基于主成分分析-云模型的黄土隧道施工稳定性评估

黄土隧道极易发生大变形和塌方等灾害，为准确、高效地识别其施工中的稳定性，针对其稳定性评估具有随机性和模糊性共存的特点，选取同一施工条件下黄土含水率、干密度、孔隙比、黏聚力和内摩擦角等5个物理力学参数作为稳定性评估指标。采用主成分分析法获得各指标权重，结果显示含水率、黏聚力和内摩擦角3个指标对黄土隧道稳定性影响较大；通过各指标分级标准计算获得云模型的数字特征；结合各指标权重与云模型的数字特征，通过正向云发生器，构建基于主成分分析和正态云理论的黄土隧道施工稳定性评估模型。将该评估模型应用于蒙华铁路延安段黄土隧道，预测准确率高达90%，优于Critic-云模型，验证了该模型的可行性和有效性。

Stability Evaluation of Loess Tunnel during Construction Based on Principal Component Analysis Cloud Model

Loess tunnels are prone to large deformation and collapse. Accordingly, to assess the stability of loess tunnels accurately and efficiently in construction, five physical and mechanical parameters of loess, such as water content, dry density, void ratio, cohesion, and internal friction angle, are selected as stability predictors under the same construction condition when considering the coexistence of randomness and ambiguity in stability assessment. Furthermore, based on principal component analysis, the weights of predictors are obtained, showing that water content, cohesion, and internal friction angle affect the stability of loess significantly. Then, the characteristics of the cloud model are obtained through the grading standard of predictors. Finally, based on the weights and characteristics of the cloud model, the stability evaluation model of a loess tunnel during construction based on principal component analysis and normal cloud theory is constructed using the forward cloud generator. The proposed evaluation model was applied to the loess tunnel of the Yan′an section of the Menghua railway. The prediction accuracy rate is as high as 90%, which is better than that of the Critic cloud model, thereby validating the feasibility and effectiveness of the proposed model.