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Chen A,Golparvar-Fard M,Kleiner B

SAVES: a safety training augmented virtuality environment for construction hazard recognition and severity identification

Abstract: One of the most challenging aspects of health and safety (H&S) management for construction sites is ensuring that workers can predict, identify, and respond to potential hazardous conditions before they are exposed. While OSHA addresses the need for enforcement of comprehensive H&S training programs, many safety training programs still do not include hazard recognition or systematic preparations for the avoidance of unsafe conditions. From a scientific standpoint, we currently lack the knowledge of discovering the most efficient training styles for safety and also understanding why and how these styles of training can influence the post-training activities. To address these needs, an Augmented Virtuality(AV) training environment named System for Augmented Virtuality Environment Safety (SAVES) was designed and is presented in this paper. SAVES which integrates a Building Information Model (BIM) with photographs of typical energy sources on a jobsite, allows trainees to control and navigate an avatar within such AV environment. Within the AV environment, the user can conduct a set of interactions with the environment and accomplish multiple instruction and task-based training scenarios. These scenarios include detection of ten types of hazard and/or energy sources at three levels of severity. The energy sources which in SAVES are embedded in forms of 3D elements and 2D imagery are designed to elevate the safety awareness of the users, enable them to predict and identify various types of hazards, and assess their level of severity. To fully document the experience of the users, during each exercise, trainees’ choices, time for decision-making and corresponding prevention plan are documented in the system. The complete process of design, development, implementation and results analysis of SAVES is presented.

Keywords: Safety,Training,Virtual Reality,Hazard Recognition


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Series: convr:2013 (browse)
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E Petrinja, V Stankovski & ˇ Turk

Provenance Metadata for Shared Product Model Databases

Abstract: The process of saving metadata committed to track all changes to some data, is known as ""provenance"". In the AEC/FM sector provenance data can be exploited for tracking all interactions of different users between each other and with parts of data. For a particular application, we need to consider which metadata are essential for future queries and who is going to use these. The IFC standard already contains some provenance concepts in its entity structure. We have considered these provenance concepts to build a provenance tracking software. The provenance ontology server was developed by using the OWL ontology language, already available IFC concepts and some complementary concepts that we had to include for the sake of generality of our implementation. The developed prototype allows us to upload an IFC file to a web enabled service that parses it and saves instances of retrieved concepts for later queries, according to the ontology we have defined.""



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Series: w78:2005 (browse)
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Permission to reproduce these papers has been graciously provided by the Technische Universität Dresden.

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