岩石抗压强度随钻预测及其机制解释研究

Study on prediction of rock strength while drilling and its mechanism interpretation

摘要:

准确评估岩石抗压强度，尤其是隧道施工过程中的实时强度预测，是确保工程安全和优化设计的关键基础。基于随钻参数的机器学习预测方法在该领域展现出巨大潜力。然而，传统机器学习模型的黑箱特性限制了其在工程实践中的应用。为此，本研究提出一种基于可解释人工智能的岩石抗压强度随钻预测方法，通过SHAP分析技术解析极端梯度提升（XGBoost）模型的决策机制，实现预测过程透明化和可解释性。研究表明，构建的XGBoost模型在岩石抗压强度预测方面取得了良好效果，测试集预测精度达75.71%。更重要的是，SHAP值分析定量揭示了各输入参数对预测结果的贡献机制，发现钻速均值以25.67%的贡献度占据主导地位，而参数变异性指标（标准差类）的总贡献度高达49.70%，突破了传统认知中仅关注参数均值的局限性。本研究揭示了随钻参数随钻进深度变化的一般规律，实现了岩石抗压强度随钻预测，对推动地下工程参数预测的可解释性和工程应用具有重要意义。

Abstract:

Accurate evaluation of rock strength， especially real-time strength prediction during tunnel construction， is a key basis for ensuring engineering safety and optimal design. Machine learning prediction methods based on drilling parameters have shown great potential in the field of rock strength assessment. However， the black-box nature of traditional machine learning models limits their application in engineering practice. To this end， this study proposes a rock strength prediction method based on interpretable artificial intelligence. The decision-making mechanism of the XGBoost model is analyzed via SHAP analysis technology to achieve transparency and interpretability of the prediction process. The results show that the XGBoost model has achieved good performance in rock strength prediction， with the test set prediction accuracy reaching 75.71%. More importantly， SHAP value analysis quantitatively reveals the contribution mechanism of each input parameter to the prediction results. It is found that the average drilling speed dominates with a contribution rate of 25.67%， while the total contribution rate of parameter variability indicators （standard deviation category） is as high as 49.70%. This finding breaks the limitation of only focusing on parameter means in traditional cognition. This study reveals the general law of drilling parameters changing with drilling depth， realizes rock strength prediction while drilling， and is of great significance for promoting the interpretability and engineering application of underground engineering parameter prediction.