ANN modelling approach for predicting SCC properties - Research considering Algerian experience. Part I. Development and analysis of models
Abstract
This paper presents research on the use of artificial neural networks (ANNs) to predict fresh and hardened properties of self compacting concrete (SCC) made with Algerian materials. A multi-layer perceptron network with 5 nodes, 12 inputs, and 5 outputs is trained and optimized using a database of 167 mixtures collected from literature. The inputs for the ANN models are ordinary Portland cement (Cm), polycarboxylate ether superplasticizer (Sp), river sand (RS), crushed sand (CS), dune sand (DS), Gravel 3/8 (G1), Gravel 8/15 (G2), Water (W), Limestone filler (Lim), Marble powder (MP), blast furnace slag (Slag) and natural pozzolan (Pz). Instead, Slump flow (Slump), V-funnel, L-Box, static stability (Pi) and 28 days compressive strength (Rc28) were the outputs of the study. Results indicate that ANN models for data sets collected from literature have a strong potential for predicting 28 days compressive strength. Slump flow, V-funnel time and L-Box ratio could be moderately identified while an acceptable prediction has been obtained for static stability. Results have also confirmed by statistical parameters, Regression plots and residual analysis.
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