@inproceedings{Nugroho2026_2489,
  author = {Nugroho, Andrianto Widhi and Atiekasari, Ayu Nindya and Akbar, Satria Maulana and Rayadi, Adi Tisna and Stiefani, Natasya and Setyawan, Bagus Hendri},
  editor = {Hamzeh, Farook and Poshdar, Mani and Garcia-Lopez,, Nelly P. and Gan, Vincent},
  title = {AI-based safety monitoring using SKOPIA for preventing accidents in heavy equipment operations at Jragung Dam construction project},
  booktitle = {Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)},
  year = {2026},
  pages = {74--84},
  address = {Singapore, Singapore},
  issn = {2789-0015},
  doi = {10.24928/2026/0180},
  url = {https://www.iglc.net/papers/details/2489},
  abstract = {Construction projects involving heavy equipment operations present significant safety risks due to close interactions between workers and heavy equipment, limited visibility, and reliance on manual supervision. Heavy equipment-related accidents remain one of the leading causes of fatal incidents in infrastructure projects. This study examines the implementation of an artificial intelligence (AI)-based safety monitoring system, SKOPIA (Smart Kit & Observation Platform for Industrial Awareness), to prevent heavy equipment accidents in a dam construction project. A case study approach was adopted at the Jragung Dam Construction Project Package V in Indonesia. The system utilizes computer vision, machine learning, and real-time alert mechanisms to monitor worker and equipment movements within hazardous zones. Data were collected through field observations, near-miss records, and operational comparisons before and after implementation. The signalman observation period is January – May 2025 while the SKOPIA observation period is June – September 2025. Observations are carried out every day when work is carried out with the Transport Lift Aircraft. The findings indicate that AI-based monitoring enhances early hazard detection, reduces response time, and minimizes dependency on manual signalmen. This study contributes empirical evidence on integrating AI-enabled monitoring into proactive safety management, supporting lean construction principles and risk-based accident prevention in large-scale infrastructure projects.},
  keywords = {AI, safety, lean construction, heavy equipment operations, dam projects.},
}