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Compressive Strength Prediction of Lightweight Short Columns at Elevated Temperature Using Gene Expression Programing and Artificial Neural Network

Author(s):



Medium: journal article
Language(s): English
Published in: Journal of Civil Engineering and Management, , n. 2, v. 26
Page(s): 189-199
DOI: 10.3846/jcem.2020.11931
Abstract:

The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/jcem.2020.11931.
  • About this
    data sheet
  • Reference-ID
    10414419
  • Published on:
    26/02/2020
  • Last updated on:
    26/02/2020
 
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