Research Overview

This project utilizes advanced machine learning techniques to detect cracks and structural anomalies in materials under stress. By identifying early-stage cracks, monitoring their propagation, and analyzing their orientation, we aim to improve the durability and reliability of materials commonly used in construction and engineering.

Early detection of these defects enables proactive maintenance, reducing the risk of structural failures and ensuring long-term safety and performance. Through automated analysis, this approach enhances efficiency in material assessment, contributing to more resilient infrastructure and sustainable engineering practices.