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RETRACTED ARTICLE: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

Author

Listed:
  • Ali Toghroli

    (University of Malaya)

  • Meldi Suhatril

    (University of Malaya)

  • Zainah Ibrahim

    (University of Malaya)

  • Maryam Safa

    (University of Malaya)

  • Mahdi Shariati

    (University of Malaya)

  • Shahaboddin Shamshirband

    (University of Malaya)

Abstract

Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.

Suggested Citation

  • Ali Toghroli & Meldi Suhatril & Zainah Ibrahim & Maryam Safa & Mahdi Shariati & Shahaboddin Shamshirband, 2018. "RETRACTED ARTICLE: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1793-1801, December.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:8:d:10.1007_s10845-016-1217-y
    DOI: 10.1007/s10845-016-1217-y
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    References listed on IDEAS

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    1. Wong, Pak Kin & Wong, Ka In & Vong, Chi Man & Cheung, Chun Shun, 2015. "Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search," Renewable Energy, Elsevier, vol. 74(C), pages 640-647.
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    Cited by:

    1. Safa, Maryam & Sari, Puteri Azura & Shariati, Mahdi & Suhatril, Meldi & Trung, Nguyen Thoi & Wakil, Karzan & Khorami, Majid, 2020. "Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    2. Ali Toghroli & Meldi Suhatril & Zainah Ibrahim & Maryam Safa & Mahdi Shariati & Shahaboddin Shamshirband, 2020. "Retraction Note to: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1311-1311, June.

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