IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

HYBRID REMOTE VIDEO AUDITING FRAMEWORK FOR PROACTIVE CONSTRUCTION SITE SAFETY MANAGEMENT

G Sandya Dr. Vishal Singh

Dr. N. R. Dakshina Murthy

Department of Civil Engineering/ CBIT/ Hyderabad, India

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

The construction industry ranks among the most hazardous sectors globally, accounting for approximately 17–20% of all occupational fatalities while employing only 7% of the global workforce. Conventional safety auditing methods characterized by periodic inspections, limited spatial coverage, and reactive orientation—fail to achieve the continuous hazard detection necessary to substantially reduce construction accident rates. This paper presents and evaluates a Hybrid Remote Video Auditing (HRVA) framework designed to transform safety monitoring on construction sites through the integration of three complementary components: (1) continuous high-definition video surveillance across identified high-risk zones, (2) AI-assisted automated hazard detection using YOLOv10 and Vision Transformer (ViT) architectures, and (3) expert-led remote audit processes incorporating OSHA 29 CFR 1926 and BIS-aligned checklists. The framework was evaluated through an eight-week audit cycle on an active commercial building construction project (G+12 floors), yielding 183 identified hazards across 726 reviewed footage clips. Fall protection violations constituted 42.1% of observations, followed by material handling (31.1%) and electrical safety (26.8%). The implementation of structured Corrective and Preventive Action (CAPA) protocols produced a site-wide compliance improvement of 25.8 percentage points over the study period. Comparative analysis confirmed that the HRVA framework achieved a 239% higher hazard detection rate, greater than 90% reduction in time-to-detection, and 55–70% lower cost per hazard detected compared to conventional inspection-based auditing. These findings demonstrate the scalability, regulatory alignment, and practical effectiveness of the HRVA framework as a proactive, data-driven construction safety governance model for both developing and developed economies.

Keywords: Hybrid Remote Video Auditing; Construction Site Safety; OSHA Compliance; Computer Vision; Deep Learning; YOLOv10; CAPA; Risk Classification; Proactive Safety Management; BIS Standards; BOCW Act.

How to Cite this Paper

Sandya, G. & Singh, V. (2026). Hybrid Remote Video Auditing Framework for Proactive Construction Site Safety Management. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.030

Sandya, G, and Vishal Singh. "Hybrid Remote Video Auditing Framework for Proactive Construction Site Safety Management." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.030.

Sandya, G, and Vishal Singh. "Hybrid Remote Video Auditing Framework for Proactive Construction Site Safety Management." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.030.

Search & Index

References

[1] Hinze, J., & Wilson, G. (2000). Behavior-based safety: Understanding the role of observation in preventing incidents. Journal of Construction Engineering and Management, 126(1),35–41. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:1(35)

[2] Song, L., Wang, Y., Li, H., & Chen, Z. (2025). A drone-based model for real-time construction site safety detection using GS-LinYOLOv10. Alexandria Engineering Journal, 74, 104839. https://doi.org/10.1016/j.aej.2025.01.021

[3] Li, H., Fang, Q., Luo, X., & Ding, L. (2025). AI-powered safety monitoring system for automated hazard detection on construction sites. Automation in Construction, 160, 106353. https://doi.org/10.1016/j.autcon.2025.106353

[4] Zhang, Y., Liu, P., Chen, F., & Li, X. (2025). Integrating AI and IoT for predictive safety analytics in construction energy systems. Applied Energy,369,

https://doi.org/10.1016/j.apenergy.2025.126836

[5] Ahmed, K., Park, D., & Lee, J. (2025). Vision-based multi-environment analysis for safety hazard recognition. Measurement: Advances in Engineering Design, 12, 100011. https://doi.org/10.1016/j.meadig.2025.100011

[6] Xu, H., Teizer, J., & Li, H. (2025). Human-AI collaboration in hybrid safety auditing systems for construction risk management. Automation in Construction, https://doi.org/10.1016/j.autcon.2025.106482

[7] Huang, P., Liu, J., & Zhang, L. (2025). Application of explainable AI in automated safety auditing for civil projects. MethodsX, 12, 103508. https://doi.org/10.1016/j.mex.2025.103508

[8] Zhao, D., Li, X., & Zhang, Y. (2024). Worker detection and risk assessment via multi-modal visual sensors in high-rise construction. Automation in Construction, 152, 105457. https://doi.org/10.1016/j.autcon.2024.105457

[9] Fang, D., Wu, C., & Luo, X. (2025). Adaptive video analytics for intelligent safety assessment in dynamic construction environments. Advanced Engineering Informatics, 60, 103305. https://doi.org/10.1016/j.aei.2025.103305

[10] Hong, K., & Teizer, J. (2025). Digital construction site layout planning and real-time trajectory analysis for proactive safety monitoring. Automation in Construction. https://doi.org/10.1016/j.autcon.2025.106353

Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: Jun 04 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Scroll to Top