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http://dx.doi.org/10.12989/scs.2022.44.6.867

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)
Publication Information
Steel and Composite Structures / v.44, no.6, 2022 , pp. 867-882 More about this Journal
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
concrete compressive strength; high-performance concrete; multi-layer perceptron; non-linear analysis;
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