A study on crane accidents: investigating the role of hand signals in construction site communication
DOI:
https://doi.org/10.7770/safer-V12N-art787Keywords:
Python, Machine Learning, Jupyter Notebook, Hand-Signal Detection, Construction Industry, Image Training, Computer Vision, TensorFlowAbstract
Construction industry is one of the largest industries in the global economy. In today's construction industry, where time and profit margins are crucial, modern construction equipments plays a crucial role. Hand signals are frequently used by construction workers on the job site to communicate, as they are easy and effective. However, there can be a risk of miscommunication or misinterpretation with hand signals, resulting in mistakes and potential hazards on the construction site. This paper presents a comprehensive study on crane accidents in construction sites, with a particular focus on the communication between crane operators and signal men through hand signals. The study begins with a literature review on the causes and effects of crane accidents, statistics and incidents on construction sites. The study then investigates the current practices of hand signals on construction sites and highlights the associated safety concerns. The study concludes that the misinterpretation of hand signals between the signal man and the crane operator is a significant contributor to crane accidents. To address this issue, the paper recommends the development of a real-time hand signal detection system for material handling using machine learning and computer vision. The proposed solution will reduce the strain on signal men and improve communication between the signal man and the crane operator, thereby enhancing safety in construction sites.
Keywords: Safety Study, Crane Accidents, Construction Sites, Human Error, Hand Signals, Material Handling, Construction Safety, Signal Man
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