The objective of the course is to demonstrate different AI/ML methods in Civil Engineering problems with specific focus on convolution neural networks, image processing and support vector machine. In all experiments, the students will gather the data through real-life experimentation in the laboratory or in the field; followed by application of AI/ML routines for predicting outcomes..
The laboratory course will include 8 hands-on projects using sensors, which the students will perform in groups, by turn, to cover the entire semester. The data acquisition may be done using an Arduino (microcontroller board) connected to a laptop, or using an Arduino with a Raspberry Pi (microprocessor board) and a digital display unit.
The course focuses on simulating, analyzing, and optimizing large-scale traffic networks under dynamic conditions, often utilizing data-driven approaches and advanced simulation tools. The topics covered in this course include MFD, Kinematic wave models, FD, Numerical analysis, Continuum model and its solutions, etc.