Fall from heights is one of the most lethal incidents in the construction industry. To mitigate the risk of fall hazards, safety managers need to continuously monitor jobsite conditions to identify potential hazardous situations. While, previous studies develop algorithms to automatically analyze a building to detect fall hazards; their application is limited as the real-time data cannot be collected during the dynamic nature of construction processes. One of the emerging technologies that can address this limitation is an unmanned aerial system (UAS). UASs can provide several advantages for safety managers as they can move faster than humans, reach inaccessible areas of jobsites, and can be equipped with video cameras, wireless sensors, radars, or different communication hardware to transfer real-time data. This research study aims to provide a proof of concept of potential application of UASs in developing an automated aerial system to collect, identify, and assess fall hazards in construction projects. The objective of the study is achieved by collecting real-time video feed of the construction jobsite using UASs, generating point cloud data using image/videogrammetric techniques, and developing an algorithm to process spatial point cloud data to identify fall hazards. The algorithm would ultimately search the point cloud data to detect location of the current guardrails and openings and then checks if they are safety-approved. This paper proposes a workflow for identifying fall hazard using unmanned aerial systems and later will present how some parts of the workflow was implemented and tested in a pilot study.
展开▼
