Smart Structures and Systems

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Predicting the concrete compressive strength through MLP network hybridized with three evolutionary algorithms

  • Geng, Xin (School of Computer and Communication Engineering, Zhengzhou University of Light Industry) ;
  • Moayedi, Hossein (Department for Management of Science and Technology Development, Ton Duc Thang University) ;
  • Pan, Feifei (Zhengzhou Electromechanical Engineering Research Institute) ;
  • Foong, Loke Kok (Institute of Research and Development, Duy Tan University)
  • Received : 2020.01.22
  • Accepted : 2021.07.25
  • Published : 2021.11.25
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Abstract

In this research, we synthesized an artificial neural network (ANN) with three metaheuristic algorithms, namely particle swarm optimization (PSO) algorithm, imperialist competition algorithm (ICA), and genetic algorithm (GA) to achieve a more accurate prediction of 28-day compressive strength of concrete. Seven input parameters (including cement, water, slag, fly ash, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA)) were considered for this work. 80% of data (82 samples) were used to feed ANN, PSO-ANN, ICA-ANN, and GA-ANN models, and their performance was evaluated using the remaining 20% (21 samples). Referring to the executed sensitivity analysis, the best complexities for the PSO and GA were indicated by the population size = 450 and for the ICA by the population size = 400. Also, to assess the accuracy of the used predictors, the accuracy criteria of root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) were defined. Based on the results, applying the PSO, ICA, and GA algorithms led to increasing R2 in the training and testing phase. Also, the MAE and RMSE of the conventional MLP experienced significant decrease after the hybridization process. Overall, the efficiency of metaheuristic science for the mentioned objective was deduced in this research. However, the combination of ANN and ICA enjoys the highest accuracy and could be a robust alternative to destructive and time-consuming tests.

Keywords

Acknowledgement

This work was supported by the Major Science and Technology Innovation Project (No.2019CXZX0042).

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