[目的] 充分挖掘现有黄土湿陷试验资料的价值,建立基于黄土湿陷性机理的湿陷系数预测模型. [方法] 以山西省中部黄土为例,基于前期开展的原位大型浸水试验及配套的室内试验资料,首先按物理意义的异同将黄土土性指标分为7大类,而后通过黄土物理力学参数与湿陷系数散点图,对各土性指标与湿陷系数的相关性从土力学与工程地质学角度进行了深入的分析和讨论,再利用偏相关分析定量地给出湿陷系数与各土性指标的相关性及相关程度排序. [结果] 根据排序剔除了相关性非常小的液、塑限及塑性指数这一大类指标,其余6大类指标中相关性由高到低依次为:取土深度、孔隙比、干密度、压缩模量、饱和度、颗粒组成C5~15μm,并将上述结果引入RBF神经网络,建立了基于黄土湿陷性机理的、参数选取较为全面、建模方法较为科学的湿陷系数预测模型. [结论] 通过非母体数据的验证表明模型精度可以满足工程应用的需要,研究过程与结果可加深对黄土湿陷机理的认识.
[Objective] We aimed to use the existed laboratory test data in order to establish the coefficient of collapsibility prediction model based on the mechanism of loess collapsibility and data mining method. [Methods] The loess soil located at middle of the Loess Plateau was taken as the case study. Based on the results from in-situ immersion test and laboratory test conducted in this region, the test data were divided in to 7 classes according to their physical significance. Scatter diagrams with coefficient of collapsibility were then plotted. From these scatter diagrams, the relationships between loess collapsibility and each single physical-mechanical parameter were investigated. The correlations between all parameters and the coefficient of collapsibility were analyzed by partial correlation analysis. [Results] Plastic limit, liquid limit and plastic index were eliminated from the model due to the low correlation. The correlation coefficient from the highest to the lowest was: saturation, dry density, void ratio, soil depth, compression modulus and grain size from 5 to 15μm. We then used the data in RBF neural network in Matlab software to establish the prediction model of the collapsibility coefficient, which was based on the mechanism of collapsibility loess, parameter selection more comprehensive, modeling method more scientific. [Conclusion] The established model can predict the coefficient of collapsibility with low error value, which meets the requirement of the engineering application. The result of this research is of great importance to understand the collapsibility mechanism of loess soil.