Abstract: Construction safety is a critical global concern due to the high-risk environment faced by workers, with accidents often leading to serious injuries and fatalities. To enhance construction management, this study proposes a scalable deep-learning model for real-time compliance monitoring of safety regulations. The research gap addressed is the lack of real-time, scalable AI solutions for safety compliance monitoring in dynamic construction environments. The YOLOv11n model was trained and evaluated to identify and track the use of safety helmets and vests in extreme dynamic environments, ensuring timely detection of non-compliance. It is hypothesized that the YOLOv11n model will outperform baseline models in accuracy and real-time monitoring speed. The YOLOv11n model outperformed other baseline models, with precision, recall, and mean average precision scores of 89.5%, 85%, and 91.6%, respectively, and a real-time processing speed of 71.68 FPS. Its lightweight size and performance make it suitable for deployment. Integrated with a person-detection framework, the system provides real-time desktop alerts for safety violations, enhancing safety compliance. These findings contribute to construction automation by advancing scalable AI-driven solutions for proactive safety compliance, reducing accidents, and improving operational efficiency on construction sites.

Aisha Hassan, Ali H. Hassan, Yasmin Christensen and Hussain Alsadiq. “Advanced AI-Driven Safety Compliance Monitoring in Dynamic Construction Environment”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.6 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160641

@article{Hassan2025,
title = {Advanced AI-Driven Safety Compliance Monitoring in Dynamic Construction Environment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160641},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160641},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Aisha Hassan and Ali H. Hassan and Yasmin Christensen and Hussain Alsadiq}
}