Bendable concrete, also known as Engineered Cementitious Composite (ECC) is a type of ultra-ductile cementitious composites reinforced with fibres to control the width of cracks. It has the ability to enhance concrete flexibility by withstanding strains of 3% and higher. The properties of bendable concrete mixes (compressive strength, flexural strength, and drying shrinkage) are here assessed after the incorporation of supplementary cementitious materials, silica fume, polymer fibres, and the use of ordinary Portland cement (O.P.C) and Portland limestone cement (IL). Mixes with Portland limestone cement show lower drying shrinkage and lower compressive and flexural strength than mixes with ordinary Portland cement, due to the ratio of clinker existence being lower in the Portland limestone cement.
From the sustainability point of view a combination of using water absorption polymer balls in concrete mix produce from Portland limestone cement (IL) is worth to be perceived. Compressive strength and drying shrinkage behavior for the mixes of concrete prepared by Ordinary Portland Cement (O.P.C) and Portland limestone cement (IL) were investigated in this research. Water absorbent polymer balls (WAPB) are innovative module in producing building materials due to the internal curing which eliminates autogenous shrinkage, enhances the strength at early age, improve the durability, give higher compressive strength at early age, and reduce the effect of insufficient external curing. Polymer balls (WAPB) had been used in the mixes of this research to provide good progress in compressive strength with time. Water absorption polymer balls have the ability to absorb water and after usage in concrete it will spill it out and shrink leaving voids of their own diameter before shrinking that lead to provide internal curing. The required quantity of water for the mixes were reduced due to the addition of water from the absorption polymers. Mixes produced from Portland limestone cement in this research show drying shrinkage results and compressive strength results lower than mixes made from ordinary Portland cement.
Artificial Neural Networks, ANN, technique is a computerized system that is built to simulate the neural networks in the human brain. Throughout the recent couple decades, ANNs had solved with a good degree of success many problems. In the present work, ANN model was developed by SPSS software for estimating creep strain development of self-compacting concrete mixes produced with different types of Portland cement, Type I and Type IL. The independent variables in this model were: age, compressive strength, modulus of elasticity, applied stress, initial strain, water to powder ratio, water to binder ratio, filler to cement ratio, clinker to cement ratio, aggregate size, and slump flow. The used data for model building were local, extracted from the present work. The predictions of the model have been compared to those of an international well-known model, ACI 209 Committee. The comparison revealed the good reliability of the present models in predictions (r = 0.998).
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