LATERITIC SOIL FOR SUSTAINABLE CONCRETE PRODUCTION: OPTIMIZATION OF MIX PROPORTIONING USING CCD
Abstract
Continuous mining of river sand in large volume for use as fine aggregate in concrete has led to erosion, flooding and other environmental problems. This has necessitated investigation into use of other materials to replace river sand. This study investigated the use of lateritic soil to partially replace sand in concrete production. Twenty-nine (29) combinations of variables including water to cement ratio (W/C), coarse aggregate to total aggregate ratio (CA/TA), total aggregate to cement ratio (TA/C), and lateritic soil to fine aggregate ratio (LS/FA) were generated using Central Composite Design (CCD). Three cube samples (150mm) were cast for each of the 29 mix points and the control mix. Workability of the fresh concrete was tested using slump test while compressive strength test was done on the hardened concrete samples after 28 days of curing. Response Surface Methodology (RSM) was employed in developing a regression model that predicts the compressive strength of concrete containing lateritic soil. Results from this investigation reveals that concrete slump decreases with increasing lateritic soil content. Compressive strength as high as 30.40 N/mm2 can be obtained with a mix combination of W/C of 0.5, CA/TA of 0.6, TA/C of 4.5 and LS/FA of 0.15. The model developed has R2 of 86.58%, adjusted R2 of 73.15% and overall p-value of 0.001. It was concluded that: concrete workability and compressive strength decrease with increasing lateritic soil content when other constituent proportions are kept constant, other constituent proportions influence the compressive strength of concrete irrespective of lateritic soil content, the optimal replacement level of fine aggregate with lateritic soil is 15%, and that the developed model is adequate, valid and has high predictive capabilities.
KEYWORDS: Concrete; Compressive strength, Lateritic soil, Constituent mix proportion, Central composite design, Modelling, Response surface methodology
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