Data-driven prediction of initial rotational stiffness in beam-to-upright connections of steel pallet racks
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The structural performance of steel pallet rack systems is largely governed by the rotational behavior of beam-to-upright connections. Although design practice often idealizes these joints as rigid or pinned, experimental evidence shows that most exhibit a semi-rigid response. Realistic structural analysis therefore requires a reliable estimate of the initial rotational stiffness, k0, which controls the moment–rotation relationship and affects internal force distribution and frame stability. This study proposes a data-driven framework for predicting k0 directly from the geometric and mechanical properties of uprights, beams, and connectors. The model incorporates key sectional parameters and component yield strengths and is trained on a consolidated database compiled from experimentally validated literature. A comparative assessment of regression techniques shows that ensemble-based machine-learning models, particularly the Extra Trees regressor, provide the highest predictive accuracy. Feature importance and SHAP analyses indicate that connection stiffness is primarily governed by geometric parameters controlling load transfer and deformation mechanisms, while material strength plays a secondary role. The proposed approach eliminates the need for extensive experimental testing and supports realistic semi-rigid modelling and more economical pallet rack design.
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