Automatic Cave-in Safety Risk Identification in Construction Excavation

Safety continues to be among the top issues in the construction industry after experiencing 738 fatalities in 2011. Among all construction operations, excavation is one of the most hazardous because of its inherent hazards with possible cave-ins, contact with objects and equipment, bad air, and so on. Up till today, most safety inspectors are still inspecting the site manually, making the inspection timeconsuming and error-prone. This paper presents a method that automatically identifies cave-in safety risks in construction excavation. It first extracts relevant safety rules from OHSA standards and industrial best practices. Then, it collects a set of point cloud data of a construction site under excavation using laser scanning, registering and cleaning the point cloud data afterwards. Finally, it develops an automated identification algorithm based on those rules and applies the algorithm onto the data to identify potential cave-in risks by analyzing geometrical properties. An experimental trial is also conducted in this paper and results show that the method successfully identifies those cave-in risks. The presented method can actively monitor the fast changing situations of construction sites under excavation and help inspectors and project managers make good safety decisions, preventing accidents and fatalities.

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