Comprehensive analysis of structural parameters influencing the fundamental period of steel-braced RC buildings using machine learning interpretability - Rahman, Taimur; Momin, Md. Farhad; Provasha, Afra Anam (AI in Civil Engineering, 2025)
The accurate prediction of the fundamental period of steel-braced reinforced concrete (RC) buildings is crucial for optimizing seismic design and ensuring structural safety. Traditionally, empirical formulas provided by building codes such as Eurocode 8 and ASCE 7–22 primarily rely on building height to estimate the fundamental period. However, these height-based models often overlook the significant influence of other structural parameters, such as bracing configurations, bracing lengths, and material properties. This study addresses these limitations by offering a comprehensive evaluation of the factors affecting the fundamental period of steel-braced RC buildings, using advanced computational techniques for more precise and interpretable predictions. A dataset comprising 17,280 building models with varied structural configurations was generated using computational simulations. Key parameters, including total building height, bracing type, bracing length, and building dimensions, were systematically varied. The study utilized machine learning techniques and employed SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) plots as post-hoc interpretability tools to analyze the contributions of structural parameters. Results show that total building height remains the dominant factor, contributing approximately 45% to the predicted fundamental period, while bracing length and bracing type significantly influence the period, reducing it by up to 20%. The inclusion of these parameters improves prediction accuracy and reveals limitations in existing height-based formulas. The study concludes that height alone is insufficient for accurate prediction of the fundamental period in steel-braced RC buildings. Incorporating bracing systems and other structural factors is essential for more reliable seismic design. These findings contribute to the development of more resilient building codes and enhanced seismic performance.