基于超前钻探及优化集成算法的隧道围岩双层质量评价

为解决隧道超前钻探地质预报在解译过程中存在的主观判断性强、定量数据利用率低、评判标准不统一等问题，通过引入机器学习中的极限梯度提升集成算法模型（extreme gradient boosting，XGBoost），结合钻探数据开展隧道围岩完整程度与围岩级别的双层质量评价研究。一方面，采取数据降噪、等距分割、二级指标计算等数据预处理手段对11 233条原始钻探采样数据进行规律发掘和质量提升；另一方面，结合遗传算法（genetic algorithm，GA）与分类器链（classifier chains，CC）构建GA-CC-XGBoost模型，实现复杂机器学习模型的超参数组合自动寻优以及多标签分类的内在相关性考虑。最终所构建训练集的完整程度与围岩级别2项标签的分类准确率分别为95.91%、97.95%，综合分类准确率为93.88%。经过实际隧道工程应用表明，该模型预测结果满足现场超前钻探地质预报的解译需求。

Double Layer Quality Evaluation of Surrounding Rock of a Tunnel Based on Advance Drilling and Optimized Integration Algorithm

The interpretation of advance drilling forecasts for tunnels involves many problems, such as highly subjective judgment, low quantitative data utilization, and inconsistent evaluation standards. Therefore, the extreme gradient boosting ensemble model (XGBoost) in machine learning is introduced, and the double layer (integrity and classification) quality evaluation of the surrounding rock of the tunnel is conducted based on the drilling data. First, the 11 233 original drilling sampling data are preprocessed to improve data quality using data noise reduction, equidistant segmentation, and secondary index calculation methods. Second, a genetic algorithm (GA) classifier chain (CC) XGBoost model is constructed. This model realizes the automatic optimization of the hyperparameter combination of the complex machine learning model and considers the inherent relevance of multilabel classification. The classification accuracy of the GA CC XGBoost model in the double layer label is 95.91% and 97.95%, and the comprehensive classification accuracy is 93.88%. The actual tunnel engineering application indicates that the prediction results of the model meet the interpretation requirements of geological forecasts for on site advance drilling.