用多元回归模型预测稻壳灰中纳米二氧化硅和微二氧化硅的抗压强度

Mustapha A. Raji, Boluwatife M. Falola, Jesse T. Enikuomehin, Akintoye O. Oyelade, Yetunde O. Abiodun, Yusuf A. Olaniyi, Olusola G. Olagunju, Kosisochukwu L. Anyaegbuna, Musa O. Abdulkareem, Christopher A. Fapohunda
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摘要

在混凝土生产中使用农业副产品,如稻壳灰(RHA),作为传统建筑材料的可持续替代品,已经引起了极大的关注。本研究旨在评价和比较纳米稻壳灰(NRHA)和微稻壳灰(MRHA)对混凝土抗压强度的影响。混凝土样品采用不同替代水平的NRHA(0%至3%)和MRHA(0%至14%)制备,并在7、21、28和56天进行坍落度和抗压强度测试。结果表明,NRHA在1%的替代水平下达到最大抗压强度,而MRHA在0.5%的替代水平下达到峰值。然而,将1%的NRHA (22 N/mm2)与0.5%的MRHA (21.5 N/mm2)的抗压强度进行比较发现,强度的边际差异使MRHA成为更具成本效益的选择,因为其制备费用较低。因此,MRHA提出了一个更经济的解决方案,以实现可比的抗压强度。此外,该研究应用线性、非线性和混合回归分析,基于一组综合变量对NRHA和MRHA混凝土的性能进行建模。分析发现,与线性和非线性模型相比,普通和对数混合模型提供了最好的拟合,提供了更高的精度。
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Prediction of compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models

The use of agricultural by-products, such as Rice Husk Ash (RHA), in concrete production has gained significant attention as a sustainable alternative to traditional construction materials. This study aims to evaluate and compare the effects of Nano-Rice Husk Ash (NRHA) and Micro-Rice Husk Ash (MRHA) on the compressive strength of concrete. Concrete samples were prepared with varying replacement levels of NRHA (0% to 3%) and MRHA (0% to 14%) and underwent thorough examination through both slump and compressive strength tests conducted at 7, 21, 28, and 56 days. The results showed that NRHA achieved maximum compressive strength at a 1% replacement level, while MRHA reached its peak at a 0.5% replacement level. However, a comparison of the compressive strength of NRHA at 1% (22 N/mm2) against MRHA at 0.5% (21.5 N/mm2) revealed that the marginal difference in strength made MRHA a more cost-effective option due to the lower expenses involved in its preparation. Thus, MRHA presents a more economical solution for achieving comparable compressive strength. Furthermore, the study applied linear, non-linear, and mixed regression analyses to model the properties of NRHA and MRHA concrete based on a comprehensive set of variables. The analysis found that the blended ordinary and logarithmic models provided the best fit, offering superior accuracy compared to linear and non-linear models.

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