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Concrete is one of the most important construction materials in industry due to its versatile applicability in commercial and industrial projects. Slump test measures the workability or consistency of concrete and slump flow test evaluates the flowability of self-compacting concrete (SCC). Concrete production with an acceptable slump value and also the related testing procedures are complex and uncertain. In this research, slump, slump flow, and compressive strength tests of concrete are examined by developing a Type-1 Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed fuzzy inference system is evaluated using root mean squared error (RMSE) and mean absolute error (MAE) metrics. The results obtained from the implemented fuzzy inference system show competitive performance compared to the existing machine learning and neural network models.

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Predicting Slump, Slump Flow and Compressive Strength of Concrete Using Type-1 TSK Fuzzy System

  • 会议论文

  • 发表日期：

    2026年

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  • Ali Aghajari,
  • Samin Yadollahi,
  • Hooman Tahayori,
  • Ali Bahadori-Jahromi,
  • Amir Hossein Moharrer

摘要

Concrete is one of the most important construction materials in industry due to its versatile applicability in commercial and industrial projects. Slump test measures the workability or consistency of concrete and slump flow test evaluates the flowability of self-compacting concrete (SCC). Concrete production with an acceptable slump value and also the related testing procedures are complex and uncertain. In this research, slump, slump flow, and compressive strength tests of concrete are examined by developing a Type-1 Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed fuzzy inference system is evaluated using root mean squared error (RMSE) and mean absolute error (MAE) metrics. The results obtained from the implemented fuzzy inference system show competitive performance compared to the existing machine learning and neural network models.