Building materials obtained by recycling coal ash and waste drilling fluid and characterization of engineering properties by means of Artificial Neural Networks
Abstract Recycling ash from coal combustion deposited in dump sites and waste drilling fluid (WDF) from oil extraction industry for the purpose of partial replacement of clay in construction materials was investigated. Compressive strength, density and pore density were determined experimentally following standard procedures for test specimens manufactured from mixtures of the two waste materials and clay in varying proportions. A neural network model was developed and cross-validated based on experimental data with two purposes: correlating the engineering properties with the material composition and estimating quantitatively the mixture components individual influence on the engineering properties by means of sensitivity analysis (SA). It was found that both ash and WDF are compatible with clay and can partially replace it in construction materials. SA demonstrated that both ash and WDF have the effect of reducing density and compressive strength and increasing pore density.