Parametric modeling framework for assessing urban structure vulnerability to natural disasters

This paper presents a computational simulation framework to evaluate the probabilistic
vulnerability of urban residential buildings and critical infrastructure under flood conditions. Part of a
broader platform assessing city-wide vulnerability to natural disasters, this framework develops digital
twins of infrastructure components and analyzes their interdependencies through coupled simulations,
focusing on buildings as key urban infrastructure. To improve traditional vulnerability assessment methods,
a multi-step approach is proposed. It begins by defining the urban area and compiling its buildings
into a database. A categorization step groups these buildings into representative types, automated using
data from digital city models. Probabilistic analysis, including Monte Carlo simulations and structural
analysis, generates fragility curves that quantify damage probability based on loading intensity. Parametric
building models account for uncertainties in material properties and categorization variability, with
results integrated back into the building database for large-scale assessments. The modular implementation
allows integration with various software tools for nonlinear finite element analysis and probabilistic
post-processing. A case study demonstrates the process, yielding fragility curves that provide insights
into structural vulnerability, aiding in damage prediction and flood response strategies.