Predicting the rheological flow of fresh self-consolidating concrete mixed with limestone powder for slump, V-funnel, L-box and Orimet models using artificial intelligence techniques

¹ Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria
² Department of Civil Engineering, School of Engineering, University of the Peloponnese, GR-26334 Patras, Greece
³ School of Science and Technology, Hellenic Open University, GR-26335 Patras, Greece
⁴ Department of Structural Engineering, Future University in Egypt, New Cairo, Egypt
⁵ Department of Civil Engineering, Faculty of Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria

^* Corresponding author: kontoni@uop.gr

Abstract

In this paper, selected materials that influence the viscosity of the self-consolidating concrete (SCC) are introduced like the Limestone Powder (LSP), the High Range Water Reducing Admixture (HRWRA), which reduce the interparticle force between concrete constituents like the aggregates, and other superplasticizers. Moreover, in serious attempts to design the SCC for different infrastructure requirements, there have been repeated laboratory visits, which need to be reduced. In this research paper, the artificial intelligence (AI) methods: Artificial Neural Network (ANN), Evolutionary Polynomial Regression (EPR), and Genetic programming (GP) have been deployed to predict the slump flow (SF), V-funnel flow time (VFFT), L-box ratio (LBR) or passing ratio, and Orimet flow time (OFT) of LSP-admixed SCC. The independent variables of the predictive model were cement, LSP, water, water-binder ratio, HRWRA, sand, and coarse aggregates of 4/8 mm and 8/16 mm sizes. The flow tests were conducted after 5 minutes of waiting time after mixing. The model results showed ANN with superior intelligent learning ability over previous models in terms of overall performance.