A computational framework for large-scale seismic simulations of residential building stock

Urban areas reveal particularly vulnerable due to the high concentration of people and, in many cases, their hazard-prone location. Indeed, according to data from the United Nations, about 2/3 of the population will live in large cities by 2050, and the majority of the world's cities are highly exposed to disasters. This paper presents a computational framework to assess the seismic vulnerability and the damage of residential building portfolio in urban areas. First, a surrogated model is proposed to estimate the global capacity of building structures. Monte Carlo simulations are implemented to take into account the uncertainties associated with the material, mechanical, and geometrical parameters. The proposed approach is validated through nonlinear finite element models and a real case study. Then, the proposed computational framework is implemented and applied to a virtual city that is envisioned for being representative of a typical Italian residential building stock. The main achievement of this work is to introduce a new simplified approach for large scale structural analyses to limit the computational efforts while providing reasonable results.

Automated summary

This paper introduces a computational framework for assessing the seismic vulnerability and damage of residential building portfolios in urban areas. The proposed approach uses surrogate models and Monte Carlo simulations to account for uncertainties, and is validated through finite element models and a real case study. The main achievement is the introduction of a simplified approach for large scale structural analyses that limits computational efforts while providing reasonable results.