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夏元友, 张宏伟, 吝曼卿, 阎要锋. 基于数据预处理技术并考虑围岩应力梯度影响的隧洞岩爆预测[J]. 岩土工程学报, 2023, 45(10): 1987-1994. DOI: 10.11779/CJGE20220701
引用本文: 夏元友, 张宏伟, 吝曼卿, 阎要锋. 基于数据预处理技术并考虑围岩应力梯度影响的隧洞岩爆预测[J]. 岩土工程学报, 2023, 45(10): 1987-1994. DOI: 10.11779/CJGE20220701
XIA Yuanyou, ZHANG Hongwei, LIN Manqing, YAN Yaofeng. Prediction of tunnel rockbursts based on data preprocessing technology considering influences of stress gradient of surrounding rock[J]. Chinese Journal of Geotechnical Engineering, 2023, 45(10): 1987-1994. DOI: 10.11779/CJGE20220701
Citation: XIA Yuanyou, ZHANG Hongwei, LIN Manqing, YAN Yaofeng. Prediction of tunnel rockbursts based on data preprocessing technology considering influences of stress gradient of surrounding rock[J]. Chinese Journal of Geotechnical Engineering, 2023, 45(10): 1987-1994. DOI: 10.11779/CJGE20220701

基于数据预处理技术并考虑围岩应力梯度影响的隧洞岩爆预测

Prediction of tunnel rockbursts based on data preprocessing technology considering influences of stress gradient of surrounding rock

  • 摘要: 针对目前岩爆预测研究通常忽视岩爆数据集存在离群样本、缺失值与样本不平衡性问题以及围岩应力梯度的影响,提出一套完备的岩爆数据预处理流程,引入可间接表征围岩应力梯度的洞径指标,建立了隧洞岩爆多因素综合预测模型。在数据采集阶段,考虑隧道与采场及隧洞群受力条件差异,从岩爆数据库中分离出隧洞岩爆样本共306例。在岩爆预测指标选取阶段,选取隧洞洞径D0、围岩最大切向应力 \sigma _\theta \max 、岩石单轴抗压强度 \sigma _\textc 、岩石抗拉强度 \sigma _\textt 、弹性能变形指数Wet共5个指标。在数据预处理阶段:针对缺失值,引入随机森林多重插补法(MI-RF)对岩爆样本进行补全;针对离群样本,引入最近邻(KNN)、孤立森林(Isolation Forest)、局部异常因子(LOF)3种无监督算法综合评估岩爆数据集并剔除离群样本;针对样本不平衡,引入自适应综合过采样(ADASYN)算法扩容少数类样本。在模型验证阶段:采用支持向量机(SVM)、随机森林(RF)、梯度提升树(GBDT)、自适应提升树(AdaBoost)、极限梯度提升树(XGBoost)5类算法构建岩爆预测模型。模型预测结果表明:基于数据预处理并考虑洞径指标的5类模型皆为同类算法模型中的最优;在不进行数据预处理的条件下,考虑洞径指标模型要优于不考虑洞径指标的同类算法模型。

     

    Abstract: As the current rockburst prediction investigation frequently ignores outliers, missing values, sample imbalance in the rockburst dataset and the influences of surrounding rock stress gradient, a complete preprocessing process of rockburst data is proposed, and the hole diameter index that indirectly represents the stress gradient of surrounding rock of tunnel is employed to establish the multi-factor comprehensive prediction model for tunnel rockbursts. At the stage of the data collection, considering the variation in stress conditions between the tunnel, stope and tunnel group, 306 samples of rockbursts in tunnels are isolated from the rockburst database. At the stage of determining prediction index, five indices are selected including the hole diameter (D0), the maximum tangential stress ( \sigma _\theta \max ), the uniaxial compressive strength ( \sigma _\textc ), the uniaxial tensile strength of the rock (σt) and the elastic energy deformation index (Wet). At the stage of the data preprocessing, the multiple imputation method of random forest (MI-RF) is introduced to fill in the missing values. Three unsupervised algorithms including the K-nearest neighbor (KNN), the isolation forest (IForest) and the local outlier factor (LOF) are introduced to comprehensively evaluate the rockburst dataset and removed outliers. The adaptive comprehensive oversampling (ADASYN) algorithm is introduced to expand the number of minority samples. At the stage of the model validation, five types of models including the support vector machine (SVM), the random forest (RF), the gradient boosted decision trees (GBDT), the adaptive boosting algorithm (AdaBoost) and the extreme gradient boosting algorithm (XGBoost) are adopted for comparison. The results demonstrate that the aforementioned models based on the data preprocessing and the hole diameter index are all the best among similar algorithm models. Without the data preprocessing, the model considering the hole diameter index is better than those without considering the hole diameter.

     

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