为综合利用隧道勘察及施工阶段的地质信息，提升掌子面围岩级别判识的准确性，提出一种基于钻进参数与超前地质预报地震波反射法（TSP）融合的围岩级别智能判识方法。首先，从施工现场收集钻进参数、TSP报告及掌子面地质素描，读取相应的数据与围岩级别信息。其次，为明确钻进参数与地震波反射法的融合方式，设置5种融合工况，并选择4种机器学习分类算法（K近邻、梯度提升树、随机森林、极限树）验证融合数据的可分性。考虑到数据划分方式对模型的拟合能力有影响，通过设置随机数种子对数据集进行500种划分，以使各模型充分拟合。研究结果表明： 采用钻进参数中的进给速度、推进压力、打击压力及回转压力与TSP中纵波速度、横波速度进行融合的工况下，模型分类效果最好，最大准确率为91.3%（采用极限树模型）。最后，使用贝叶斯优化算法对极限树模型的超参数进行优化，通过对比分析，经过优化后的模型在各个围岩级别上的分类性能均有所提升，且总体准确率为93.27%，提升了1.93%。
An intelligent surrounding
rock grade identification method based on the fusion of drilling parameters and
tunnel seismic prediction (TSP) method is proposed to improve the
identification accuracy of surrounding rock grade based on geological
information in tunnel survey and construction stages. Drilling parameters, TSP
reports, and geological sketches are collected from the construction site to
interpret corresponding data and surrounding rock information. Then, five
fusion scenarios are set and four machine leaning classification algorithms,
i.e., K-nearest neighbor, gradient
boosting tree, random forest, and extreme tree, are employed to verify the
separability of fused data for determining the final fusion mode of the
drilling parameters and TSP data. To determine the impact of various data
partitioning methods on the fitting ability of models, 500 partitioning modes
are applied to the dataset by setting random number seeds to fully fit each
model. Results show that the extreme tree model yields the best surrounding
rock grade classification effect with a maximum accuracy of 91.3% by fusing
drilling parameters such as feed rate, thrust pressure, impact pressure, and
rotation pressure with the longitudinal and transverse wave velocities in TSP,
respectively. Finally, the Bayesian optimization algorithm is used to optimize
the hyperparameters of the extreme tree model. The optimized model exhibits
improved classification performance for various rock grades, with overall and
increase accuracies of 93.27%
and 1.93%, respectively.
