Undergraduate Courses

Probability Concepts and Civil Engineering (CE 119)

Introduces students to elements of probability theory (notion of random variable, PDF/CDF, moments, well-known distributions and theorems (CLT)) and statistical data analysis (maximum likelihood estimation, confidence intervals, hypothesis testing, regression). Intro to coding with Python (open and visualize a dataset, create functions to perform statistical analysis routines and use Python packages numpy, scipy, scikit-learn).

Offered every Fall semester.

Graduate Courses

Uncertainty Quantification Concepts for Civil and Environmental Engineering (CE 599)

Introduces students to various concepts in uncertainty quantification, starting from basics of probability theory (random events, descriptors of univariate and multivariate random variables, well-known distributions and theorems (CLT, Bayes' theorem)), and moving forward with an intro to random processes, techniques for forward propagation of uncertainties (Monte Carlo simulation, perturbation methods) and probabilistic inverse modeling (frequentist and Bayesian paradigms). Last lecture opens up on research UQ fields such as sensitivity analysis or surrogate modeling.

Grading includes homeworks, in-class exam and a project (can be designed by the student to meet their research needs).

Offered every Fall.

Structural Identification and Health Monitoring (CE 599 - coming soon)

Will introduce students to the field of structural health monitoring, with a focus on vibration-based techniques. Subjects covered:

Introduction and core concepts: Hierarchy of damage identification (from identification to localization to prognosis). Importance of uncertainty quantification. Data-based vs. model-based methods.

Structural Identification: Short review of dynamics, time vs. frequency domain. Different techniques for structural identification (modal identification, Kalman filtering).

Machine learning for SHM: SHM as a statistical pattern recognition, unsupervised learning and outlier detection methods. Recent advances in SHM including deep learning for vision-based SHM.

Will be first offered in Spring 2023