The precise forecasting of the weathering degree of surrounding rock holds paramount importance for the scientific design and secure execution of tunnel engineering. The apparent features of the surrounding rock serve as critical indicators for evaluating its weathering degree. This paper endeavors to quantify the rock apparent features based on an improved Computer vision model and establish a multi-source heterogeneous dataset encompassing 10 parameters, thereby facilitating data-driven predictions of the weathering degree. Specifically, the rock appearance parameters are quantified and segmented by an improved Tunnel face feature segmentation (TFFSeg) model, which is tailored to the unique characteristics of groundwater, fractures, and interlayers. Concurrently, the TFFSeg model exhibits significantly enhanced performance for these rock features compared to other widely employed Computer vision methods. Subsequently, this multi-source dataset is further enriched by incorporating rock physical and mechanical parameters as well as tunnel design parameters. Nevertheless, the issue of data incompleteness persists within this dataset. To achieve precise prediction of the weathering degree based on this incomplete dataset, a novel Tree-augmented Bayesian network (TAN-BN) is designed, which is capable of learning from incomplete datasets. The predictive outcomes demonstrate that the proposed TAN-BN surpasses other currently utilized meta models and ensemble models, such as ANN, GBRT, and Naive BN. Finally, sensitivity analysis is conducted to determine the importance rankings of the 10 parameters, offering valuable insights for on-site evaluation of the rock weathering degree at the tunnel face.

中文翻译：

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使用不完全数据集预测岩石风化程度的新型树增强贝叶斯网络


对围岩风化程度的精确预测对于隧道工程的科学设计和安全执行至关重要。围岩的明显特征是评估其风化程度的关键指标。本文试图基于改进的计算机视觉模型对岩石表观特征进行量化，并建立了包含 10 个参数的多源非均质数据集，从而促进对风化程度的数据驱动预测。具体来说，岩石外观参数通过改进的隧道掌子面特征分割 （TFFSeg） 模型进行量化和分割，该模型是根据地下水、裂缝和夹层的独特特征量身定制的。同时，与其他广泛采用的计算机视觉方法相比，TFFSeg 模型在这些岩石特征上表现出显著增强的性能。随后，通过整合岩石物理和力学参数以及隧道设计参数，进一步丰富了这个多源数据集。然而，数据不完整的问题在这个数据集中仍然存在。为了实现基于该不完全数据集的风化程度的精确预测，设计了一种新的树增强贝叶斯网络 （TAN-BN），该网络能够从不完整的数据集中学习。预测结果表明，所提出的 TAN-BN 超越了其他目前使用的元模型和集成模型，例如 ANN、GBRT 和 Naive BN。最后，进行敏感性分析，确定 10 个参数的重要性排序，为现场评价隧道掌子面岩石风化程度提供有价值的见解。