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Prediction of compressive strength of heavyweight concrete by ANN and FL models

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Abstract

The compressive strength of heavyweight concrete which is produced using baryte aggregates has been predicted by artificial neural network (ANN) and fuzzy logic (FL) models. For these models 45 experimental results were used and trained. Cement rate, water rate, periods (7–28–90 days) and baryte (BaSO4) rate (%) were used as inputs and compressive strength (MPa) was used as output while developing both ANN and FL models. In the models, training and testing results have shown that ANN and FL systems have strong potential for predicting compressive strength of concretes containing baryte (BaSO4).

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Authors and Affiliations

  1. Yapı Eğitimi Bölümü, Teknik Eğitim Fakültesi, Süleyman Demirel Üniversitesi, Isparta, Turkey

    C. Başyigit & S. Kilincarslan

  2. Fizik Bölümü, Fen Edebiyat Fakültesi, Süleyman Demirel Üniversitesi, Isparta, Turkey

    Iskender Akkurt

  3. Yapı Egt. bol Teknil Egitim Fakultesi, Duzce Universitesi, Duzce, Turkey

    A. Beycioglu

Authors
  1. C. Başyigit
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  2. Iskender Akkurt
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  3. S. Kilincarslan
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  4. A. Beycioglu
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Correspondence to Iskender Akkurt.

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Başyigit, C., Akkurt, I., Kilincarslan, S. et al. Prediction of compressive strength of heavyweight concrete by ANN and FL models. Neural Comput & Applic 19, 507–513 (2010). https://doi.org/10.1007/s00521-009-0292-9

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  • DOI: https://doi.org/10.1007/s00521-009-0292-9

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