditions identification based on stacking algorithm in large-diameter shield tunnel excavation of Chunfeng tunnel project. The relationships among shield parameters were analyzed using the Spearman rank correlation coefficient. Principal component analysis was employed to extract essential information from the parameters during shield tunneling. Then, the K-means + + clustering algorithm was used to construct a correlation dataset between the principal components of shield parameters and the categories of geological conditions. Subsequently, the Stacking algorithm was applied to recognize the geological categories ahead of the shield cutterhead, and the results were compared with those of optimized random forest, support vector machine, and gradient boosting decision tree algorithms. The proposed method achieved superior performance, accurately classifying geological features ahead of the shield cutterhead with an accuracy of 0.984. Moreover, the stacking algorithm achieves higher accuracy in geological category recognition compared to the optimized single-machine learning algorithms. This research results can provide valuable geological conditions for adjusting shield parameters, gesture control, and risk reduction during the large-diameter shield tunneling.</p>

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Identification of geological conditions based on stacking algorithm during slurry shield tunneling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月15日

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  • Yi Zeng,
  • Weiwei Zhao,
  • Zhengyi Yu,
  • Kunan Wei,
  • Xiaolong Zhang

摘要

Geological detection ahead of shield cutterhead is a challenge in the construction of large-diameter shield tunnels. This study proposed a framework for geological conditions identification based on stacking algorithm in large-diameter shield tunnel excavation of Chunfeng tunnel project. The relationships among shield parameters were analyzed using the Spearman rank correlation coefficient. Principal component analysis was employed to extract essential information from the parameters during shield tunneling. Then, the K-means + + clustering algorithm was used to construct a correlation dataset between the principal components of shield parameters and the categories of geological conditions. Subsequently, the Stacking algorithm was applied to recognize the geological categories ahead of the shield cutterhead, and the results were compared with those of optimized random forest, support vector machine, and gradient boosting decision tree algorithms. The proposed method achieved superior performance, accurately classifying geological features ahead of the shield cutterhead with an accuracy of 0.984. Moreover, the stacking algorithm achieves higher accuracy in geological category recognition compared to the optimized single-machine learning algorithms. This research results can provide valuable geological conditions for adjusting shield parameters, gesture control, and risk reduction during the large-diameter shield tunneling.