The goal of this graduate course is to provide students with theoretical and practical applications of statistical and visual pattern recognition for data mining in built environment applications. Students learn theoretical concepts, use state-of-the-art programing environments for implementation, and gain experience in working with data using publicly available data sets. Furthermore, state-of-the-art machine learning and data visualization tools are introduced. The applications range across different disciplines in civil engineering.  

This graduate course provides knowledge and hands-on experience (through a number of case studies) for managing data through DBMS, data visualization, and data mining platforms and introduces the hardware and software fundamentals for developing and deployment of data acquisition systems for common applications in infrastructure management.

This graduate course provides knowledge as well as hands-on/minds-on experience for virtual modeling of infrastructure systems through the application of Building and Civil Information Modeling. Through active learning, students learn how to use information modeling processes from reality capturing, model authoring, specialty project management, virtual collaborations, and developing immersive virtual environments.

This undergraduate course introduces computer applications in civil and environmental engineering and integrates design, data management, computer programming, and problem-solving skills with computer tools and techniques. Topics include systems analysis, optimization, database management, computer programming, and data structures. 

This undergraduate course provides knowledge and hands-on experience on Building Information Modeling and the integrated practices associated with BIM technologies.