SUITABILITY ASSESSMENT OF LATERITE SOIL AS ALTERNATIVE TO SHARP SAND IN CEMENT BLOCK PRODUCTION: PERFORMANCE EVALUATION AND PREDICTIVE ANALYSIS

Hyginus Ozioko; Emmanuel Ebube Eze; Augustine Chidera  Ekunie

doi:10.4314/njt.2025.4767

Authors

H.O. Ozioko Department of Civil Engineering, Michael Okpara University of Agriculture Umudike, Abia State
E.E. Eze Department of Civil Engineering, Michael Okpara University of Agriculture Umudike, Abia State
A. C. Ekunie Department of Civil Engineering, Michael Okpara University of Agriculture Umudike, Abia State

DOI:

https://doi.org/10.4314/njt.2025.4767

Keywords:

Laterite soil, cement block production, compressive strength, sustainable building materials and partial sand replacement

Abstract

To address the growing need for alternative building materials, this study investigated laterite soil as a partial replacement for sharp sand in cement block production, aiming to provide a sustainable and cost-effective building material. Laterite soil and sharp sand were characterized, and cement blocks with 0-30% laterite replacement were produced. Particle size distribution test was carried out to determine the grading characteristics of the aggregates. In addition, water absorption, bulk density, and compressive strength tests were evaluated at 7, 14, 21, and 28 days of curing. Statistical and predictive analysis, including ANOVA and machine learning models, was performed. Results showed laterite had a higher Coefficient of Uniformity (15.40) than sand (9.47), indicating better grading. Water absorption increased with laterite replacement, reaching 2.95% at 30% compared to 1.88% for the control mix. Bulk density decreased linearly from 2448 kg/m³ to 2180 kg/m³ at 30% replacement. Compressive strength declined with increased laterite, with significant reductions beyond 25%. ANOVA revealed significant differences in compressive strength (p < 0.000157), and Scheffé post-hoc analysis identified significant strength reductions at 25% and 30% laterite replacement. Predictive modeling using Artificial Neural Networks (ANN) achieved the highest accuracy (R² = 0.982, RMSE = 0.118), outperforming polynomial (R² = 0.962) and linear regression (R² = 0.854). The study concluded that laterite replacement up to 20% maintains acceptable block quality, balancing sustainability with structural performance.

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