Seismic fragility analysis of horizontally curved bridge portfolios using a machine-learning-based partial dependence plot approach

This study employs the uniform design (UD) method to establish a sample database and utilises a machine-learning-based partial dependence plot (PDP) approach to develop a multi-parameter seismic fragility analysis framework for horizontally curved bridge portfolios. Six machine learning (ML) models are compared to identify the optimal seismic fragility prediction model and assess the importance of parameters through permutation importance (PI). Additionally, the PDP is applied to generate seismic fragility curves for the curved bridge portfolios, effectively avoiding the need for high-dimensional mixed integrals. Furthermore, the PDP technique is used to explore the relationship between critical structural attributes and fragility under specified seismic intensity levels. The results indicate that the ML model exhibits favourable fidelity and robustness, with an area under the curve (AUC) exceeding 0.9. The most influential structural attributes affecting the fragility of horizontally curved bridges include boundary type (BT), support design configuration (SPD), column height (H), single-span length (L), and central angle (α). The ductility seismic system and the seismic isolation system for horizontally curved bridges can be preliminarily distinguished based on parameters α, L, and H.