Behaviour and Microstructural Characteristics of Lime-GGBS-Treated Kaolin Clay Contaminated with Gypsum.

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. A reduction in water absorption by 82% was also observed with L-GGBS treatment after 28-days curing. The UCS results also showed better performance with L-GGBS treatment exceeding 856% at 28 days. The effect of increased cementitious product with increasing gypsum content was negated by simultaneous and rapid growth of ettringite minerals which reduced the strength and increased swelling of OPC treated samples up to 18.92%, exceeding allowable limits of 2.5% as specified in Highway Agency Advice Note HA 74/07. The L-GGBS treated gypseous soil samples meet the strength requirement for stabilised sub-base (CS) and stabilised road-bases (CB1 and CB2) as described in TRL ORN31. Hence, the use of L-GGBS combination was found to be effective in ameliorating sulphate-induced expansion and therefore encouraged in the stabilisation of subgrade and road-base materials with high sulphate contents.

Wet-Dry Cycles and Microstructural Characteristics of Expansive Subgrade Treated with Sustainable Cementitious Waste Materials.

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. The results show a gradual reduction in the California bearing ratio (CBR), mass, and the resilient modulus of samples for all subgrade types as the number of cycles increases. The treated subgrades containing 23.5% GGBS recorded the highest CBR value of 230% under dry conditions while the lowest CBR value of 15% (wetting cycle) was recorded for the subgrade treated with 11.75% GGBS and 11.75% BDW at the end of the wetting-drying cycles, both of which find useful application in road pavement construction as calcium silicate hydrate (CSH) gel was formed in all stabilised subgrade materials. However, the increase in alumina and silica content upon the inclusion of BDW initiated the formation of more cementitious products due to the increased availability of Si and Al species as indicated by EDX analysis. This study concluded that subgrade materials treated with a combination of GGBS and BDW are durable, sustainable and suitable for use in road construction.

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans-A Machine Learning Approach.

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. Both ML regression and multinomial classification of the UCS of the stabilised mix are investigated. Rigorous sensitivity-driven diagnostic testing is also performed to validate and provide an understanding of the intricacies of the decisions made by the algorithm. Results indicate that the well-tuned and optimised GB algorithm has a very high capacity to distinguish between positive and negative UCS categories ('firm', 'very stiff' and 'hard'). An overall accuracy of 0.920, weighted recall rates and precision scores of 0.920 and 0.938, respectively, were produced by the GB model. Multiclass prediction in this regard shows that only 12.5% of misclassified instances was achieved. When applied to a regression problem, a coefficient of determination of approximately 0.900 and a mean error of about 0.335 were obtained, thus lending further credence to the high performance of the GB algorithm used. Finally, among the eight input features utilised as independent variables, the additives seemed to exhibit the strongest influence on the ML predictive modelling.

Optimisation of Embodied Carbon and Compressive Strength in Low Carbon Concrete.

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. From the result of the analysis, a function was developed that was able to predict the associated embodied carbon of a concrete mix for a given water-to-cement ratio. The use of an alkaline solution is observed to increase the compressive strength of low carbon concrete when used in combination with ground granular base slag and silica fume. It is further shown that ground granular base slag contributes significantly to an increase in the compressive strength of Low carbon concrete when compared with pulverised fly ash. The optimised mix design program resulted in a 26% reduction in embodied carbon and an R2 value of 0.9 between the measured compressive strength and the optimised compressive strength.

Road Pavement Thickness and Construction Depth Optimization Using Treated and Untreated Artificially-Synthesized Expansive Road Subgrade Materials with Varying Plasticity Index.

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. In this study, road pavement thickness and construction depth optimization were conducted using the CBR values achieved in this study. Additives proportions of 8% lime and 20% cement were used in expansive subgrade to improve their engineering properties, making them suitable for use in road construction. The study investigated the characteristics, mineral structure, Atterberg limit, compaction, CBR, swell and microstructural properties of expansive subgrade. The results show a reduction in road pavement thickness and a construction depth with an increase in CBR value. All CBR values for treated samples were above 2%, making them usable in road construction. A reduction in swell potential up to 0.04% was observed for treated expansive subgrade. The study concluded that pavement thickness and construction depth can be reduced by enhancing subgrade materials and using cement and lime as binders.