Study on the Construction of Tunnel Face Safety Factor Prediction Formula Based on MARS
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摘要: 为解决山岭隧道工作面稳定性评估难题，提出一种快速高效的评估模型.收集文献中包括GSI、σc和mi等关键参数在内的818组数据，得到σc与mi以及GSI与σc的相关系数；结合拉丁超立方抽样和解析解构建Ⅴ级围岩隧道工作面稳定性数据库；通过MARS(Multivariate Adaptive Regression Splines)算法建立隧道工作面安全系数的预测公式，将其评估结果与施工现场相验证.对比分析表明：相比于岩体自身参数(σc、mi和GSI)，隧道的几何参数更能影响工作面的稳定性;该模型对各因素之间的复杂隐式关系具有良好的可解释性，可实现快速、准确、可靠的计算.因此，该评估模型更便于施工现场的应用，可为岩石隧道工作面稳定性的快速评估提供参考.Abstract: To address the challenge of assessing the stability of rock tunnel face,a fast and efficient assessment model was proposed.A comprehensive dataset comprising 818 set of data encompassing crucial parameters like GSI,σc and mi were meticulously collected from the literature.The correlation coefficients between σc and mi as well as GSI and σc,were subsequently determined.The Latin hypercube sampling and analytical solutions were combined to construct a robust stability database specifically tailored for Grade V rock tunnel faces.Utilizing the Multivariate Adaptive Regression Splines (MARS) algorithm,a predictive formula for the safety factor of rock tunnel face was developed,and rigorously validated using on-site construction data.Comparative analysis indicates that the geometric parameters of the tunnel exert a pronounced impact on the stability than rock properties.The model exhibits good interpretability of the intricate relationships of the tunnel face safety factor to various factors,enabling rapid,accurate,and reliable computation.Consequently,the assessment model stands as a convenient on-site tool,offering expeditious evaluations of the rock tunnel face stability.
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