Selecting deconstruction processes using virtual reality comparisons

Yinghui Zhao; Gabriel Earle; Yun-Ha Jung; Carl Haas; Sriram Narasimhan

doi:10.26599/JIC.2024.9180022

| Sign up

PDF (3.1 MB)

Cite

EndNote(RIS) BibTeX

Collect

Submit Manuscript

Show Outline

Figures (7)

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Tables (3)

Table 1

Table 2

Table 3

Research Article | Open Access

Selecting deconstruction processes using virtual reality comparisons

Yinghui Zhao^{^a}, Gabriel Earle^{^a}(), Yun-Ha Jung^{^a}, Carl Haas^{^a}, Sriram Narasimhan^{^b}

aDepartment of Civil and Environmental Engineering, University of Waterloo, Waterloo N2L 3G1, Canada

bDepartment 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.

Keywords

virtual reality deconstruction packing optimization human computer collaboration sustainability nuclear decommissioning

References

[1]

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

Participant	Education level	VR familiarity	Nuclear packing familiarity
A	Ph.D. researcher	Medium	High
B	Undergraduate	High	Low

Scenario	Sequential	Backhoe loader	Human-guided	Automated
Number of invalid packing configuration	17	21	0	0
Percentage of invalid packing configuration (%)	42.5	52.5	0	0

Scenario		Mean difference	Sig.	95% Confidence interval
Scenario		Mean difference	Sig.	Lower bound	Upper bound
Note: *The mean difference is significant at the 0.050 level.
Sequential packing	Backhoe loader packing	0.0943	<0.001*	0.0620	0.1267
	Trial and error	−0.0279	0.002*	−0.0472	−0.0085
	Autonomous packing	−0.0323	<0.001*	−0.0508	−0.0139
Backhoe loader packing	Sequential packing	−0.0943	<0.001*	−0.1267	−0.0620
	Trial and error	−0.1222	<0.001*	−0.1525	−0.0919
	Autonomous packing	−0.1267	<0.001*	−0.1565	−0.0968
Human-guided packing	Sequential packing	0.0279	0.002*	0.0085	0.0472
	Backhoe loader packing	0.1222	<0.001*	0.0919	0.1525
	Autonomous packing	−0.0045	0.807	−0.0176	0.0087
Autonomous packing	Sequential packing	0.0323	<0.001*	0.0139	0.0508
	Backhoe loader packing	0.1267	<0.001*	0.0968	0.1565
	Trial and error	0.0045	0.807	−0.0087	0.0176