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A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

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Abstract

Compressive strength of concrete is one of the most determinant parameters in the design of engineering structures. This parameter is generally determined by conducting several tests at different ages of concrete in spite of the fact that such tests are not only costly but also time-consuming. As an alternative to these tests, machine learning (ML) techniques can be used to estimate experimental results. However, the dependence of compressive strength on different parameters in the fabrication of concrete makes the prediction problem challenging, especially in the case of concrete with partial replacements for cement. In this investigation, an extreme learning machine (ELM) is combined with a metaheuristic algorithm known as grey wolf optimizer (GWO) and a novel hybrid ELM-GWO model is proposed to predict the compressive strength of concrete with partial replacements for cement. To evaluate the performance of the ELM-GWO model, five of the most well-known ML models including an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), an extreme learning machine, a support vector regression with radial basis function (RBF) kernel (SVR-RBF), and another SVR with a polynomial function (Poly) kernel (SVR-Poly) are developed. Finally, the performance of the models is compared with each other. The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model. Also, it is deducted that the ELM-GWO model is capable of reaching superior performance indices in comparison with those of the other models.

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Authors and Affiliations

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

    Mahdi Shariati

  2. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mohammad Saeed Mafipour, Behzad Ghahremani & Fazel Azarhomayun

  3. Department of Civil Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran

    Masoud Ahmadi

  4. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, 758307, Vietnam

    Nguyen Thoi Trung & Ali Shariati

  5. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, 758307, Vietnam

    Nguyen Thoi Trung & Ali Shariati

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  1. Mahdi Shariati
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  2. Mohammad Saeed Mafipour
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  3. Behzad Ghahremani
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  7. Ali Shariati
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Shariati, M., Mafipour, M.S., Ghahremani, B. et al. A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Engineering with Computers 38, 757–779 (2022). https://doi.org/10.1007/s00366-020-01081-0

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