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Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors

Author

Listed:
  • Hamed Safayenikoo

    (Department of Civil Engineering, Chabahar Maritime University, Chabahar 9971778631, Iran)

  • Fatemeh Nejati

    (Department of Art and Architecture, Faculty of Architecture, Khatam University, Tehran 1991633357, Iran)

  • Moncef L. Nehdi

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

Abstract

Estimating the mechanical parameters of concrete is significant towards achieving an efficient mixture design. This research deals with concrete slump analysis using novel integrated models. To this end, four wise metaheuristic techniques of biogeography-based optimization (BBO), salp swarm algorithm (SSA), moth-flame optimization (MFO), and wind driven optimization (WDO) are employed to optimize a popular member of the neural computing family, namely multilayer perceptron (MLP). Four predictive ensembles are constructed to analyze the relationship between concrete slump and seven concrete ingredients including cement, water, slag, fly ash, fine aggregate, superplasticizer, and coarse aggregate. After discovering the optimal complexities by sensitivity analysis, the results demonstrated that the combination of metaheuristic algorithms and neural methods can properly handle the early prediction of concrete slump. Moreover, referring to the calculated ranking scores (RSs), the BBO-MLP (RS = 21) came up as the most accurate model, followed by the MFO-MLP (RS = 17), SSA-MLP (RS = 12), and WDO-MLP (RS = 10). Lastly, the suggested models can be promising substitutes to traditional approaches in approximating the concrete slump.

Suggested Citation

  • Hamed Safayenikoo & Fatemeh Nejati & Moncef L. Nehdi, 2022. "Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10373-:d:893260
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    References listed on IDEAS

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    Cited by:

    1. Arash Mohammadi Fallah & Ehsan Ghafourian & Ladan Shahzamani Sichani & Hossein Ghafourian & Behdad Arandian & Moncef L. Nehdi, 2023. "Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    2. Cheng-Hsiung Tsai & Yu-Da Lin & Cheng-Hong Yang & Chien-Kun Wang & Li-Chun Chiang & Po-Jui Chiang, 2023. "A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    3. Celal Cakiroglu & Gebrail Bekdaş & Sanghun Kim & Zong Woo Geem, 2022. "Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete," Sustainability, MDPI, vol. 14(21), pages 1-24, November.

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