In structural engineering, model updating is often used for non-destructive damage
assessment: by calibrating stiffness parameters of finite element models based on …

During the past decades, significant efforts have been dedicated to develop reliable
methods in structural health monitoring. The health assessment for the target structure of …

It is desirable that nonlinear dynamic analyses for structural fragility assessment are
performed using unscaled ground motions. The widespread use of a simple dynamic …

Probabilistic uncertainty and imprecision in structural parameters and in environmental
conditions and loads are challenging phenomena in engineering analyses. They require …

Quantifying the value of the information extracted from a structural health monitoring (SHM)
system is an important step towards convincing decision makers to implement these …

Probability logic with Bayesian updating provides a rigorous framework to quantify modeling
uncertainty and perform system identification. It uses probability as a multi‐valued …

Bayesian methods are a powerful tool in many areas of science and engineering, especially
statistical physics, medical sciences, electrical engineering, and information sciences. They …

In a full Bayesian probabilistic framework for “robust” system identification, structural
response predictions and performance reliability are updated using structural test data 𝒟 by …

Bayesian updating is a powerful method to learn and calibrate models with data and
observations. Because of the difficulties involved in computing the high-dimensional …

Cloud Analysis is based on simple regression in the logarithmic space of structural response
versus seismic intensity for a set of registered records. A Bayesian take on the Cloud …