The natural period is a key parameter in seismic design, but current empirical code formulas act as lower bounds for design safety, making them overly conservative for the precise performance assessment of existing buildings. To derive an optimal best estimate of the actual dynamic behavior, this study proposes a novel methodology based on 283 measured data points worldwide. Overcoming the limitations of conventional single-variable models, this study introduces story height as a physical proxy variable alongside data clustering techniques. Story height extends beyond simple geometry, indirectly representing mass distribution and structural stiffness design levels, thereby effectively controlling the dispersion of heterogeneous global data on physical grounds. Consequently, the proposed piecewise bivariate non-linear regression model achieved a significantly lower RMSE across all structural systems compared to existing design codes and single-variable models, substantially improving prediction accuracy. Unlike traditional fixed-constant approaches, this continuously upgradable framework can serve as a robust foundational model for large-scale seismic screening in smart cities and digital twin-based maintenance systems.
Na et al. (Fri,) studied this question.
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