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Application of response surface methodology: Predicting and optimizing the properties of concrete containing steel fibre extracted from waste tires with limestone powder as filler

机译:响应面法的应用:预测和优化以石灰石粉为填充料的废轮胎中提取的含钢纤维的混凝土的性能

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This study showcases the predictive and optimization capabilities of response surface methodology with respect to the fresh and hardened properties of waste tyre steel fibre reinforced concrete containing limestone powder. Response surface methodology has the advantage of simultaneously varying chosen independent variables to provide a useful model for overall response variation. The study identifies aspect ratio (50–140), water cement ratio (0.2–0.4) and cement content (25%–40%) as independent variables while limestone powder was kept constant at 5% by weight of concrete. Predictive equations for the water intake/absorption, compressive strength, flexural strength, split tensile strength and slump of fibre reinforced concrete were obtained using the independent variables. The analysis of variance (ANOVA) for all properties indicates that the modified quadratic model was able to effectively predict the fresh and hardened properties of fibre reinforced concrete with coefficient of determination ranging between 0.86 and 0.98. In addition, RSM model predictive efficiency was classified as very good for compressive strength, splitting tensile strength, slump and water absorption and acceptable for FS in terms of Nash & Sutcliffe coefficient of model efficiency. An optimum condition of 140 for the aspect ratio, 0.26 for water cement ratio and 40% for cement content corresponding to 0.94%, 42.69 N/mm2, 7.97 N/mm25.23 N/mm27.65 cm for water intake/absorption, compressive strength, flexural strength, split tensile strength and slump respectively was achieved. These predictions were validated and a good correlation was observed between the experimental and predicted values judging by the absolute relative percent error of 0.842, 11.35, 3.6, 18.22 and 2.04 for water intake/absorption, compressive strength, flexural strength, split tensile strength and slump respectively. The proposed mathematical models are capable of predicting the required fresh and hardened properties of fibre-reinforced concrete as to inform early decision making when utilized in construction.
机译:这项研究针对含石灰石粉的废旧轮胎钢纤维增强混凝土的新鲜和硬化性能,展示了响应面方法的预测和优化能力。响应面方法的优点是可以同时改变所选的独立变量,从而为整体响应变化提供有用的模型。该研究确定纵横比(50–140),水灰比(0.2–0.4)和水泥含量(25%–40%)作为自变量,而石灰石粉保持在混凝土重量的5%不变。利用自变量获得了纤维增强混凝土的吸水/吸收,抗压强度,抗弯强度,劈裂抗拉强度和坍落度的预测方程。所有性能的方差分析(ANOVA)表明,改进的二次模型能够有效地预测纤维增强混凝土的新鲜和硬化性能,其测定系数在0.86至0.98之间。此外,就模型效率的纳什&萨特克利夫系数而言,RSM模型的预测效率被归类为非常出色的抗压强度,抗拉强度,坍落度和吸水率,而对于FS则可以接受。长宽比为140的最佳条件,水灰比为0.26,水泥含量的40%的最佳条件对应于0.94%,42.69 N / mm2、7.97 N / mm25.23 N / mm27.65 cm的吸水/吸收,压缩强度,弯曲强度,断裂抗拉强度和坍落度分别达到。通过对进水/吸水率,抗压强度,抗弯强度,劈裂抗拉强度和坍落度的绝对相对误差百分比分别为0.842、11.35、3.6、18.22和2.04的判断,可以验证这些预测并观察到实验值与预测值之间存在良好的相关性。分别。所提出的数学模型能够预测纤维增强混凝土所需的新鲜和硬化性能,从而在建筑中使用时为早期决策提供依据。

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