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

• Published in: AI in Civil Engineering Vol. 3; no. 1; p. 22
• Main Authors: Raji, Mustapha A., Falola, Boluwatife M., Enikuomehin, Jesse T., Oyelade, Akintoye O., Abiodun, Yetunde O., Olaniyi, Yusuf A., Olagunju, Olusola G., Anyaegbuna, Kosisochukwu L., Abdulkareem, Musa O., Fapohunda, Christopher A.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2024; Springer; Springer Nature B.V
• Subjects: Artificial Intelligence; Cement; Civil Engineering; Composite materials; Concrete; Construction industry; Energy consumption; Engineering; Mechanical properties; Nanoparticles; Original Article; Particle size; Rice; Silica; Sustainable development; Waste management; Waste materials; Nigeria; Rice husk ash; Micro silica; Compressive strength; Agricultural waste; Sustainable construction; Nano silica
• ISSN: 2097-0943; 2730-5392; 2730-5392
• DOI: 10.1007/s43503-024-00043-5

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.