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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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Jiayu Chen, Fei Dai and Zejun Chen

Assessing Construction Workers' Vigilance Level Through Wearable Electroencephalography System

Abstract: Construction industry requires constant caution on construction labors when they expose to hazardous environments. Although received fundamental safety training, construction workers tend to insensitive to hazards because of their long time exposures to risks. Many construction workers take unsafe behavior when they wrongly estimated the potential risks. Therefore, the discrepancy between the environment risks and workers' perceived risks is the major cause of unsafe behaviors. However, current assessing approaches are subjective and post-hoc. In this paper, we proposed a wearable Electroencephalography (EEG) system to quantitatively and objectively assess the construction workersÕ vigilance level for perceived risks. With such data acquisition approach, the construction workers' risk perception can be further understood and guide the safety training programs in future.

Keywords: Construction Safety, EEG, Vigilance, Wearable

DOI: https://doi.org/10.24928/JC3-2017/0030

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Shelbourn M, Aouad G, Hoxley M

Integrating a case-based-reasoning application with virtual reality to portray building pathology movies

Abstract: "Building defects are notoriously difficult to identify, even by the most experienced surveyor. Traditional training methods of identifying defects in buildings involved the physical visiting of a property. This has become more difficult as insurance and organisational issues have made this practice unsafe and costly. Methods of training surveyors are being brought up to date with the introduction of desktop technology to provide learners with a rich set of learning resources in a much easier format. Defects generated from real life cases using digital cameras are stored in a format that can be then used to train inexperienced surveyors. The identification of the types of building defects is done using case-based-reasoning technologies. This paper describes a system where a case-based-reasoning application is linked to virtual reality software. The architecture of the system is described and the feedback from surveyors is analysed. The methodology is based on collecting data from industrialists, which is stored in the case-based system and simulated in the virtual reality environment. An iterative approach is used to develop the system and validate it."

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Full text: content.pdf (1,047,063 bytes) (available to registered users only)

Series: w78:2000 (browse)
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Class: class.represent (0.020411) class.deployment (0.020279) class.education (0.010704)
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Permission to reproduce these documents have been graciously provided by Icelandic Building Research Institute. The assistance of the editor, Mr. Gudni Gudnason, is gratefully appreciated


Srinivas S. Pulugurtha and Vinay K. Vanapalli

Using GIS To Assist Decision Makers In Identifying Unsafe Bus-Stops

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Full text: content.pdf (684,648 bytes) (available to registered users only)

Series: w78:2006 (browse)
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Zhang S,Teizer J,Perez E,McDonald M

Automated safety-in-design rule-checking for capital facility projects

Abstract: Safety-in-design (SID) reviews are mandatory for capital facility projects because they eliminate hazards before activities in the construction, operation, and maintenance phases take place. Existing SID review processes which many large corporations have in place, however, still rely mainly on manual input and judgment of experienced safety experts. Often very skilled humans make decisions based upon paper-based drawings or three-dimensional visualization models. As such, tasks in safety-in-design review sessions remain to be manual and thus are very much time-consuming, expensive. Furthermore, if not all hazards are detected and mitigated, they can be potentially error-prone. Unsafe design ultimately exposes workers at risk as it provides an unsafe work environment. It can also become very costly if unsafe design is detected outside of the design and construction planning phases of a capital facility project. The objective of this work was to develop a safety code compliance checking technology that does not replace human judgment, but supports human decision making of safety experts, designers, engineers, and field staff. The developed work applies novel safety code compliance checking algorithms on intelligent information models which are prepared during design and construction planning. The initial scope of the developed algorithms is limited to check for safe work access and egress requirements in existing information models. As existing safety rules and best practices are embedded in the developed code compliance checking system, they can be automatically executed on information models which exist for every capital facility project. A case study is presented to illustrate its practical implementation for an off-shore oil platform. Results show that the developed system generates automated reports that list the safety violations and furthermore, along with visual screenshots of the unsafe object in the information model, indicate the process of how these issues can be mitigated based upon established best safety practices. The significance of human-assisted decision-making in SID reviews and its potential to lead to safer designs early in a project is explained.

Keywords: Capital facility projects,design for safety,design reviews,information modeling,rule checking,3D model,safety-in-design

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