The Application of the Novel Kolmogorov–Arnold Networks for Predicting the Fundamental Period of RC Infilled Frame Structures

• DOI: 10.1002/msd2.70004
• Publication Date: 2025-03-19T02:44:06Z
• Authors: Shan Lin; Kaiyang Zhao; Hongwei Guo; Quanke Hu; Xitailang Cao; Hong Zheng

ABSTRACT The fundamental period is a crucial parameter in structural dynamics that informs the design, assessment, and monitoring of structures to ensure safety stability buildings during earthquakes. Numerous machine‐learning deep‐learning approaches have been proposed predict infill‐reinforced concrete frame structures. However, challenges remain, including insufficient prediction accuracy excessive computational resource demands. This study aims provide new paradigm for accurately efficiently predicting periods, namely, Kolmogorov–Arnold networks (KANs) their variants, especially radial basis function KANs (RBF‐KANs). are formulated based on representation theorem, positioning them as promising alternative multilayer perceptron. In this research, we compare performance against fully connected neural (FCNNs) context prediction. mutual information method was employed analysis dependencies between features FP4026 data set. Nine predictive models, KANs, F‐KANs, FCNN‐2, FCNN‐11, CatBoost, Support Vector Machine, others, were constructed compared, with hyperparameters determined by Optuna, which will highlight optimal model amongst F‐KANs models. Numerical results manifest highest yielded R 2 = 0.9948, offers an explicit form formula. Lastly, further dive into explainability interpretability revealing number stories opening percentage significant effect results.