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Production and optimization of eco-efficient self compacting concrete SCC with limestone and PET

机译:用石灰石和PET生产和优化生态高效自密实混凝土SCC

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This study aims to produce and optimize Eco-efficient self-compacting concrete (SCC) mixes using multi-waste substitutions. The main input parameters of mixes were total binder, fine aggregate and water contents whereas slump flow and compressive strength were the two main operational responses of produced concrete. Limestone powder (LP) and waste Polyethylene Terephthalate (PET) were used in concrete as parts of cement and fine aggregate respectively with high range water reducing admixture (SP) as part of water. Response Surface Methodology (RSM) and multi-objectives optimization using Minitab 17 statistical software were employed for this purpose.Twenty SCC mixes were designed and checked experimentally using Central Composite Design (CCD) concept in RSM. Mathematical models were established and evaluated using analysis of variance test (ANOVA) according to the experimental results. This is in order to define the effectiveness degree of design parameters on the properties required and to adjust the derived mathematical models. Multi-objectives optimization process was adopted to determine the optimum values of the input parameters. The optimization revealed that the optimum values of the input factors, LP, PET and SP were 20.1%, 2.4% and 1.16% by weight respectively. These theoretical values were checked experimentally and the achieved responses were quiet similar or higher than the best proposed mix.It was deduced that the developed models can be used to ensure a speedy mix design process by achieving maximum tested properties of eco-efficient SCC. (C) 2018 Elsevier Ltd. All rights reserved.
机译:这项研究旨在使用多种废物替代品生产和优化生态高效的自密实混凝土(SCC)混合物。混合料的主要输入参数是总粘结剂,细骨料和水含量,而坍落度和抗压强度是所生产混凝土的两个主要操作响应。石灰石粉(LP)和废聚对苯二甲酸乙二酯(PET)分别用作混凝土的水泥和细骨料的一部分,高范围减水剂(SP)作为水的一部分。为此,使用Minitab 17统计软件对响应面方法(RSM)和多目标进行了优化。设计了20种SCC混合物,并使用RSM中的中央复合设计(CCD)概念进行了实验检查。建立了数学模型,并根据实验结果使用方差分析(ANOVA)进行了评估。这是为了定义设计参数对所需属性的有效性程度并调整导出的数学模型。采用多目标优化过程来确定输入参数的最佳值。优化表明,输入因子LP,PET和SP的最佳值分别为20.1%,2.4%和1.16%(重量)。通过实验检查了这些理论值,得出的响应与建议的最佳混合料相比安静或相似。推断出所开发的模型可通过实现生态高效SCC的最大测试性能来确保快速的混合料设计过程。 (C)2018 Elsevier Ltd.保留所有权利。

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