ANN modelling approach for predicting SCC properties - Research considering Algerian experience .Part II. Effects of aggregates types and contents

  • Mohamed Sahraoui Structures Rehabilitation and Materials Laboratory (SREML), University Amar Telidji, Laghouat, Algeria.
  • Tayeb Bouziani Structures Rehabilitation and Materials Laboratory (SREML), University Amar Telidji, Laghouat, Algeria.
DOI: https://doi.org/10.34118/jbms.v8i1.778
Keywords: SCC, ANN, aggregates types and contents, contour plots, fresh and hardened properties

Abstract

The objective of this investigation is to illustrate the effect of aggregates types and contents on fresh and hardened properties of self-compacting concrete (SCC) considering Algerian experience. Based on experimental data available in the literature, Artificial neural network (ANN) models are established to illustrate the variation of aggregate types and contents (sand and gravel) in binary and ternary contour plots. Modelling results concerning the effect of sand types and proportions in binary and ternary combinations show the beneficial effect of river sand (RS) and crushed sand (CS) on slump flow. The highest L-Box ratio was obtained for mixtures composed of 50% of both RS and CS for binary and ternary mixtures. The increase in CS content enhance static stability, while the increase in RS gives higher compressive strength at 28 days. Concerning the study of aggregate sizes and contents, it was found that the increase of sand content leads to an increase in flowability and a decrease in static stability. An increase in gravel content leads to a decrease in passing ability, while a significant improvement in viscosity, static stability and mechanical strength with an increase in gravel content were observed.

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Published
2021-06-29
How to Cite
Sahraoui, M., & Bouziani, T. (2021). ANN modelling approach for predicting SCC properties - Research considering Algerian experience .Part II. Effects of aggregates types and contents. Journal of Building Materials and Structures, 8(1), 63-71. https://doi.org/10.34118/jbms.v8i1.778
Issue
Vol 8 No 1 (2021)
Section
Original Articles

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