Fidelis Onyebuchi Okafor

This research utilized a multilayer perceptron model based on an artificial neural network (ANN) to improve concrete blocks by partially replacing stone dust and cement with coconut shell and coconut shell ash. From 35 experimental data points, 68.6 % were used for training and 31.4 % for testing. Chemical analyses showed that coconut shell ash, rich in silicon and other oxides, enhanced pozzolanic reactions as a cement substitute. Results indicated that higher proportions of coconut shell ash and shells reduced density, making the mixture lighter but potentially affecting structural integrity. However, increased replacement levels improved workability, shown by higher slump values. The curing period significantly impacted strength, with longer times leading to increased strength due to continued hydration. An optimal compressive strength of 14.17 N/mm ² was achieved with a 5 % replacement after 70 days. The ANN model demonstrated a sum square error of 0.198 and a high correlation coefficient of 0.983 when predicting strength. Optimal conditions for achieving a compressive strength of 33.4 N/mm ² were identified, underscoring the cost-effectiveness of using coconut byproducts instead of traditional materials.

An analytical chemo-hydromechanical model of residual soils was presented in this study. The modeling procedure utilized a unique phenomenological approach, which highlighted some of the macro-mechanical influences responsible for the behavior of residual soils. The macromechanical analysis yielded an extended poroelastic theory. The developed model was applied in solving a typical civil engineering problem of soil consolidation. For a chemical loading treatment, the problem was using requisite boundary conditions. Thereafter, the mathematical software Mathematica was used for solving the ensuing diffusion equations using physico-chemical properties typical of a residual soil sample. Even though the chemical loading was instantaneous, it took some time before its effect could be felt everywhere according to the poroelastic theory. From obtained results the evolution of the generated pore pressure showed increasing segments of the soil returning to their initial state after the passage of the pore pressure front. The vertical displacement showed a slight increase or elevation of the soil surface. The flux of water flow in the soil was initially positive before turning negative, which can be explained by initial successful penetration of the infiltrating liquid until it met increasingly tortuous paths as result of lower permeability. The volume (per unit area) of water leaving the soil after chemical loading demonstrated that a portion of the water flux associated with the infiltrating liquid ends up leaving the soil through the sides. The developed model was later compared with a similar porochemoelastic model found in literature with reasonable agreement.

At present, the large quantity of wastes generated by the ceramic industry is not reused in any significant quantity. Research has shown the feasibility of incorporating these wastes into concrete production. This will benefit both the ceramic and concrete industries. However, not much research data is available on the use of ceramic wastes as fine aggregate material compared to their use as coarse aggregate material. Moreover, there are presently no models for predicting the properties of ceramic waste aggregate concretes. In this study, a modified regression theory based on Taylor’s series was adopted to formulate mathematical model for predicting compressive strength of concrete into which Recycled Ceramic Tile (RCT) is incorporated as fine aggregate. Preliminary tests on RCT indicate that it is a suitable fine aggregate material for concrete production. It has also been established that addition of RCT improves compressive strength of concrete and reduces concrete’s workability. The formulated model is a function of the mix proportions of its constituents and its predicted responses are in good agreement with experimentally observed data. The model has been tested using student’s t-test and analysis of variance and has been confirmed to be adequate and hence is validated.