This course provides an introduction to probability and statistics
with a specific focus on applications in structural and geotechnical
engineering. Engineering systems are inherently affected by
multiple sources of uncertainty, including variability in material and
soil properties, randomness in loads, and modeling approximations.
The course aims to equip PhD students with the fundamental tools
required to model, quantify, and propagate uncertainty in engineering
contexts.
The first part of the course introduces probabilistic modeling, including
random variables, common probability distributions used in
engineering, and basic concepts of dependence and uncertainty
propagation. These tools are then employed to formulate and analyze
engineering problems under uncertainty. A central component
of the course is the introduction to structural and geotechnical reliability,
where safety is characterized in probabilistic terms through
limit state functions, probability of failure, and reliability indices.
The second part focuses on statistical methods for the analysis
of experimental data, including parameter estimation, confidence
intervals, hypothesis testing, and regression models. Particular
emphasis is placed on the integration between data-driven approaches
and probabilistic modeling. The course combines theoretical
concepts with engineering-oriented examples, aiming to
provide a coherent framework for uncertainty quantification and
reliability-based analysis in civil engineering.
13, 20, 27 Maggio 2026