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Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups

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Abstract

Shear design of RC beams with and without stirrups using laboratory experiments is difficult or even impossible as a large number of variables need to be considered simultaneously, such as the span-to-depth ratio, web width and reinforcement ratio. In addition, due to the complex shear failure mechanism, empirical approaches for shear design are derived within the boundaries of their own testing regimes. Thus, the generalization ability and applicability of these approaches are limited. To address this issue, this study uses machine learning approaches for shear design. A random forest model is constructed to predict the shear strength of RC beams. The hyperparameters of RF are tuned using beetle antennae search algorithm modified by Levy flight and inertia weight. The developed model is trained on two data sets of RC beams with and without stirrups containing 194 and 1849 samples, respectively. The obtained model has high prediction accuracy with correlation coefficients of 0.9367 and 0.9424 on these two test data sets, respectively. The proposed method is powerful and efficient in shear design of RC beams with and without stirrups and therefore paves the way to intelligent construction.

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Acknowledgements

This study was funded by National Key Research and Development Program (Grant number 2016YFC0600901), China Scholarship Council (Grant number 201706460008), National Natural Science Foundation of China (Grant numbers 51574224, 51704277) and Fundamental Research Funds for the Central Universities (grant number: 2020ZDPY0221).

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Authors and Affiliations

  1. Department of Civil, Environmental and Mining Engineering, The University of Western Australia, Perth, 6009, Australia

    Junfei Zhang & Junbo Sun

  2. Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Yuantian Sun & Guichen Li

  3. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 10093, China

    Yuhang Wang

  4. School of Information Technology, Deakin University, Geelong, Australia

    Jianxin Li

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  1. Junfei Zhang
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  2. Yuantian Sun
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Correspondence to Yuantian Sun.

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Zhang, J., Sun, Y., Li, G. et al. Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups. Engineering with Computers 38, 1293–1307 (2022). https://doi.org/10.1007/s00366-020-01076-x

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  • DOI: https://doi.org/10.1007/s00366-020-01076-x

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