UROP Project

Image Analysis of Composite Materials (i.e., Asphalt Concrete)

Image Analysis, Composites, Fatigue Cracking, Asphalt Concrete

Research Mentor: Dr. Michael Elwardany, He/him/his
Department, College, Affiliation: Civil and Environmental Engineering, FAMU-FSU College of Engineering
Contact Email: melwardany@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Civil Engineering, Computer Engineering, Computer Science, and other engineering majors
Project Location: FAMU-FSU College of Engineering
Research Assistant Transportation Required: FSU Buss Services
Remote or In-person: In-person
Approximate Weekly Hours: 8 hours a week, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

With the growing use of mechanistic-empirical models in pavement design, performance testing of asphalt materials has become increasingly important. These models rely on accurate performance data—particularly fatigue cracking behavior—to inform the structural design of pavements. However, traditional performance tests (e.g., uniaxial cyclic fatigue test) are time-consuming, require specialized equipment, and involve substantial material and labor costs.

This project explores an innovative, non-destructive approach using image analysis of asphalt mixtures to predict fatigue performance. By analyzing key characteristics such as aggregate gradation, orientation, and structure from specimen images, the goal is to establish correlations between visual aggregate features and fatigue cracking performance indicators. This image-based method has the potential to reduce reliance on costly mechanical testing, saving both time and materials for practitioners and accelerating the pavement design process.

During the UROP term, the student will assist in capturing and processing high-resolution images of asphalt mixture specimens, quantifying aggregate structure using image analysis software, and performing statistical or machine learning-based correlation with known fatigue performance data.

Research Tasks: Data collection (Capturing images of Samples)
Literature review
Data analysis
Building software for analysis
Documentation and reporting of outcomes


Skills that research assistant(s) may need: Required:
Basic research skills
Basic data analysis skills
Programming skills
Recommended:
Coding Skills