ANN modelling approach for predicting SCC properties - Research considering Algerian experience. Part III. Effect of mineral admixtures

  • 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.v8i2.1080

Abstract

This paper addresses the effect of mineral admixtures on fresh and hardened properties of self compacting concrete (SCC). Artificial neural networks (ANN) and simplex lattice design approach was integrated to predict and evaluate the effect of limestone, marble powder, natural pozzolan and slag on rheological and mechanical properties of SCC evaluated by slump flow, L-Box, V-funnel, sieve segregation test and 28 days compressive strength. The modelling results show an acceptable prediction accuracy of SCC behaviour containing mineral admixtures as substitution of cement especially related to the flow time measured with the V-funnel test and mechanical compressive strength at 28 days.

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Published
2021-12-31
How to Cite
Sahraoui, M., & Bouziani, T. (2021). ANN modelling approach for predicting SCC properties - Research considering Algerian experience. Part III. Effect of mineral admixtures. Journal of Building Materials and Structures, 8(2), 128-138. https://doi.org/10.34118/jbms.v8i2.1080
Issue
Vol 8 No 2 (2021)
Section
Original Articles

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