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<p>A Gaussian Process Regression (GPR) model was developed to predict the shear capacity of exterior reinforced concrete (RC) beam–column joints subjected to seismic loading. The model accounts for key parameters, including beam and column geometry, reinforcement detailing, axial column load, and concrete compressive strength. A database of 273 experimentally tested specimens was used, with emphasis on horizontal joint shear strength. Three kernel structures within the GPR framework—Primary, Rational Second-Order, and Combined kernels—were examined. Model predictions were evaluated against existing shear-strength formulations using deterministic metrics (MAE, RMSE, and R<sup>2</sup>) and probabilistic measures (NLPD and MSLL). The results show that kernel effectiveness depends on model formulation; however, the Combined kernel exhibited more stable predictions and improved uncertainty calibration for models with higher-dimensional input sets. Sensitivity analysis identified concrete compressive strength, beam depth, and joint transverse reinforcement as dominant variables, followed by column height, axial load ratio, and reinforcement configuration. Overall, the proposed framework enables consistent shear-strength prediction and quantifies the relative influence of geometric and material parameters, contributing to more informed assessment and design of RC beam–column joints.</p>

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Prediction of shear strength in exterior reinforced concrete joints using kernel-based Gaussian Regression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Amir Alvandkoohy,
  • Jalil Shafaei,
  • Amin Golabpour

摘要

A Gaussian Process Regression (GPR) model was developed to predict the shear capacity of exterior reinforced concrete (RC) beam–column joints subjected to seismic loading. The model accounts for key parameters, including beam and column geometry, reinforcement detailing, axial column load, and concrete compressive strength. A database of 273 experimentally tested specimens was used, with emphasis on horizontal joint shear strength. Three kernel structures within the GPR framework—Primary, Rational Second-Order, and Combined kernels—were examined. Model predictions were evaluated against existing shear-strength formulations using deterministic metrics (MAE, RMSE, and R2) and probabilistic measures (NLPD and MSLL). The results show that kernel effectiveness depends on model formulation; however, the Combined kernel exhibited more stable predictions and improved uncertainty calibration for models with higher-dimensional input sets. Sensitivity analysis identified concrete compressive strength, beam depth, and joint transverse reinforcement as dominant variables, followed by column height, axial load ratio, and reinforcement configuration. Overall, the proposed framework enables consistent shear-strength prediction and quantifies the relative influence of geometric and material parameters, contributing to more informed assessment and design of RC beam–column joints.