Risques physiques, mécaniques ou de sécurité
Aristizábal Pla, G. et Beschorner, K. E. (2025). Automated traction analysis for worn shoes: Combining novel imaging technology, convolutional neural networks, and hydrodynamic modelling to predict friction performance. Footwear Science. https://doi.org/10.1080/19424280.2025.2603436
Jin, H., Xu, Z., Xu, Z., Li, N. et Goodrum, P. M. (2026). Computer vision-assisted multi-objective spatial optimization of fall protection systems in construction: Integrating hazard zone modeling and posture detection. Developments in the Built Environment, 25, article 100839. https://doi.org/10.1016/j.dibe.2025.100839 
Mohy, A. A., Bassioni, H. A., Elgendi, E. O. et Hassan, T. M. (2025). Deep learning enabled computer vision model for automated safety compliance in construction environments. Journal of Information Technology in Construction, 30, 1398-1430. https://doi.org/10.36680/j.itcon.2025.057
Nguyen, T., Elelu, K., Le, T. et Le, C. (2026). Enhancing auditory safety warnings in highway construction zones with loud noise using generative artificial intelligence. Journal of Construction Engineering and Management, 152(3), article 04025283. https://doi.org/10.1061/JCEMD4.COENG-16495
Sujit, K., Indhumathi, K., Mohan, G., Jose, N. N., Ramudu, K., Abdennaji, T. S., . . . Fissha, Y. (2025). Real-time construction safety monitoring using a drone based deep hybrid attention model. Scientific Reports, 16(1), article 1812. https://doi.org/10.1038/s41598-025-31392-5
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Centre canadien d'hygiène et de sécurité au travail. (2025). Intelligence artificielle (IA). CCHST. 