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

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.