Urban heritage documentation often separates 3D data acquisition from immersive interaction, limiting both accuracy and user impact. This study develops and validates an end-to-end workflow that integrates UAV photogrammetry with terrestrial LiDAR and deploys the fused model in a VR environment. Applied to Piazza Vittorio Emanuele II in Rovigo, Italy, the approach achieves centimetre-level registration, completes roofs and upper façades that ground scanning alone cannot capture, and produces stable, high-fidelity assets suitable for real-time interaction. Effectiveness is assessed through a three-layer evaluation framework encompassing vision, behavior, and cognition. Eye-tracking heatmaps and scanpaths show that attention shifts from dispersed viewing to concentrated focus on landmarks and panels. Locomotion traces reveal a transition from diffuse roaming to edge-anchored strategies, with stronger reliance on low-visibility zones for spatial judgment. Post-VR interviews confirm improved spatial comprehension, stronger recognition of cultural values, and enhanced conservation intentions. The results demonstrate that UAV-enabled completeness directly influences how users perceive, navigate, and interpret heritage spaces in VR. The workflow is cost-effective, replicable, and transferable, offering a practical model for under-resourced heritage sites. More broadly, it provides a methodological template for linking drone-based data acquisition to measurable cognitive and cultural outcomes in immersive heritage applications.

Unmanned aerial vehicles (UAVs) are widely used for documentation, surveying, and 3D modeling in the built environment, yet their outputs often remain difficult to reuse for immersive comparison of alternative construction scenarios. This study presents a low-cost UAV-to-3DGS-to-VR workflow for constructing scenario-comparable immersive environments for built-environment review. The workflow combines multi-angle UAV imagery, point-cloud-based geometric anchoring, 3D Gaussian Splatting (3DGS), and Unity-based virtual reality (VR) to transform drone-captured reality into a reusable scene for controlled scenario comparison. The workflow is demonstrated in Middenbeemster, the central town of the Beemster polder World Heritage property. One present-condition scene (M0) and three alternative construction scenarios (M1 to M3) were created within a shared spatial reference. Reconstruction quality was assessed using PSNR and SSIM, and the VR scenes were further evaluated through eye-tracking, head-motion recording, and subjective ranking. The results indicate that the workflow can generate visually reliable and directly comparable immersive scenes from UAV data in this case study. Behavioral and subjective findings showed a consistent pattern, with M1 appearing more compatible than M2 and M3 in this pilot evaluation. The study contributes a pilot UAV-based workflow that links reality capture, immersive scenario comparison, and supplementary behavioral evidence within one process.

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures.