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Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete

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Abstract

Due to the important role of concrete in construction sector, a novel metaheuristic method, namely whale optimization algorithm (WOA), is employed for simulating 28-day compressive strength of concrete (CSC). To this end, the WOA is coupled with a neural network (NN) to optimize its computational parameters. Also, dragonfly algorithm (DA) and ant colony optimization (ACO) techniques are considered as the benchmark methods. The CSC influential parameters are cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA). First, a population-based sensitivity analysis is carried out to achieve the most efficient structure of the proposed model. In this sense, the WOA-NN with the population size of 400 and five hidden nodes constructed the best-fitted network. The results revealed that the WOA-NN (Error = 2.0746 and Correlation = 0.8976) presents the most reliable prediction of the CSC, followed by the DA-NN (Error = 2.5138 and Correlation = 0.8209) and ACO-NN (Error = 2.8843 and Correlation = 0.8000) benchmark models. The findings showed that utilizing the WOA optimization technique, along with typical neural network, results in developing a promising tool for modeling the CSC.

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Author information

Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Dieu Tien Bui

  2. Civil Engineering Department, College of Engineering, University of Hafr Al-Batin, Al-Jamiah, Eastern Province, 39524, Kingdom of Saudi Arabia

    Mu’azu Mohammed Abdullahi

  3. Department of Civil Engineering, Payam Noor University, Tehran, Iran

    Soheil Ghareh

  4. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  5. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  6. Department of Surface Mining, Hanoi University of Mining Land Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

    Hoang Nguyen

  7. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

    Hoang Nguyen

Authors
  1. Dieu Tien Bui
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  2. Mu’azu Mohammed Abdullahi
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  3. Soheil Ghareh
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  4. Hossein Moayedi
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  5. Hoang Nguyen
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Correspondence to Hossein Moayedi.

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Tien Bui, D., Abdullahi, M.M., Ghareh, S. et al. Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete. Engineering with Computers 37, 701–712 (2021). https://doi.org/10.1007/s00366-019-00850-w

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  • DOI: https://doi.org/10.1007/s00366-019-00850-w

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