Construction And Building Materials, cilt.301, sa.1, 2021 (SCI-Expanded)
In this study, the parameters affecting Marsh-funnel flow time and mini-slump of the paste mixtures
were determined through experimental and modelling studies. Marsh-funnel flow times were modelled
through artificial intelligence and regression methods. A novel model was used to train the coefficients of
artificial neural networks (ANN) with the Teaching-Learning Based Artificial Bee Colony (TLABC)
Algorithm. Accuracy of this method was investigated through ANN-Back Propagation, ANN-Teaching
Learning Based Optimization Algorithm, ANN-Artificial Bee Colony, Multivariate Adaptive Regression
Splines and Classical Regression Analysis methods. ANN-TLABC method showed the best results among
the applied models. The admixture content, cement fineness, solid material content of admixture and
C3A content of cement were found to be the most important parameters affecting the flowability of
the paste. However, C2S, equivalent alkali, C4AF and C3S contents of the cement were observed to have
no considerable effect on the Marsh-funnel flowability of paste.