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On the strength and pulse velocity of rubberized concrete containing silica fume and zeolite: Prediction using multivariable regression models

机译:关于含硅粉和沸石的橡胶混凝土的强度和脉冲速度:使用多元回归模型进行预测

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In this study, rubberized concrete containing waste rubber chips, silica fume and zeolite was investigated. For this purpose, coarse aggregates were partially replaced by waste tire rubber, i.e. 10 wt% and 15 wt%. Silica fume and zeolite were incorporated into the binder to replace 10% of the cement. Different mixes were cast and cured in two curing conditions. Compressive strengths of mixes were measured at different ages as 3, 7, 28, and 42 days. Ultrasonic tests were carried out to measure the pulse velocity in different mixes of the rubberized concrete. The relationship between the compressive strength and the pulse velocity was investigated by a power relationship and the coefficients were determined for different mixes. It was observed that strength drops were typically greater in sample without admixtures especially at 28 days of age. It was also found that the pulse velocity could well capture the curing condition and admixtures' effect on rubberized concrete. Quite high correlations were also observed between the compressive strength and pulse velocity in various mixtures. In order to predict the compressive strengths of the rubberized concrete, different types of multivariable regression models were developed and compared to find the best predictive model for the rubberized concrete. It was found that the full quadratic model was the best model with highest correlation among the assessed regression models. (C) 2019 Elsevier Ltd. All rights reserved.
机译:在这项研究中,对含废橡胶碎屑,硅粉和沸石的涂胶混凝土进行了研究。为此目的,粗骨料部分地由废轮胎橡胶代替,即10wt%和15wt%。将硅粉和沸石掺入粘合剂中以代替10%的水泥。在两种固化条件下浇铸和固化不同的混合物。在3、7、28和42天的不同年龄下测量混合物的抗压强度。进行了超声波测试,以测量橡胶混凝土不同混合物中的脉冲速度。通过幂关系研究了抗压强度与脉冲速度之间的关系,并确定了不同混合物的系数。观察到,没有掺混物的样品强度下降通常更大,尤其是在28天龄时。还发现脉冲速度可以很好地记录固化条件和掺合料对橡胶混凝土的影响。在各种混合物中的抗压强度和脉冲速度之间也观察到很高的相关性。为了预测橡胶混凝土的抗压强度,开发了不同类型的多元回归模型并进行了比较,以找到橡胶混凝土的最佳预测模型。结果发现,在评估的回归模型中,完整的二次模型是具有最高相关性的最佳模型。 (C)2019 Elsevier Ltd.保留所有权利。

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