Problem statement: For predicting workability and hardened properties of SelfCompacting Concrete (SCC) no well known explicit formulation. Approach: Statistical models were carried out to model the influence of key mixture parameter (cement, water to powder ratio, fly ash and super plasticizer) on hardened properties affecting the performance of SCC. Such responses included compressive strength at 3, 7 and 28 days and modulus of elasticity. Thirty one mixtures were prepared to derive the numerical models and evaluate the accuracy. The models were valid for a wide range of mixture proportioning. Results: The research presented derived numerical models that can be useful to reduce the test procedures and trials needed for the proportioning of self-compacting concrete. The qualities of these models were evaluated based on several factors such as level prediction, residual error, residual mean square and correlation coefficients. Conclusion: Full quadratic models in all the response (compressive strength at 3,7 and 28 days and modulus of elasticity) showed high correlation coefficient (R 2 ), adjusted correlation coefficient, less level of significant and sum of square errors from the four predictions models (linear, interaction, full quadratic and pure quadratic) were developed.

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In this study, the effect of polypropylene (PP) fibers on thermogravimetric parameters of self‐compacting concrete (SCC) containing indigenous materials was investigated experimentally and statistically. The mixes containing cement, water, fly ash, fine aggregate, coarse aggregate, and super plasticizer, with the addition of PP fibers (0%, 0.05%, 0.1%, and 0.15%) by volume of the mixtures, were prepared. The physical properties of SCC were determined at elevated temperatures (200, 400, and 600 °C) after cooling in the laboratory. Regression models were developed to determine the responses, and the optimum amount of 0.05% PP fibers by volume was measured. Copyright © 2012 John Wiley & Sons, Ltd.

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