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基于XGBoost算法的公路隧道失稳风险评估模型及系统开发

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为降低公路隧道建设过程中围岩掉块、塌方等灾害的发生,在充分发挥工程数据价值的基础上,建立了基于XGBoost算法的公路隧道失稳风险评估模型.选取岩体的风化程度、围岩级别、岩体完整程度、结构面结合程度、地下水出水情况和开挖方法共6个指标作为公路隧道失稳风险评估模型的指标,通过对各定性指标进行量化赋值来实现风险评估.引入混淆矩阵和准确率对失稳风险评估模型的测试集和预测集的训练效果进行检验.通过对比大华山隧道模型应用结果与实际工程结果,对所建立的风险评估模型进行了验证,验证结果二者呈现高度的一致性,成功预警了围岩失稳灾害的发生.基于Visual Basic自主开发了"公路隧道失稳风险评估系统",实现了公路隧道建设过程中准确快速的失稳风险评估.
Risk Assessment Model and System Development of Highway Tunnel Peripheral Rock Instability Based on XGBoost Algorithm
In order to reduce the occurrence of disasters such as rock fall and landslide during the construction of road tunnels,this paper establishes a road tunnel instability risk assessment model based on XGBoost algorithm on the basis of giving full play to the value of engineering data.Six indicators,namely,weathering degree of rock body,surrounding rock level,integrity of rock body,structural surface bonding degree,groundwater outflow and excavation method,are selected as the assessment indicators of the instability risk assessment model of the road tunnel,and the risk assessment is achieved by quantitatively assigning the values to each qualitative indicator.Confusion matrix and accuracy rate are introduced to test the training effect of the test set and prediction set of the instability risk assessment model.The risk assessment model is validated by comparing the results of the Dahuashan Tunnel with the results of the actual project,and the validation results show a high degree of consistency,which successfully warns the occurrence of surrounding rock instability disaster.Based on Visual Basic,the"Road Tunnel Instability Risk Assessment System(RCIAS)"was developed independently,which realises accurate and fast instability risk assessment in the construction process of road tunnels.

road tunnelinstability risk assessmentquantification of qualitative indicatorsmachine learningXGBoost algorithmearly warning system

王迎超、郭崟、姜雯、张政、邹鹤民

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中国矿业大学深地工程智能建造与健康运维全国重点实验室,江苏徐州 221116

中国矿业大学力学与土木工程学院,江苏徐州 221116

青岛地铁集团有限公司,山东青岛 266100

中国铁路上海局集团有限公司合肥铁路枢纽工程建设指挥部,安徽合肥 230011

中铁十六局集团第三工程有限公司,浙江湖州 313000

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公路隧道 失稳风险评估 定性指标量化赋值 机器学习 XGBoost算法 预警系统

国家自然科学基金项目国家重点研发计划项目中铁十六局集团有限公司科技计划项目中国铁路上海局集团有限公司科研项目

422723132022YFC3003304K2023-6B2022178

2024

10.16058/j.issn.1005-0930.2024.04.004

应用基础与工程科学学报
中国自然资源学会

应用基础与工程科学学报

CSTPCD北大核心
影响因子：0.895
ISSN：1005-0930
年,卷(期)：2024.32(4)
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