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Novel Evolutionary-Optimized Neural Network for Predicting Fresh Concrete Slump

Author

Listed:
  • Hamed Safayenikoo

    (Department of Civil Engineering, Chabahar Maritime University, Chabahar 99717-78631, Iran)

  • Mohammad Khajehzadeh

    (Department of Civil Engineering, Anar Branch, Islamic Azad University, Anar 77419-43615, Iran)

  • Moncef L. Nehdi

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4M6, Canada)

Abstract

Accurate prediction of fresh concrete slumps is a complex non-linear problem that depends on several parameters including time, temperature, and shear history. It is also affected by the mixture design and various concrete ingredients. This study investigates the efficiency of three novel integrative approaches for predicting this parameter. To this end, the vortex search algorithm (VSA), multi-verse optimizer (MVO), and shuffled complex evolution (SCE) are used to optimize the configuration of multi-layer perceptron (MLP) neural network. The optimal complexity of each model was appraised via sensitivity analysis. Various statistical metrics revealed that the accuracy of the MLP was increased after coupling it with the above metaheuristic algorithms. Based on the obtained results, the prediction error of the MLP was decreased by up to 17%, 10%, and 33% after applying the VSA, MVO, and SCE, respectively. Moreover, the SCE emerged as the fastest optimizer. Accordingly, the novel explicit formulation of the SCE-MLP was introduced as a capable model for the practical estimation of fresh concrete slump, which can assist in project planning and management.

Suggested Citation

  • Hamed Safayenikoo & Mohammad Khajehzadeh & Moncef L. Nehdi, 2022. "Novel Evolutionary-Optimized Neural Network for Predicting Fresh Concrete Slump," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4934-:d:797715
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    References listed on IDEAS

    as
    1. Fathy, Ahmed & Rezk, Hegazy, 2018. "Multi-verse optimizer for identifying the optimal parameters of PEMFC model," Energy, Elsevier, vol. 143(C), pages 634-644.
    2. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
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    1. Rana Muhammad Adnan Ikram & Imran Khan & Hossein Moayedi & Atefeh Ahmadi Dehrashid & Ismail Elkhrachy & Binh Nguyen Le, 2024. "Novel evolutionary-optimized neural network for predicting landslide susceptibility," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 17687-17719, July.
    2. Di Liang & Jieyi Wang & Ran Bhamra & Liezhao Lu & Yuting Li, 2022. "A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS–ABC Algorithm," Mathematics, MDPI, vol. 10(21), pages 1-24, October.

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