Prediction of compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models

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

AI in Civil Engineering ›› 2024, Vol. 3 ›› Issue (1) : 22

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AI in Civil Engineering ›› 2024, Vol. 3 ›› Issue (1) : 22 DOI: 10.1007/s43503-024-00043-5
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Prediction of compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models

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Abstract

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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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. Prediction of compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models. AI in Civil Engineering, 2024, 3(1): 22 DOI:10.1007/s43503-024-00043-5

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