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Engineers are faced with the problems of providing very suitable materials for highway and other foundations construction. Lateritic soils are highly weathered indigenous soil available in large quantities but generally need improvements to adequately satisfy the required construction purposes. This research investigates the influence of steel slag and marble dust addition on some geotechnical properties of lateritic soil. The results revealed that the soil is well graded based on particle size distribution and is classified as A-2-5 under AASHTO system. With progressive increase in each stabiliser, both the liquid limit, plastic limit and plasticity index exhibit fluctuating patterns. In addition, both marble dust and steel slag increased the maximum dry density with increasing proportions in the soil sample but exhibit irregular patterns for optimum moisture content. This research provides an insight to the quality of lateritic soils obtainable in the study area, and the level of improvements required before they are suitable for road construction.

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A network of the feedforward-type artificial neural networks (ANNs) was used to predict the compressive strength of concrete made from crude oil contaminated soil samples at 3, 7, 14, 28, 56, 84, and 168 days at different degrees of contamination of 2.5%, 5%, 10%, 15%, 20% and 25%. A total of 49 samples were used in the training, testing, and prediction phase of the modeling in the ratio 32 : 11 : 7. The TANH activation function was used and the maximum number of iterations was limited to 20,000 the model used a momentum of 0.6 and a learning rate of 0.031056. Twenty (20) different architectures were considered and the most suitable one was the 2-2-1. Statistical analysis of the output of the network was carried out and the correlation coefficient of the training and testing data is 0.9955712 and 0.980097. The result of the network has shown that the use of neural networks is effective in the prediction of the compressive strength of concrete made from crude oil impacted sand.