Prediction of compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models
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/mm 2 ) against MRHA at 0.5% (21.5 N/mm 2 ) 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.
Authors
- Misjudeen A. Raji
- Y. O. Abiodun (ORCID: https://orcid.org/0000-0003-0233-631X)
- Christopher Fapohunda (ORCID: https://orcid.org/0000-0002-2448-6952)
- Olasunkanmi Olusogo Olagunju (ORCID: https://orcid.org/0000-0003-0279-3051)
- Muyideen Abdulkareem (ORCID: https://orcid.org/0000-0002-3208-4144)
- Akintoye O. Oyelade (ORCID: https://orcid.org/0000-0001-6869-6436)
- Boluwatife M. Falola
- Jesse T. Enikuomehin
- Yusuf A. Olaniyi
- Kosisochukwu L. Anyaegbuna
Institutions
- University of Lagos (NG)
- Federal University Oye Ekiti (NG)
Publication Details
- Journal
- AI in Civil Engineering
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1007/s43503-024-00043-5
- Citations
- 3
- Primary Topic
- Concrete and Cement Materials Research
- Type
- article
- Field-Weighted Citation Impact
- 0.78