Detection and Measurement of Crack Parameters in Large Reinforced
Concrete Structures Using Uavs and Machine Learning Algorithms
Professor:Najeeb Shariff Mohammad
UID: CE01
Evaluating Methods to Determine the Residual Prestress in Aged
Bridges
Professor: Najeeb Shariff Mohammad
UID: CE02
Geomorphological Studies of a River Basin
Professor:V. Jothiprakash
UID: CE03
CE01
Description
The project involves detecting cracks and other defects in
reinforced concrete members using machine learning algorithms
assisted by cameras mounted on UAVs. The students get to work
on image processing algorithms which give vital information on
the crack parameters. An AI-powered engine shall be developed
which can predict the further progression of the defect/crack
with time
Number of students
3
Year of study
Students in their 2nd year (Semester 3), Students in their 3rd
year (Semester 5), Students in their 4th/5th year (Semester
7/9)
CPI
7 and above
Prerequisites
Knowledge on machine learning, python, camera and sensors will
be useful
In this study different methods which are employed to estimate
the residual prestress in existing bridges are studied through
experimental evaluation. The variables obtained from field
measurements are used to determine the member capacities
through user-friendly programs developed as a part of this
study.
Number of students
2
Year of study
Students in their 3rd year (Semester 5), Students in their
4th/5th year (Semester 7/9)
CPI
7 and above
Prerequisites
Interest in coding
Duration
4 months
Learning outcome
-
Weekly time commitment
6 hours
General expectations
-
Assignment
-
Instructions for assignment
-
CE03
Description
1. To download the remote sensing map of a river basin
2. Prepare the DEM, geology, LU/LC map, and fine various
geological parameters. 3. Assess the capability in terms
of runoff generation for a given rainfall.
Number of students
1
Year of study
Students in their 3rd year (Semester 5)
CPI
8 and above
Prerequisites
Should have studied a course in Geology / Remote Sensing
Duration
1 to 2 months
Learning outcome
A journal paper in geo hydrology
Weekly time commitment
8 hours
General expectations
Good communication skills, reading ability of journal papers,
good in remote sensing map preparation.