Experimenting the influence of corncob ash on the mechanical strength of slag-based geopolymer concrete

IF 3.6 4区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Reviews on Advanced Materials Science Pub Date : 2024-03-04 DOI:10.1515/rams-2023-0187
Jing Wang, Qian Qu, Suleman Ayub Khan, Badr Saad Alotaibi, Fadi Althoey, Yaser Gamil, Taoufik Najeh
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Abstract

The construction sector has been under growing public attention recently as one of the leading causes of climate change and its detrimental effects on local communities. In this regard, geopolymer concrete (GPC) has been proposed as a replacement for conventional concrete. Predicting the concrete’s strength before pouring is, therefore, quite useful. The mechanical strength of slag and corncob ash (SCA–GPC), a GPC made from slag and corncob ash, was predicted utilizing multi-expression programming (MEP). Modeling parameters’ relative importance was determined using sensitivity analysis. When estimating the compressive, flexural, and split tensile strengths of SCA–GPC with MEP, 0.95, 0.93, and 0.92 R 2-values were noted between the target and predicted results. The developed models were validated using statistical tests for error and efficiency. The sensitivity analysis revealed that within the mix proportions, the slag quantity (65%), curing age (25%), and fine aggregate (3.30%) quantity significantly influenced the mechanical strength of SCA–GPC. The MEP models result in distinct empirical equations for the strength characteristics of SCA–GPC, unlike Python-based models, which might aid industry and researchers worldwide in determining optimal mix design proportions, thus eliminating unneeded test repetitions in the laboratory.
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试验玉米芯灰对矿渣基土工聚合物混凝土机械强度的影响
最近,建筑行业作为气候变化及其对当地社区有害影响的主要原因之一,受到了公众越来越多的关注。为此,有人提出用土工聚合物混凝土(GPC)替代传统混凝土。因此,在浇筑前预测混凝土的强度非常有用。利用多重表达式编程(MEP)预测了矿渣和玉米芯灰(SCA-GPC)(一种由矿渣和玉米芯灰制成的 GPC)的机械强度。利用灵敏度分析确定了建模参数的相对重要性。使用 MEP 估算 SCA-GPC 的抗压、抗弯和劈裂拉伸强度时,发现目标结果和预测结果之间的 R 2 值分别为 0.95、0.93 和 0.92。使用误差和效率统计测试对所开发的模型进行了验证。敏感性分析表明,在混合比例中,矿渣量(65%)、养护龄期(25%)和细集料(3.30%)的数量对 SCA-GPC 的机械强度有显著影响。与基于 Python 的模型不同,MEP 模型为 SCA-GPC 的强度特性提供了独特的经验方程,可帮助全球工业界和研究人员确定最佳的混合设计比例,从而避免在实验室进行不必要的重复试验。
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来源期刊
Reviews on Advanced Materials Science
Reviews on Advanced Materials Science 工程技术-材料科学:综合
CiteScore
5.10
自引率
11.10%
发文量
43
审稿时长
3.5 months
期刊介绍: Reviews on Advanced Materials Science is a fully peer-reviewed, open access, electronic journal that publishes significant, original and relevant works in the area of theoretical and experimental studies of advanced materials. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-time preservation, language-correction services, no space constraints and immediate publication. Reviews on Advanced Materials Science is listed inter alia by Clarivate Analytics (formerly Thomson Reuters) - Current Contents/Physical, Chemical, and Earth Sciences (CC/PC&ES), JCR and SCIE. Our standard policy requires each paper to be reviewed by at least two Referees and the peer-review process is single-blind.
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