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Swarm-based hybridizations of neural network for predicting the concrete strength

  • Ma, Xinyan (China Airport Planning & Design Institute Co., Ltd.) ;
  • Foong, Loke Kok (Department for Management of Science and Technology Development, Ton Duc Thang University) ;
  • Morasaei, Armin (Department of Civil Engineering, K.N. Toosi University of Technology) ;
  • Ghabussi, Aria (Department of Civil Engineering, Central Tehran Branch, Islamic Azad University) ;
  • Lyu, Zongjie (Institute of Research and Development, Duy Tan University)
  • Received : 2019.12.08
  • Accepted : 2020.05.29
  • Published : 2020.08.25
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Abstract

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

Keywords

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