Automated safety risk assessment for crane operations using cascade learning

Abstract

Construction machinery enhances productivity and ensures project timelines. However, equipment failure poses significant risks, including injuries, fatalities, and financial losses. Traditional safety assessments rely on manual reporting and are prone to errors, delays, and inconsistencies. This paper introduced a cascade learning technique for automated safety risk assessment in crane operations, ensuring reliable, accurate, and adaptable evaluations. The cascade model detects cranes, classifies safety statuses and activities, and computes risk values using confidence scores and impact factors. A risk threshold of 0.52 triggers real-time alerts for intervention. Video-feed analysis supports continuous monitoring and documentation. Expert validation confirmed the practicality of the risk-quantification models. The model achieved 92.10 % precision in crane detection, 99.25 % accuracy in safety classification, and 99.47 % accuracy in activity classification, with an inference time of 0.70 s. This approach enhances Smart Site Safety System (4S) technologies, automates safety assessments, and contributes to improved construction safety standards.