AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (3.1 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Selecting deconstruction processes using virtual reality comparisons

Yinghui ZhaoaGabriel Earlea( )Yun-Ha JungaCarl HaasaSriram Narasimhanb
Department of Civil and Environmental Engineering, University of Waterloo, Waterloo N2L 3G1, Canada
Department of Civil and Environmental Engineering, University of California Los Angeles, Los Angeles 90095, USA
Show Author Information

Abstract

Grand challenges such as achieving sustainability goals and managing aging infrastructure are creating an unprecedented demand for deconstruction. However, deconstructing aging infrastructure is inherently risky, repetitive, and costly, necessitating effective project planning. Virtual reality (VR) technology offers the potential for planners to improve deconstruction project risk, time, safety, and cost. We propose that planners leverage VR early in the planning phase to compare alternative feasible tools and processes. Planners can create low-fidelity models of various deconstruction process alternatives and collect metrics on their suitability with VR-capable three-dimensional (3D) game engines. We present and formalize a methodology for conducting comparisons of candidate deconstruction processes by modeling candidates in VR, conducting trials, and collecting analytics data from the VR engine. We present a case study that demonstrates our approach to a cutting and waste packing process for nuclear power plant (NPP) decommissioning. As a novel contribution in this paper, we show that VR simulations can efficiently produce detailed insights useful for critically analyzing and comparing deconstruction process alternatives.

References

[1]

B. Sanchez, C. Rausch, C. Haas. Deconstruction programming for adaptive reuse of buildings. Autom Constr, 2019, 107: 102921.

[2]
D. Iurchak. 200–400 Nuclear Reactors to be Decommissioned by 2040 [Online]. 2020: pp 1–8. https://energypost.eu/200-400-nuclear-reactors-to-be-decommissioned-by-2040/ (accessed 2023-10-01).
[3]

R. Volk, F. Hübner, T. Hünlich, et al. The future of nuclear decommissioning—A worldwide market potential study. Energy Policy, 2019, 124: 226–261.

[4]
M. Ghobadi, S. M. E. Sepasgozar. An investigation of virtual reality technology adoption in the construction industry. In: Smart Cities and Construction Technologies. S. Shirowzhan, K. F. Zhang, Eds. London, UK: IntechOpen, 2020: pp 157–192.
[5]

M. Kassem, L. Benomran, J. Teizer. Virtual environments for safety learning in construction and engineering: Seeking evidence and identifying gaps for future research. Vis Eng, 2017, 5: 16.

[6]

B. V. Koen. Toward a definition of the engineering method. Eur J Eng Educ, 1988, 13: 307–315.

[7]

H. Abou-Ibrahim, F. Hamzeh, E. Zankoul, et al. Understanding the planner’s role in lookahead construction planning. Prod Plann Control, 2019, 30: 271–284.

[8]
L. Priyadarshini, P. Roy. Risk assessment and management in construction industry. In: Recent Developments in Sustainable Infrastructure (ICRDSI-2020)—Structure and Construction Management. B. B. Das, C. P. Gomez, B. G. Mohapatra, Eds. Singapore: Springer, 2022: pp 539–556.
[9]

W. F. Xu, B. Liang, Y. S. Xu. Survey of modeling, planning, and ground verification of space robotic systems. Acta Astronaut, 2011, 68: 1629–1649.

[10]
S. Bükrü, M. Wolf, O. Golovina, et al. Using field of view and eye tracking for feedback generation in an augmented virtuality safety training. In: Proceedings of the Construction Research Congress 2020: Safety, Workforce, and Education, Reston, USA, 2020: pp 625–632.
[11]

A. A. Muhammad, I. Yitmen, S. Alizadehsalehi, T. Celik. Adoption of virtual reality (VR) for site layout optimization of construction projects. Teknik Dergi, 2019, 31: 9833–9850.

[12]

S. You, J. H. Kim, S. Lee, et al. Enhancing perceived safety in human–robot collaborative construction using immersive virtual environments. Autom Constr, 2018, 96: 161–170.

[13]

J. M. Davila Delgado, L. Oyedele, T. Beach, et al. Augmented and virtual reality in construction: Drivers and limitations for industry adoption. J Constr Eng Manage, 2020, 146: 04020079.

[14]

R. Dias Barkokebas, M. Al-hussein, X. M. Li. VR–MOCAP-enabled ergonomic risk assessment of workstation prototypes in offsite construction. J Constr Eng Manage, 2022, 148: 04022064.

[15]

X. M. Li, S. Han, M. Gül, et al. 3D visualization-based ergonomic risk assessment and work modification framework and its validation for a lifting task. J Constr Eng Manage, 2018, 144: 04017093.

[16]

Y. X. Zhang, B. Xiao, M. Al-Hussein, et al. Prediction of human restorative experience for human-centered residential architecture design: A non-immersive VR–DOE-based machine learning method. Autom Constr, 2022, 136: 104189.

[17]

A. Ahmad, I. Yitmen, S. Alizadehsalehi, et al. Adoption of virtual reality (VR) for site layout optimization of construction projects. Tek Dergi, 2020, 31: 9833–9850.

[18]
International Atomic Energy Agency. Handling and Processing of Radioactive Waste from Nuclear Applications. Vienna, Austria: International Atomic Energy Agency, 2001: pp 1–143.
[19]
International Atomic Energy Agency. Disposal Aspects of Low and Intermediate Level Decommissioning Waste. Vienna, Austria: International Atomic Energy Agency, 2007.
[20]

M. Fakoor, S. M. N. Ghoreishi, H. Sabaghzadeh. Spacecraft component adaptive layout environment (SCALE): An efficient optimization tool. Adv Space Res, 2016, 58: 1654–1670.

[21]
S. H. Wu, M. Kay, R. King, et al. Multi-objective optimization of 3D packing problem in additive manufacturing. In: Proceedings of the IIE Annual Conference, Norcross, USA, 2014: pp 1485–1494.
[22]
A. M. Chugay, A. V. Zhuravka. Packing optimization problems and their application in 3D printing. In: Advances in Computer Science for Engineering and Education III. Z. B. Hu, S. Petoukhov, I. Dychka, et al., Eds. Cham (Germany): Springer, 2021.
[23]

J. Vanek, J. A. G. Galicia, B. Benes, et al. PackMerger: A 3D print volume optimizer. Comput Graphics Forum, 2014, 33: 322–332.

[24]
S. Edelkamp, P. Wichern. Packing irregular-shaped objects for 3D printing. In: Proceedings of the 38th Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz), Dresden, Germany, 2015: pp 45–58.
[25]

A. A. S. Leao, F. M. B. Toledo, J. F. Oliveira, et al. Irregular packing problems: A review of mathematical models. Eur J Oper Res, 2020, 282: 803–822.

[26]

L. J. P. Araújo, A. Panesar, E. Özcan, et al. An experimental analysis of deepest bottom-left-fill packing methods for additive manufacturing. Int J Prod Res, 2020, 58: 6917–6933.

[27]

A. Hertz, M. Widmer. Guidelines for the use of meta-heuristics in combinatorial optimization. Eur J Oper Res, 2003, 151: 247–252.

[28]

Y. H. Zhao, C. Rausch, C. Haas. Optimizing 3D irregular object packing from 3D scans using metaheuristics. Adv Eng Inf, 2021, 47: 101234.

[29]
Unity Asset Store (n.d.). The best assets for game making. https://assetstore.unity.com/
[30]
I. Ikonen, W. E. Biles, A. Kumar, et al. Concept for a genetic algorithm for packing three dimensional objects of complex shape [Online]. J Required, 1996: 1–5. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=476099cc8239eaea2e4bfa213a2f92225bff319f (accessed 2023-10-01).
[31]

A. S. Gogate, S. S. Pande. Intelligent layout planning for rapid prototyping. Int J Prod Res, 2008, 46: 5607–5631.

[32]

S. S. Shapiro, M. B. Wilk. An analysis of variance test for normality (complete samples). Biometrika, 1965, 52: 591–611.

[33]

B. L. Welch. On the comparison of several mean values: An alternative approach. Biometrika, 1951, 38: 330–336.

[34]

P. A. Games, J. F. Howell. Pairwise multiple comparison procedures with unequal N’s and/or variances: A Monte Carlo study. J Educ Stat, 1976, 1: 113–125.

[35]

G. Ballard, G. Howell. Shielding production: Essential step in production control. J Constr Eng Manage, 1998, 124: 11–17.

[36]

T. Romanova, J. Bennell, Y. Stoyan, et al. Packing of concave polyhedra with continuous rotations using nonlinear optimisation. Eur J Oper Res, 2018, 268: 37–53.

Journal of Intelligent Construction
Article number: 9180022
Cite this article:
Zhao Y, Earle G, Jung Y-H, et al. Selecting deconstruction processes using virtual reality comparisons. Journal of Intelligent Construction, 2024, 2(3): 9180022. https://doi.org/10.26599/JIC.2024.9180022

4251

Views

231

Downloads

0

Crossref

Altmetrics

Received: 19 October 2023
Revised: 01 February 2024
Accepted: 15 February 2024
Published: 10 July 2024
© The Author(s) 2024. Published by Tsinghua University Press.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Return