A novel Tree-augmented Bayesian network for predicting rock weathering degree using incomplete dataset

IF 7 1区 工程技术 Q1 ENGINEERING, GEOLOGICAL International Journal of Rock Mechanics and Mining Sciences Pub Date : 2024-10-21 DOI:10.1016/j.ijrmms.2024.105933
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Abstract

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.
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利用不完整数据集预测岩石风化程度的新型树增强贝叶斯网络
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实施至关重要。围岩的表观特征是评价围岩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,并建立一个包含 10 个参数的多源异构数据集,从而为数据驱动的风化程度预测提供便利。具体来说,通过改进的隧道面特征分割(TFFSeg)模型对岩石外观参数进行量化和分割,该模型专门针对地下水、裂缝和夹层的独特特征而设计。同时,与其他广泛使用的计算机视觉方法相比,TFFSeg 模型在处理这些岩石特征方面的性能明显提高。随后,通过加入岩石物理和机械参数以及隧道设计参数,进一步丰富了这一多源数据集。然而,该数据集仍存在数据不完整的问题。为了在不完整数据集的基础上实现对风化程度的精确预测,设计了一种新型树增强贝叶斯网络(TAN-BN),它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 超越了目前使用的其他元模型和集合模型,如 ANN、GBRT 和 Naive BN。最后,通过敏感性分析确定了 10 个参数的重要性排序,为现场评估隧道工作面岩石风化程度提供了有价值的见解。
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来源期刊
CiteScore
14.00
自引率
5.60%
发文量
196
审稿时长
18 weeks
期刊介绍: The International Journal of Rock Mechanics and Mining Sciences focuses on original research, new developments, site measurements, and case studies within the fields of rock mechanics and rock engineering. Serving as an international platform, it showcases high-quality papers addressing rock mechanics and the application of its principles and techniques in mining and civil engineering projects situated on or within rock masses. These projects encompass a wide range, including slopes, open-pit mines, quarries, shafts, tunnels, caverns, underground mines, metro systems, dams, hydro-electric stations, geothermal energy, petroleum engineering, and radioactive waste disposal. The journal welcomes submissions on various topics, with particular interest in theoretical advancements, analytical and numerical methods, rock testing, site investigation, and case studies.
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