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Prediction of compressive strength of concrete modified with fly ash: Applications of neuro-swarm and neuro-imperialism models

  • Mohammed, Ahmed (College of Engineering, Civil Engineering Department, University of Sulaimani) ;
  • Kurda, Rawaz (Department of Highway Engineering Techniques, Technical Engineering College, Erbil Polytechnic University) ;
  • Armaghani, Danial Jahed (Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University) ;
  • Hasanipanah, Mahdi (Institute of Research and Development, Duy Tan University)
  • Received : 2020.12.18
  • Accepted : 2021.04.21
  • Published : 2021.05.25

Abstract

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

Keywords

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