Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

<p>Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (<em>W</em>/<em>B</em>), 183–317 kg/m<sup style="border: 0px; font-size: 0.75em; margin: 0px; padding: 0px; line-height: 0; color: rgb(46, 46, 46); font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, sans-serif; text-align: justify; word-spacing: -1px; background-color: rgb(255, 255, 255);">3</sup> of cement content, 29–261 kg/m<sup style="border: 0px; font-size: 0.75em; margin: 0px; padding: 0px; line-height: 0; color: rgb(46, 46, 46); font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, sans-serif; text-align: justify; word-spacing: -1px; background-color: rgb(255, 255, 255);">3</sup> of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.</p>