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Regression and artificial neural network models for strength properties of engineered cementitious composites

Authors
  • Hossain, Khandaker M. A.1
  • Anwar, Muhammed S.2
  • Samani, Shirin G.1
  • 1 Ryerson University, Department of Civil Engineering, Toronto, ON, Canada , Toronto (Canada)
  • 2 University of Toronto, Department of Engineering Science, Toronto, ON, Canada , Toronto (Canada)
Type
Published Article
Journal
Neural Computing and Applications
Publisher
Springer-Verlag
Publication Date
Sep 15, 2016
Volume
29
Issue
9
Pages
631–645
Identifiers
DOI: 10.1007/s00521-016-2602-3
Source
Springer Nature
Keywords
License
Yellow

Abstract

This paper describes the development of regression and artificial neural network (ANN) models to determine the 28-day compressive and tensile strength of engineered cementitious composite (ECC) based on the mix design parameters. One hundred eighty ECC mixtures having variable mix designs were obtained from pervious experiments. Factors influencing the strengths were examined to determine the appropriate parameters for the ANN models. The optimized input parameters using training and development of ANN models were used to formulate the regression models. The ANN and regression models were tested with new sets of data for performance validation. Based on the good agreement and other statistical performance parameters, optimized ANN and regression models capable of predicting the strengths of ECC mixtures (using arbitrary mix design parameters) were developed and suggested for practical applications. ANN and regression models demonstrated excellent predictive ability showing predicted experimental strength ratio ranging between 0.95 and 1.00.

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