Quantification of model uncertainties in regional-scale seismic analysis of building portfolios

Seismic events are characterized by abrupt onset, wide spatial impact, and substantial destructive potential, often leading to cascading socioeconomic consequences. At the regional scale, seismic assessment faces two persistent challenges: (i) building inventories typically lack complete, high-resolution structural information, and (ii) refined nonlinear models are computationally prohibitive for large portfolios. Against this background, this paper develops simplified multi-spring models for building portfolios in regional-scale seismic analysis. Specifically, the modeling framework consists of a lumped-shear multi-degree of freedom (MDOF) model for multi-story building, and a lumped flexural-shear-coupling MDOF model for high-rise buildings. Moreover, two types of parameters (i.e., coarse-scale and fine-scale) are compared in model generation, which are based on the building-level attributes and the component-level capacity characteristics, respectively. Both the parameter variability and seismic uncertainties (i.e., individual and combined parameter) are incorporated during the simulation to assess the demand variations. The results show that the simplified representation preserves essential seismic response characteristics while enabling high computational efficiency suitable for regional applications. To further enhance reliability, a lognormal-based probabilistic revision strategy is introduced to calibrate coarse-scale seismic demand statistics (median and dispersion) using fine-scale reference data. The resulting framework provides a practical and efficient solution for seismic assessments at a regional scale, particularly for diverse building portfolios.