Evaluation and Management of Rail Transit Project On-site Safety Status Based on the Daily Behavioral Data of Construction Workers
Year: 2025
Type: article
Abstract: [Objective] As rail transit projects feature a large number of participants and complex influencing factors, safety management is always a challenge. The application of digital technology provides a new idea for improving the safety level of the construction site of rail transit projects. Therefore, digital technology is used to collect the daily behavioral data of construction workers, and based on this, the on-site safety management of rail transit projects is studied. [Method] The data collection system for rail transit projects is introduced in terms of data collection, processing, and analysis methods. With the collected real-time data, such as the attendance and on-site behaviors of construction workers in rail transit projects, the linear regression method is applied to analyze the relationship between the daily behavioral data of construction workers and the construction safety management. Furthermore, the system clustering method and the multi-dimensional scaling method are used to analyze the safety status of rail transit projects. The effectiveness of this method is verified through the practical application case, and feasible safety management suggestions are given according to the situation of the case project. [Result & Conclusion] The daily behaviors of on-site construction workers are directly related to the safe production status of rail transit projects. Safety inspections, safety education and training, etc. are the main factors affecting the safety status of rail transit projects, and managing the on-site behaviors of construction workers can improve the safe production status of rail transit projects.
Authors: YIN Dawei, HAN Xiangyu, ZHU Mei, XIAO Ximeng, HU Wenfa
Institution: Tongji University
Language: English
Subfield: Radiological and Ultrasound Technology
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6924840130
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