9791221502893_18.pdf

Tower and Mobile Cranes are some of the most commonly used heavy equipment in all construction sites, and any crane failures could lead to significant human and monetary losses. Moreover, rigging configuration determination is a critical task that requires the rigging crew to have significant experi...

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Γλώσσα:English
Έκδοση: Firenze University Press 2024
Διαθέσιμο Online:https://books.fupress.com/doi/capitoli/979-12-215-0289-3_18
id oapen-20.500.12657-89114
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spelling oapen-20.500.12657-891142024-04-03T02:23:42Z Chapter Safety Training for Rigging Using Virtual Reality Lemouchi, Rafik Assaf, Mohamed Al-Hussein, Mohamed Boutouhami, Khaoula Bouferguene, Ahmed Crane Operations Lift Planning Rigging Safety Training Virtual Reality thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization Tower and Mobile Cranes are some of the most commonly used heavy equipment in all construction sites, and any crane failures could lead to significant human and monetary losses. Moreover, rigging configuration determination is a critical task that requires the rigging crew to have significant experience and knowledge of various failure modes that can be encountered when performing lifting operations. However, despite the criticality of training riggers, there has yet to be a comprehensive tool used to train and guide inexperienced riggers, and hence, more practical tools are needed. This paper proposes a framework for using Virtual reality (VR) and simulation to train riggers to identify the optimal rigging configurations based on the lift type and the external conditions. Through 3D modeling, the critical components of the rigging system are modeled to accurately simulate the rigging system and their performance when faced with critical loading scenarios. The developed framework is expected to allow inexperienced riggers to identify critical failure modes and enhance construction operations' overall safety performance and productivity. Furthermore, several scenarios are assessed based on historical evidence for rigging configuration failures, and the efficiency of the training tool is assessed through real-life scenarios and tests 2024-04-02T15:46:59Z 2024-04-02T15:46:59Z 2023 chapter ONIX_20240402_9791221502893_83 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89114 eng Proceedings e report application/pdf n/a 9791221502893_18.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_18 Firenze University Press 10.36253/979-12-215-0289-3.18 10.36253/979-12-215-0289-3.18 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 11 Florence open access
institution OAPEN
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language English
description Tower and Mobile Cranes are some of the most commonly used heavy equipment in all construction sites, and any crane failures could lead to significant human and monetary losses. Moreover, rigging configuration determination is a critical task that requires the rigging crew to have significant experience and knowledge of various failure modes that can be encountered when performing lifting operations. However, despite the criticality of training riggers, there has yet to be a comprehensive tool used to train and guide inexperienced riggers, and hence, more practical tools are needed. This paper proposes a framework for using Virtual reality (VR) and simulation to train riggers to identify the optimal rigging configurations based on the lift type and the external conditions. Through 3D modeling, the critical components of the rigging system are modeled to accurately simulate the rigging system and their performance when faced with critical loading scenarios. The developed framework is expected to allow inexperienced riggers to identify critical failure modes and enhance construction operations' overall safety performance and productivity. Furthermore, several scenarios are assessed based on historical evidence for rigging configuration failures, and the efficiency of the training tool is assessed through real-life scenarios and tests
title 9791221502893_18.pdf
spellingShingle 9791221502893_18.pdf
title_short 9791221502893_18.pdf
title_full 9791221502893_18.pdf
title_fullStr 9791221502893_18.pdf
title_full_unstemmed 9791221502893_18.pdf
title_sort 9791221502893_18.pdf
publisher Firenze University Press
publishDate 2024
url https://books.fupress.com/doi/capitoli/979-12-215-0289-3_18
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