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
{"title":"Prediction of compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models","authors":"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","doi":"10.1007/s43503-024-00043-5","DOIUrl":null,"url":null,"abstract":"<div><p>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/mm<sup>2</sup>) against MRHA at 0.5% (21.5 N/mm<sup>2</sup>) 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.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00043-5.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI in civil engineering","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s43503-024-00043-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.