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首页> 外文期刊>American Journal of Traffic and Transportation Engineering >Predicting CBR Values of Black Cotton Soil Stabilized with Cement and Waste Glass Admixture Using Regression Model
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Predicting CBR Values of Black Cotton Soil Stabilized with Cement and Waste Glass Admixture Using Regression Model

机译：回归模型预测水泥和废玻璃掺合料稳定的黑棉土壤的CBR值

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摘要

In highway constructions, sub-grade and sub-base soil stabilization has been used as one of the prime and major process for many years in order to improve the engineering properties of soil. The strength of theses layers is indicated by their California bearing ratio (CBR) value which is quite expensive and time consuming. In order to overcome this situation, this study presents a methodology for predicting soaked California Bearing Ration (CBR) value of Black Cotton Soil Stabilized with Cement and Waste Glass Admixture using Multiple Regression Analysis (MRA). Experimental test results such Atterberg limit (Liquid limit (LL), Plastic limit (PL) and Plasticity index (PI)), Compaction characteristics of two compactive efforts namely standard proctor (SP) and modified proctor (MP) (maximum dry density (MDD) and optimum moisture content (OMC)), CBR, Waste glass (WG) content and Cement content (Cm), obtained from a laboratory at Abubakar Tafawa Balewa University Bauchi, Nigeria, have been employed in developing multiple regression models. California Bearing Ration was taken as the dependent variables while Liquid limit, Plastic limit, maximum dry density, optimum moisture content, waste glass content and Cement content were taken as independent variables. The regression analysis calculated the error mean square (MS_E) for each possible model, and models with large MS_E were not selected for the best regression equations. The best models have a minimum value of MS E occurring for the six-variable model (Cm, WG, LL, PL, OMC_(sp), MDD_(sp)) and (C_m, WG, PL, LL, OMC_(mp), MDD_(mp)) with a corresponding higher value of coefficient of multiple determination R~2 = 0.98 and 0.94. The performance evaluation of the fitted regression models indicates a strong correlation (R~2 = 0.89 - 0.98) between the mentioned variables, and the model equations developed from this work provided a very good prediction of the response, as the equations can be employed for making estimates of soaked CBR of other black cotton soils having similar geotechnical properties.
机译：在高速公路建设中，多年以来，路基和基层土壤稳定化一直是主要的主要过程之一，目的是改善土壤的工程性能。这些层的强度由它们的加利福尼亚承载比（CBR）值表示，这非常昂贵且耗时。为了克服这种情况，本研究提出了一种使用多元回归分析（MRA）预测用水泥和废玻璃掺合料稳定的黑棉土壤的加利福尼亚浸泡率（CBR）值的方法。实验测试结果，如阿特伯格极限（液体极限（LL），塑性极限（PL）和塑性指数（PI）），两种压实作用的压实特性，即标准推土机（SP）和改良推土机（MP）（最大干密度（MDD） ）和最佳水分含量（OMC），CBR，废玻璃（WG）含量和水泥含量（Cm）（这是从尼日利亚包奇的Abubakar Tafawa Balewa大学的实验室获得的）用于建立多元回归模型的。以加利福尼亚轴承比为因变量，以液体极限，塑料极限，最大干密度，最佳水分，废玻璃含量和水泥含量为自变量。回归分析计算出每个可能模型的误差均方（MS_E），并且未选择具有较大MS_E的模型作为最佳回归方程。最佳模型的六变量模型（Cm，WG，LL，PL，OMC_（sp），MDD_（sp））和（C_m，WG，PL，LL，OMC_（mp）的最小值为MS E ，MDD_（mp）），相应的多重确定系数R〜2的较高值= 0.98和0.94。拟合回归模型的性能评估表明，所提到的变量之间具有很强的相关性（R〜2 = 0.89-0.98），并且根据这项工作开发的模型方程可以很好地预测响应，因为可以将这些方程用于估算了具有类似岩土特性的其他黑棉土壤的浸透CBR。

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