Course content

Introduction to decision under uncertainty by the some motivating examples that will be revisited throughout the course, e.g.: optimal design of a structural component exposed to a time variable load, design of a rock fall gallery, design of a protection structure against terrorist attack, design of a component exposed to fatigue loading, maintenance and repair of that component, design assisted by laboratory tests.\\Advanced review on probability theory and statistics.\\Probabilistic Models (general concepts, but also specifically probabilistic representation of e.g. material resistance, material deterioration, loads, natural hazards, malevolence, accidents, human error…).\\Uncertainty propagation (functions of random variables, Monte Carlo + variance reducing methods, structural reliability methods, sensitivity analysis…).\\Learning models (Maximum Likelihood, Bayesian parameter estimation, …).\\Bayesian Networks and Influence diagrams.\\Bayesian Decision Theory.\\Broader topics: Risk acceptance criteria, representation of Robustness, Resilience and Sustainability, etc.\\

Learning outcome

The student learns, e.g.:to relate practical engineering problems to formal decision theory.to identify systems that represent the engineering decision problems.to develop probabilistic models for the system representation.to apply methods of structural reliability.to assess how save is safe enough.

Learning methods and activities

Lectures and selfstudy

Compulsory assignments

• Project

Recommended previous knowledge

TMA4255 - Applied Statistics or similar.

Required previous knowledge

Students are expected to have a Civil Engineering background.

Course materials

Lecture Notes, recommended books