Probabilistic seismic demand analysis of irregular continuous girder bridge based on Gaussian mixture modeling

Abstract:

To capture nonlinear variations in the mean and variance of seismic demand relative to seismic intensity measures, this study proposes a probabilistic seismic demand model based on a Gaussian mixture model (GMM). Grounded in statistical principles, the model estimates the joint and conditional probability distributions of seismic demand and intensity, subsequently established via Gaussian mixture regression (GMR). Using a 3-span, 30-meter continuous T-beam bridge as a case study, we analyze the model′s characteristics and compare them with traditional log-linear regression approaches. Results indicate that structural states exhibit distinct stage-dependent characteristics due to constraints from abutments and shear keys on girder displacement, coupled with backfill soil effects and pier column yielding. Consequently, the joint distribution of seismic demand and intensity displays significant multimodality, which the GMM effectively captures. Owing to its capacity to characterize nonlinear variations in seismic demand statistics, the GMM-based model demonstrates superior fitting performance, yielding fragility curves that differ discernibly from those generated by log-linear regression. Furthermore, mixture components of the joint probability distribution—alongside the mean and variance of the GMM-based model—show strong correlations with the structure′s stage-dependent behavioral phases.