Rosin, JuliaJuliaRosinNeuhäuser, StefanStefanNeuhäuserVetter, JoannaJoannaVetterStolz, AlexanderAlexanderStolz2025-09-182025-09-182025https://publica.fraunhofer.de/handle/publica/496009This 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.enbook article