摘要
针对隧道工程施工过程中岩体完整性定量评价高度依赖揭露掌子面信息,同时利用随钻数据进行岩体完整性评价时存在的挑战,该研究提出了一种结合数理统计与机器学习的随钻岩体完整性定量评价方法。首先,基于超前钻探作业收集了大量包含较完整、较破碎及破碎三类常见完整性岩体的数字钻探数据;随后通过数据预处理及超参数寻优,构建了高性能的岩体完整性分类随机森林模型;最后,借助SHAP值理论在增强模型预测结果可解释性的同时,实现了对多变量不安定指数分析法中的不安定指数及指数幂进行筛选与量化,构建了“区间岩体破碎指数(Interval Rock Fracture Index,I_(IRFI))”定量评价模型。实际隧道工程中的应用验证表明,该模型对三类完整性岩体的综合评价准确率达到90%左右,相较于常规方法,能够提供更加高效、准确且详尽的岩体完整性信息。该研究为隧道岩体完整性评价提供了一种新的、高效的定量方法,有助于提升施工安全性和工程效率。
In tunnel construction,the quantitative evaluation of rock mass integrity heavily relies on information from the exposed face,and there are challenges when drilling data is used for integrity evaluation.To this end,this study introduced a novel method for quantitative evaluation of rock mass integrity during drilling,integrating numerical statistics with machine learning.A substantial dataset of digital drilling data was collected,covering three common types of rock mass integrity:relatively intact,relatively fractured,and fractured.Subsequently,a high-performance random forest model for the classification of rock mass integrity was developed through data preprocessing and hyperparameter optimization.The interpretability of the model’s predictive results was enhanced using Shapley additive explanations(SHAP)value theory.Additionally,the instability index and its exponents from the multivariable instability index analysis method were selected and quantified,and the quantitative evaluation model for“interval rock fracture index(I_(IRFI))”was created.The application of the model in actual tunnel engineering demonstrates an approximate 90%accuracy rate in evaluating the three types of rock mass integrity.The model provides more efficient,accurate,and detailed information on rock mass integrity compared to conventional methods.The study offers a new and effective quantitative approach for assessing rock mass integrity in tunnels,which contributes to improved construction safety and project efficiency.
作者
张坤牧
彭浩
梁铭
韩玉
宋冠先
ZHANG Kunmu;PENG Hao;LIANG Ming;HAN Yu;SONG Guanxian(Guangxi Road and Bridge Engineering Group Co.,Ltd.,Nanning,Guangxi 530200,China)
出处
《中外公路》
2025年第6期245-253,共9页
Journal of China & Foreign Highway
基金
广西重点研发计划项目(编号:桂科AB22080033)。
关键词
隧道工程
随钻测量
岩体完整性
机器学习
定量评价
tunnel engineering
measurement while drilling
rock mass integrity
machine learning
quantitative evaluation
