Study on Loess Consolidation Collapse Test and Prediction Model in Jingyang District, Shaanxi Province
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摘要: 黄土湿陷问题的研究具有很大的工程意义及价值。野外取得陕西泾阳地区黄土原状样，在天然含水率的基础上分别配置了含水率为12%、15%、18%与20%的5组原状黄土试样，采用单线法对研究地区黄土进行了湿陷性试验，获得了在不同法向应力下黄土的压缩特性及湿陷性系数，研究了不同含水率以及不同压力对黄土湿陷性的影响。结果表明，在较低含水率的情况下，湿陷系数随着压力的增大而增大，在较高含水率的情况下，湿陷系数随着压力的增加先增大后减小。而湿陷系数与含水率的关系相对复杂，在相同压力下，湿陷系数均在某一含水率下达到峰值。根据湿陷系数与含水率及压力的曲线特征，建立了不同压力下黄土湿陷系数与含水率的回归关系式。最后，基于最小二乘支持向量机，以黄土的密度、含水率、压力等指标作为预测变量，建立了黄土湿陷性预测模型。结果表明，采用支持向量机所建立的模型来预测黄土湿陷性是可以满足工程要求的。Abstract: The study of loess collapsibility problem has great engineering significance and value. Based on natural moisture content, the five sets of undisturbed loess samples with water content of 12%, 15%, 18% and 20% respectively have been collected from the Jingyang area of Shaanxi Province. The single-line method has been used to test the collapsibility of loess in this study area, the compressive properties and collapsibility coefficient of loess under different normal stresses have been obtained, the influence of different water content and different pressure on collapsibility of loess have been studied in this paper. The results show that the coefficient of collapsibility increases with the increase of pressure at lower moisture content. On the contrary, at higher moisture content, the coefficient of collapsibility first increases firstly, and then decreases with increasing pressure. But, the relationship between the coefficient of collapsibility and the moisture content is relatively complex. At the same pressure, the collapsibility coefficient peaks at a certain moisture content. According to the curve characteristics of collapsibility coefficient, moisture content and pressure, a regression equation has been established for collapsibility coefficient and moisture content of loess under different pressures. Finally, based on the least squares support vector machine, the loess density, moisture content and pressure have been used as predictors, thus, a collapsibility prediction model for loess has been established. The results show that this model established by the support vector machine can meet the engineering requirements for predicting the collapsibility of loess.
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