34th Annual Conference of the International Group for Lean Construction (IGLC 34) - Singapore, Singapore - 2026

https://doi.org/10.24928/2026/0180

AI-based safety monitoring using SKOPIA for preventing accidents in heavy equipment operations at Jragung Dam construction project

Andrianto Widhi Nugroho1, Ayu Nindya Atiekasari2, Satria Maulana Akbar3, Adi Tisna Rayadi4, Natasya Stiefani5 & Bagus Hendri Setyawan6

1Project Manager, Jragung Dam Construction Project, Division of Infrastructure 1, PT Wijaya Karya (Persero) Tbk, Jakarta, Indonesia, [email protected] , orcid.org/0009-0000-6152-9686
2Site Manager of HSE, Jragung Dam Construction Project, Division of Infrastructure 1, PT Wijaya Karya (Persero) Tbk, Jakarta, Indonesia, [email protected] , orcid.org/0009-0008-1324-3841
3Staf of Engineer, Jragung Dam Construction Project, Division of Infrastructure 1, PT Wijaya Karya (Persero) Tbk, Jakarta, Indonesia, [email protected] , orcid.org/0009-0007-6704-8876
4Manager of Quantity Survey, Division of Infrastructure 1, PT Wijaya Karya (Persero) Tbk, Jakarta, Indonesia, [email protected] , orcid.org/0009-0004-3242-9941
5Junior Expert of Risk Management, Division of Infrastructure 1, PT Wijaya Karya (Persero) Tbk, Jakarta, Indonesia, [email protected] , orcid.org/0009-0003-2877-696X
6Staff of Quantity Survey, Division of Infrastructure 1, PT Wijaya Karya (Persero) Tbk, Jakarta, Indonesia, [email protected] , orcid.org/0009-0006-3419-4597

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.

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Reference in APA 7th edition format:

Nugroho, A. W., Atiekasari, A. N., Akbar, S. M., Rayadi, A. T., Stiefani, N. & Setyawan, B. H.. (2026). AI-based safety monitoring using SKOPIA for preventing accidents in heavy equipment operations at Jragung Dam construction project. In Hamzeh, F., Poshdar, M., & Garcia-Lopez,, N. P. (Eds.), Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) (pp. 74–84). https://doi.org/10.24928/2026/0180

Shortened reference for use in IGLC papers:

Nugroho, A. W., Atiekasari, A. N., Akbar, S. M., Rayadi, A. T., Stiefani, N. & Setyawan, B. H.. (2026). AI-based safety monitoring using SKOPIA for preventing accidents in heavy equipment operations at Jragung Dam construction project. IGLC34. https://doi.org/10.24928/2026/0180

 

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