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A practical ANN model for predicting the PSS of two-way reinforced concrete slabs

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Abstract

This paper aims to develop a practical artificial neural network (ANN) model for predicting the punching shear strength (PSS) of two-way reinforced concrete slabs. In this regard, a total of 218 test results collected from the literature were used to develop the ANN models. Accordingly, the slab thickness, the width of the column section, the effective depth of the slab, the reinforcement ratio, the compressive strength of concrete, and the yield strength of reinforcement were considered as input variables. Meanwhile, the PSS was considered as the output variable. Several ANN models were developed, but the best model with the highest coefficient of determination (R2) and the smallest root mean square errors was retained. The performance of the best ANN model was compared with multiple linear regression and existing design code equations. The comparative results showed that the proposed ANN model was provided the most accurate prediction of PSS of two-way reinforced concrete slabs. The parametric study was carried out using the proposed ANN model to assess the effect of each input parameter on the PSS of two-way reinforced concrete slabs. Finally, a graphical user interface was developed to apply for practical design of PSS of two-way reinforced concrete slabs.

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Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (No. 2018R1A2A2A05018524 and No. 2019R1A4A1021702).

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul, 05006, South Korea

    Viet-Linh Tran & Seung-Eock Kim

  2. Department of Civil Engineering, Vinh University, Vinh, 461010, Vietnam

    Viet-Linh Tran

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  1. Viet-Linh Tran
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  2. Seung-Eock Kim
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Correspondence to Seung-Eock Kim.

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Tran, VL., Kim, SE. A practical ANN model for predicting the PSS of two-way reinforced concrete slabs. Engineering with Computers 37, 2303–2327 (2021). https://doi.org/10.1007/s00366-020-00944-w

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