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

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