Modelling the Compressive Strength and Durability of Concrete Containing Laterite of Different Index Properties

Abstract:

Rapid growth in infrastructure projects in Nigeria has led to indiscriminate river sand mining, causing river bank erosion, bed degradation, biodiversity loss, and poor water quality. Researchers have explored laterite as a substitute for river sand in concrete production. This study investigated use of lateritic soil from Akure (Lat. A), Ondo (Lat. B), and Ile-Oluji (Lat. C) for replacing fine aggregate with replacement levels of 0%, 10%, 20%, and 30% used in concrete cubes (150 × 150 × 150 mm). The physical, chemical, and mineralogical properties of the laterite, and the strength and durability of the resulting concrete, were investigated through Atterberg limits, X-Ray fluorescence, compressive strength, splitting tensile strength and sorptivity tests. Results revealed that concrete with 10% Lat. C achieved the highest compressive strength of 11.45 N/mm², while 20% Lat. B and 10% Lat. A attained strengths of 8.5 N/mm² and 10.77 N/mm², respectively. The optimal sorptivity values were 3.18 ×10⁻⁴ mm/min⁰^.⁵ for 10% Lat. A, 4.73 ×10⁻⁴ mm/min⁰^.⁵ for 20% Lat. B, and 5.66 ×10⁻⁴ mm/min⁰^.⁵ for 10% Lat. C. This suggests that laterite with predominantly silty fines is comparatively better in achieving satisfactory strength and durability than laterite with predominantly clayey fines. The laterite index properties showed a good relationship with the compressive strength models, but did not fit well with the sorptivity models. Hence, while the laterite index properties contribute to the compressive strength of lateritic concrete, they may not essentially affect its sorptivity.

• Periodical: Key Engineering Materials (Volume 1042)
• Pages: 121-137
• DOI: https://doi.org/10.4028/p-7wzhOA
• Online since: February 2026
• Authors: Isah Jimoh Karikati*, Chinwuba Arum, Oluwafemi O. Omotayo
• Keywords: Compressive Strength, Durability, Index Properties, Laterite-Fine Aggregate Concrete (LAFACON), Laterized Concrete, Predictive Models

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