Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces. In tunneling practice, the rock mass quality is often assessed via a combination of qualitative and quantitative parameters. However, due to the harsh on-site construction conditions, it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction. In this study, a novel improved Swin Transformer is proposed to detect, segment, and quantify rock mass characteristic parameters such as water leakage, fractures, weak interlayers. The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%, 81%, and 86% for water leakage, fractures, and weak interlayers, respectively. A multi-source rock tunnel face characteristic (RTFC) dataset includes 11 parameters for predicting rock mass quality is established. Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset, a novel tree-augmented naive Bayesian network (BN) is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%. In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset. By utilizing the established BN, a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters, results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

中文翻译：

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基于树增强朴素贝叶斯网络的不完整多源数据集隧道掌子面岩体质量预测


岩体质量是预测岩石隧道掌子面稳定性和安全状况的重要指标。在隧道施工实践中，岩体质量通常通过定性和定量参数的组合来评估。但由于现场施工条件恶劣，岩体质量预测所必需的一些评价参数的获取相当困难。在本研究中，提出了一种新型改进的 Swin Transformer 来检测、分割和量化岩体特征参数，例如漏水、裂缝、软弱夹层。现场实验结果表明，改进后的Swin Transformer取得了最优的分割效果，对漏水、裂缝、软弱夹层的分割精度分别达到92%、81%和86%。建立了包含11个参数的多源岩隧道掌子面特征（RTFC）数据集，用于预测岩体质量。考虑到该数据集中存在不完整评估参数的预测性能限制，提出了一种新颖的树增强朴素贝叶斯网络（BN）来解决不完整数据集的挑战，并实现了 88% 的预测精度。与其他常用的机器学习模型相比，所提出的基于 BN 的方法证明在使用不完整数据集预测岩体质量方面具有改进的性能。利用建立的BN，进一步进行敏感性分析，定量评价各参数的重要性，结果表明，岩石强度和裂隙参数对岩体质量影响最为显着。