Using Digital Twin Technology to Conduct Dynamic Simulation of Industry-Education Integration

Author:

Shlash Mohammad Anber AbraheemORCID,Al- Daoud Khaleel IbrahimORCID,Al Oraini BadreaORCID,Shelash Mohammad Suleiman IbrahimORCID,Vasudevan AsokanORCID,Zhang JinORCID,Hunitie Mohammad Faleh AhmmadORCID

Abstract

The high accident rate in the construction industry has a major impact on how well projects turn out. Despite substantial investments in safety planning and supervision, there has been a marked increase in the construction industry's accident rate compared to other sectors. Serious games based on VR have recently been used in the study, suggesting that workers are now more safety conscious. However, these situations need many resources to create and are not always realistic. Hence this paper, Digital Twin-based Construction Safety Training Framework (DT-CSTF) with Artificial Intelligence (AI), has been proposed to monitor employees' emotional, mental, and physical well-being in real-time. The report sheds light on the significance of DT technology and its function in Industry 5.0. Using the Unity game engine, the proposed DT-CSTF creates a virtual reality-based training environment (VRTE) prototype that incorporates BIM, construction timetables, and safety requirements. Following this, the suggested structure enables gathering user data about risks and providing tailored feedback. Automated virtual reality game training scenarios are created using data given by digital twins on project intent, project status, safety requirements, and history. Both improved digital twins and periodic construction safety monitoring are anticipated to reap the benefits of dynamic virtual reality training. The proposed management system offers effectiveness of VR-based security training, cost-benefit analysis, monitoring,employee behaviour, safety education values are obtained by the ratio of 96,90 %, 98,33 %, 99,25 %, 95,91 %, 98,66 % respectively

Publisher

Salud, Ciencia y Tecnologia

Reference20 articles.

1. 1. Perišić, B., & Perišić, A. The Foundations for the Future Innovation Ecosystem-A Digital Twins Framework Approach. PaKSoM 2022, 27, pp. 27.

2. 2. Chakraborty, S., & Adhikari, S. Machine learning based digital twin for dynamical systems with multiple time-scales. Computers & Structures, 243, pp. 106410.

3. 3. Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. Digital twin modeling. Journal of Manufacturing Systems, 64, pp. 372-389.

4. 4. Scheibmeir, J. Quality Attributes of Digital Twins (Doctoral dissertation, Colorado State University).

5. 5. Yoon, S. Virtual Building Models in Built Environments. Developments in the Built Environment, 100453.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3