TY  - EJOU
AU  - Patil, Meenakshi S. 
AU  - Ghongade, Rajesh B. 
AU  - Dhonde, Hemant B. 

TI  - Innovative Concrete Cube Failure Mode Detection Using Image Processing and Machine Learning for Sustainable Construction Practices
T2  - Journal on Artificial Intelligence

PY  - 2025
VL  - 7
IS  - 1
SN  - 2579-003X

AB  - This study seeks to establish a novel, semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes, significantly contributing to the dependability of concrete quality evaluations. The study utilizes image processing and machine learning (ML) methods, namely object detection models such as YOLOv8 and Convolutional Neural Networks (CNNs), to evaluate images of concrete cubes. These models are trained and validated on an extensive database of annotated images from real-world and laboratory conditions. Preliminary results indicate a good performance in the classification of concrete cube failure modes. The proposed system accurately identifies cracks, determines the severity of damage to structures, indicating the potential to minimize human errors and discrepancies that might occur through the current techniques to detect the failure mode of concrete cubes. The developed system could significantly improve the reliability of concrete cube assessments, reduce resource wastage, and contribute to more sustainable construction practices. By minimizing material costs and errors, this innovation supports the construction industry’s move towards sustainability.
KW  - Concrete cube failure; image processing; machine learning; YOLOv8; CNNs

DO  - 10.32604/jai.2025.069500