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Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls

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Abstract

Accurate prediction of the ultimate shear capacity of reinforced concrete shear walls (RCSWs) is essential for robust design of buildings under seismic and wind loads. However, the shear capacity of RCSWs depends on multiple complex design variables characterized by diverse geometric and materials properties. Thus, a powerful modeling framework is required. In this paper, a hybrid artificial intelligence model is proposed for predicting the ultimate shear capacity of RCSWs named artificial neural network (ANN) coupled with adaptive harmony search optimization (AHS) algorithm. Different statistical metrics were used to compare the performances of the ANN model coupled with AHS (ANN-AHS) to three existing empirical relations and two ANN models combined with harmony search (ANN-HS) and global-best harmony search (ANN-GHS). Results show that the proposed ANN-AHS achieved superior performance in modelling the shear strength of RCSWs compared to ANN-HS and ANN-GHS models. The soft-computing models have proven to be more accurate than existing empirical relations.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Behrooz Keshtegar

  2. Department of Civil and Environmental Engineering, Western University, London, ON, Canada

    Moncef L. Nehdi

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

    Reza. Kolahchi

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

    Nguyen-Thoi Trung

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

    Nguyen-Thoi Trung

  6. Department of Civil Engineering, Birjand University of Technology, P.B. 9719866981, Birjand, Iran

    Mansour Bagheri

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  1. Behrooz Keshtegar
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  2. Moncef L. Nehdi
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  3. Reza. Kolahchi
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  4. Nguyen-Thoi Trung
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  5. Mansour Bagheri
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Correspondence to Moncef L. Nehdi or Reza. Kolahchi.

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Keshtegar, B., Nehdi, M.L., Kolahchi, R. et al. Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls. Engineering with Computers 38 (Suppl 5), 3915–3926 (2022). https://doi.org/10.1007/s00366-021-01302-0

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  • DOI: https://doi.org/10.1007/s00366-021-01302-0

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