A course for introducing new CEE students to the fields of civil and environmental engineering framed within cross-cutting topic areas

Flow of liquids with free surfaces in artificial and natural channels. Analysis of flow resistance. Computation of gradually varied flow profiles. Flow through transitions, spillways, bridges, culverts. Analysis of unsteady flow.

Data analytics applications for CEE, accessing and assessing datasets, tools focused on machine learning, advantages and limitations of data driven approaches, communication data-driven assessments.

A course for introducing new CEE students to the fields of civil and environmental engineering framed within cross-cutting topic areas

Flow of liquids with free surfaces in artificial and natural channels. Analysis of flow resistance. Computation of gradually varied flow profiles. Flow through transitions, spillways, bridges, culverts. Analysis of unsteady flow.

Applications of fluid mechanics to engineering and natural systems including fluid drag, open channel flow, turbomachinery, and environmental hydraulics; laboratory experiments; computational hydraulics.

A course for introducing new CEE students to the fields of civil and environmental engineering framed within cross-cutting topic areas

Generalization of finite element concepts; Galerkin-weighted residual and variational approaches; mixed and hybrid finite element formulations, applications, transient dynamic analysis; software implementation.

This course is recommended for those interested in data science/analytics or those who may work in transportation. This course will 1) introduce the history and fundamentals of transportation safety analysis, 2) teach the fundamental techniques for data analytics, 3) discuss crash data analysis and safety countermeasure prioritization, 4) teach students how to utilize predictive and systemic crash analysis, and 5) explore emerging technologies and emerging data sources that can be utilized in roadway safety.

Data analytics applications for CEE, accessing and assessing datasets, tools focused on machine learning, advantages and limitations of data driven approaches, communication data-driven assessments.