Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks

Sameh Fuqaha

doi:10.64808/engineeringperspective.1807130

Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks

Abstract

This study presents the development and evaluation of an Artificial Neural Network (ANN) model for predicting the shear strength of reinforced concrete (RC) beam–column joints subjected to seismic loading. A comprehensive experimental database was compiled from more than 120 RC beam–column joint test specimens reported in the literature and used to train, validate, and test the ANN within MATLAB’s Neural Network Toolbox environment. The model employed the Levenberg–Marquardt backpropagation algorithm, a single hidden layer with an optimized number of neurons, a hyperbolic tangent sigmoid transfer function in the hidden layer, and a linear activation function at the output layer. Input parameters included concrete grade, reinforcement ratio, axial load, and joint geometry, while the output corresponded to joint shear strength. The ANN achieved outstanding predictive performance, with a coefficient of determination (R²) exceeding 0.99 and minimal error metrics (MSE = 0.000105), outperforming multiple regression models and ten widely adopted international design codes. Sensitivity analysis further revealed that reinforcement ratio and axial load were the most influential predictors of joint shear capacity. In addition to numerical prediction, the ANN demonstrated strong generalization capability and robustness across different concrete grades (M25–M40) and design standards. The results highlight the superior adaptability of machine learning compared to conventional design approaches, offering an innovative, data-driven framework for seismic performance assessment. This research contributes to the advancement of performance-based design methodologies by integrating artificial intelligence into structural engineering, paving the way for more accurate, efficient, and reliable seismic safety evaluations of RC joints.

Keywords

Supporting Institution

This research was supported by the Department of Civil Engineering, Universitas Muhammadiyah Yogyakarta, Indonesia.

Ethical Statement

The author confirms that this study was conducted in accordance with ethical research standards. No experiments involving humans or animals were performed. All data used in this research were obtained from published experimental studies, ensuring transparency and academic integrity.

Thanks

The author gratefully acknowledges the support and facilities provided by Universitas Muhammadiyah Yogyakarta, which made this research and publication possible.

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Details

Primary Language

English

Subjects

Reinforced Concrete Buildings, Earthquake Engineering

Journal Section

Research Article

Authors

Sameh Fuqaha ^*
0009-0000-6914-7148
Indonesia

Publication Date

January 14, 2026

Submission Date

October 20, 2025

Acceptance Date

January 2, 2026

Published in Issue

Year 2026 Volume: 6 Number: 1

DOI

https://doi.org/10.64808/engineeringperspective.1807130

IZ

https://izlik.org/JA99WP87EC

Cite

RIS / Bibtex

APA

Fuqaha, S. (2026). Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks. Engineering Perspective, 6(1), 19-32. https://doi.org/10.64808/engineeringperspective.1807130

AMA

1.Fuqaha S. Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks. engineeringperspective. 2026;6(1):19-32. doi:10.64808/engineeringperspective.1807130

Chicago

Fuqaha, Sameh. 2026. “Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks”. Engineering Perspective 6 (1): 19-32. https://doi.org/10.64808/engineeringperspective.1807130.

EndNote

Fuqaha S (January 1, 2026) Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks. Engineering Perspective 6 1 19–32.

IEEE

[1]S. Fuqaha, “Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks”, engineeringperspective, vol. 6, no. 1, pp. 19–32, Jan. 2026, doi: 10.64808/engineeringperspective.1807130.

ISNAD

Fuqaha, Sameh. “Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks”. Engineering Perspective 6/1 (January 1, 2026): 19-32. https://doi.org/10.64808/engineeringperspective.1807130.

JAMA

1.Fuqaha S. Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks. engineeringperspective. 2026;6:19–32.

MLA

Fuqaha, Sameh. “Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks”. Engineering Perspective, vol. 6, no. 1, Jan. 2026, pp. 19-32, doi:10.64808/engineeringperspective.1807130.

Vancouver

1.Sameh Fuqaha. Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks. engineeringperspective. 2026 Jan. 1;6(1):19-32. doi:10.64808/engineeringperspective.1807130

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