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Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence

Auteur(s):



Médium: article de revue
Langue(s): en 
Publié dans: Advances in Civil Engineering, , v. 2020
Page(s): 1-12
DOI: 10.1155/2020/3012139
Abstrait:

This paper describes the application of two artificial intelligence- (AI-) based methods to predict the 28-day compressive strength of fiber-reinforced high-strength self-compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sources were used for training, validation, and testing models. Various AI models were developed with different training algorithms and a number of nodes in the hidden layer to obtain the optimal model for the FRHSSCC data. It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. Specifically, the overall coefficient of determination (R2) of the ANN and ANFIS models was 0.9742 and 0.9584, respectively. The sensitivity analysis was also conducted with the ANN model to investigate the effects of input parameters on the output. The results from the sensitivity analysis revealed that the compressive strength of FRHSSCC at 28 days was more sensitive with the changes of water by cement ratio (WCR) parameter and insensitive with varying amounts of fiber (VOF). Finally, it can be concluded that the application of artificial intelligence shows the great potential in the prediction of compressive strength of FRHSSCC.

Copyright: © Tu T. Nguyen et al. et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10462462
  • Publié(e) le:
    25.10.2020
  • Modifié(e) le:
    25.10.2020