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Rock mass quality prediction on tunnel faces with inco.plete multi-source dataset via tree-augmented naive bayesian network

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International Journal of Mining Science and Technology 2024年第3期34卷 323-337页

作者： Hongwei Huang Chen Wu Mingliang Zhou Jiayao Chen Tianze Han Le Zhang Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Tongji UniversityShanghai 200092China Department of Geotechnical Engineering Tongji UniversityShanghai 200092China School of Civil Engineering Beijing Jiaotong UniversityBeijing 100044China qingdao guoxin second jiaozhou bay subsea tunnel co. Ltd.Qingdao 266071China

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 co.bination of qualitative and quantitative parameters.However,due to the harsh on-site co.struction co.ditions,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 multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.co.sidering the limitations in predictive performance of inco.plete evaluation parameters exist in this dataset,a novel tree-augmented naive bayesian network(BN)is proposed to address the challenge of the inco.plete dataset and achieved a prediction accuracy of 88%.In co.parison with other co.monly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the inco.plete dataset.By utilizing the established BN,a further sensitivity analysis is co.ducted 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.

关键词： Rock mass quality tunnel faces Inco.plete multi-source dataset Improved Swin Transformer bayesian networks

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