Probabilistic Inverse Models for Assessing the Predictive Accuracy of Inelastic...
Probabilistic Inverse Models for Assessing the Predictive Accuracy of Inelastic Seismic Numerical Analyses
This research proposal deals with developing probabilistic inverse models for assessing the predictive accuracy of inelastic seismic numerical analyses. Numerical models for predicting the inelastic response of structures in seism...
ver más
¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video
Proyectos interesantes
IDESIGN
Enabling Seismic Design Decision Making under Uncertainty
100K€
Cerrado
CGL2011-23621
NUEVAS AVANCES DE LA GEOFISICA Y LA INGENIERIA APLICADOS A L...
91K€
Cerrado
SHARE
Seismic Hazard Harmonization in Europe
4M€
Cerrado
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
This research proposal deals with developing probabilistic inverse models for assessing the predictive accuracy of inelastic seismic numerical analyses. Numerical models for predicting the inelastic response of structures in seismic loading are biased by so-called epistemic uncertainties that arise from our lack of knowledge: modelling errors, poor comprehension of material constitutive behaviours and of the energy dissipation mechanisms, and so on. Bringing together experts in probabilistic computational mechanics, earthquake engineering, nonlinear material science and mathematical statistics, the research project aims at providing innovative useful numerical tools to researchers, designers and analysts for decision making regarding the seismic risk and structural safety of designing and existing structures. More specifically, research will be oriented toward developing deterministic-probabilistic inverse models to quantify the epistemic uncertainties in inelastic seismic numerical analyses. Such models will allow for computing confidence regions for the quantities of interest. Confidence regions reflect the predictive accuracy of the simulations and provide useful information for defining new research orientations as well as for structural safety and risk assessment.