Immersive Technology Integration for Improved Quality Assurance and Assessment Jobs in Construction
Year: 2025
Type: article
Abstract: Construction quality failures impose substantial costs on the industry, with traditional quality assurance (QA) methods operating reactively by detecting problems after they occur rather than preventing them during planning and design phases. Limited research exists on the systematic integration of immersive technologies (IMTs) for proactive quality failure prevention across construction project lifecycles. This study investigates how IMTs can systematically prevent specific quality failure categories through enhanced spatial visualization and virtual verification processes. A qualitative approach was employed, combining scoping literature review, two purposively selected case studies, and autoethnographic analysis to capture both performance metrics and implementation insights. Case Study 1 achieved 8% improvement in solar panel placement efficiency (optimizing from 82 to 90 modules) and 1.7% increase in useful energy production (85.8% vs. 84.1%) through BIM-Unreal Engine integration for shadow analysis and spatial optimization. Case Study 2 demonstrated virtual site mobilization using the Revit–Twinmotion workflow, eliminating spatial conflicts and safety clearance violations during pre-construction planning. Findings revealed that IMT applications systematically address quality failure root causes by preventing design coordination errors, measurement mistakes, and regulatory non-compliance through virtual verification before physical implementation. This paper establishes IMTs as transformative QA platforms that fundamentally shift construction quality management from reactive detection to proactive prevention, offering measurable improvements in project delivery efficiency and quality outcomes.
Source: Architecture
Institutions: RMIT University, Monash University, University of Delaware
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W4415968297