Ternary and quaternary blends as partial replacement of cement to produce hollow sandcrete blocks.

Hollow sandcrete blocks constitute more than 90% of residential building construction in developing countries especially in West Africa. Over-reliance on dredged river sands and conventional ordinary Portland cement (OPC) contributes to environmental degradation and post-construction problems such as swelling and shrinkage-induced cracks prevalent in construction projects. The study investigates potential utilization of locally available materials such as laterite, calcite and calcined clay as ternary and quaternary blends to replace cement and quarry dust as 100% replacement of river sand with the aid of Taguchi-Response surface methodology approach.

Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete.

Our study is aimed at modeling the effect of three contributory factors, namely aspect ratio, water cement ratio and cement content on the water intake/absorption, compressive strength, flexural strength, split tensile strength and slump properties of steel fiber reinforced concrete. Artificial neural network (ANN) as a multilayer perceptron normal feed forward network was integrated to develop a predictive model for the aforementioned properties. Five training algorithms belonging to three classes: gradient descent, Levenberg Marquardt (quasi Newton) and genetic algorithm (GA).