Experimental and optimization study of unconfined compressive strength of ameliorated tropical black clay
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Abstract
In the field of soil re-engineering, unconfined compressive strength (UCS) of soil material is considered as an essential soil parameter. This is because it also provides the strength benchmark of soil materials for usage in road foundations. However, Scheffe’s approach in conjunction with the utilization of waste materials have been comprehensively utilized in predicting and ameliorating various soil parameters in the field of civil infrastructural constructions. For this purpose, this study outlines the practicality of applying Scheffe’s technique in optimizing UCS values of tropical black clay soil (BCS) treated with cement kiln dust (CKD) and metakaolin (MTK) blend. The tropical black clay which falls within the A-7-6 (14) group via AASHTO classification scheme and CH via the Unified Soil Classification Scheme. Qualitative tests such as scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) were executed on both the natural and BCS ameliorated with optimal combination based on Scheffe’s concept. These qualitative tests confirmed the build ups of major compounds in the soil matrix thereby promoting the use of Scheffe’s technique in soil treatment studies. During the optimization exercise, the attained outcomes revealed that the peak values of 1206 and 1735 kN/m2 (7 and 28 days curing) with a mix ratio of 1.0:0.30:0.35:0.50 for soil, water, cement kiln dust and metakaolin respectively. The formulated mathematical models considered UCS values of compacted soil materials as dependent variable (response) whereas CKD, MTK, BCS and water were considered as independent variables. Furthermore, the analysis of variance (Anova) and student t-test which are techniques for testing the goodness of a fit were applied to statistically scrutinize the mathematical models and ascertain the adequacy and validity. Hence, the outcomes of this research work portrays the feasibility of using predictive models for UCS prediction and this will aid in providing benchmarks when utilized as road construction material for sustainable infrastructure delivery.
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