Journal article Open Access

Predicting Slump Values of Concrete Made by Pozzolans and Manufactured Sand using ANN

Kiran M. Mane; S.P. Chavan; S. A. Salokhe; P. A. Nadgouda; S. T. Patil

Sponsor(s)
Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)

Large amounts of natural fine aggregate (NFA) and cement are used in building, which has major environmental consequences. This view of industrial waste can be used in part as an alternative to cement and part of the sand produced by the crusher as fine aggregate, similar to slag sand (GGBFS), fly ash, metacaolin, and silica fume. Many times, there are issues with the fresh characteristics of concrete when using alternative materials. The ANN tool is used in this paper to develop a Matlab software model that collapses concrete made with pozzolanic material and partially replaces natural fine aggregate (NFA) with manufactured sand (MS). Predict. The slump test was carried out in reference with I.S11991959, and the findings were used to create the artificial neural network (ANN) model. To mimic the formation, a total of 131 outcome values are employed, with 20% being used for model testing and 80% being used for model training. 25 enter the material properties to determine the concrete slump achieved by partially substituting pozzolan for cement and artificial sand (MS) for natural fine aggregate (NFA). According to studies, the workability of concrete is critically harmed as the amount of artificial sand replacing natural sand grows. The ANN model's results are extremely accurate, and they can forecast the slump of concrete prepared by partly substituting natural fine aggregate (NFA) and artificial sand (MS) with pozzolan.

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