Publications by authors named "Samuel J Abbey"

This work presents an experimental study on the physico-mechanical and microstructural characteristics of stabilised soils and the effect of wetting and drying cycles on their durability as road subgrade materials. The durability of expansive road subgrade with a high plasticity index treated with different ratios of ground granulated blast furnace slag (GGBS) and brick dust waste (BDW) was investigated. Treated and cured samples of the expansive subgrade were subjected to wetting-drying cycles, California bearing ratio (CBR) tests, and microstructural analysis.

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World orange production is estimated at 60 million tons per annum, while the annual production of orange peel waste is 32 million tons. According to available data, the adsorption capacity of orange peel ranges from 3 mg/g to 5 mg/g, while their water uptake is lower than 1 mg/g. The low water uptake of orange peel and the abundance of biomass in nature has made orange peel an excellent biosorption material.

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In this experimental study, the physico-mechanical and microstructural properties of sulphate-bearing clays have been investigated. Sulphate bearing soils constituted by mixing kaolin and gypsum at 0%, 15%, 25%, and 35% gypsum contents were treated with 12% ordinary Portland cement (OPC) and 4%Lime (L) and 8% ground granulated blast furnace slag (GGBS) and subjected to compaction, swell, unconfined compressive strength (UCS), California bearing ratio (CBR), and scanning electron microscopy (SEM) and energy dispersive spectrometry (EDX) analyses. The results of the study showed that the use of L-GGBS improved the soaked CBRs of the treated samples by over 43% when compared to OPC-treated samples after 7-days curing.

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To improve the prediction of compressive strength and embodied carbon of low carbon concrete using a program algorithm developed in MATLAB, 84 datasets of concrete mix raw materials were used. The influence of water, silica fume and ground granular base slag was found to have a significant impact on the extent of low carbon concrete behaviour in terms of compressive strength and embodied carbon. While the concrete compressive strength for normal concrete increases with reducing water content, it is observed that the low carbon concrete using lightweight aggregate material increases in compressive strength with an increase in embodied carbon.

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The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans.

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Road pavement thickness and their depth of construction take a chunk of the overall cost of road construction. This has called for a need for reduced road pavement thickness by improving the engineering properties of subgrade such as the California bearing ratio (CBR). The CBR of road subgrade has been a major determining factor for road pavement thickness, and expansive subgrades generally have a low CBR, resulting in major road defects.

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