Steel and Composite Structures

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Optimized ANNs for predicting compressive strength of high-performance concrete

  • Moayedi, Hossein (Institute of Research and Development, Duy Tan University) ;
  • Eghtesad, Amirali (Department of Engineering, Islamic Azad University Science and Research Branch) ;
  • Khajehzadeh, Mohammad (Department of Civil Engineering, Anar Branch, Islamic Azad University) ;
  • Keawsawasvong, Suraparb (Department of Civil Engineering, Thammasat School of Engineering, Thammasat University) ;
  • Al-Amidi, Mohammed M. (Information Technology Unit, Al-Mustaqbal University College) ;
  • Van, Bao Le (Institute of Research and Development, Duy Tan University)
  • Received : 2021.09.20
  • Accepted : 2022.09.05
  • Published : 2022.09.25
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Abstract

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

Keywords

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