Drivers for adopting augmented reality and virtual reality technologies in the construction project management in Gaza City
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores the motivations and opportunities for adopting Augmented Reality (AR) and Virtual Reality (VR) technologies in construction project management in Gaza. A quantitative method was used, involving a questionnaire survey of 40 construction professionals. From an initial list of 35 potential drivers identified through a literature review, 33 were finalized after validation and pre-testing. These drivers were categorized into three groups: Improving Project Performance, Enhancing Company Image, and Boosting Overall Company Performance. Data analysis using SPSS revealed that the most influential drivers were real-scale design visualization, better understanding of design impacts, improved project comprehension, visualization of construction progress, and enhanced understanding of client requirements. In contrast, government incentives were ranked lowest in influence. The results highlight the significant potential of AR and VR to enhance design interpretation and project delivery in Gaza’s construction sector. The study recommends targeted strategies and training for construction practitioners to optimize the use of these technologies. By filling a research gap, the findings offer practical insights for professionals, policymakers, and researchers aiming to integrate AR and VR in construction, particularly in conflict-affected or resource-limited regions where such tools could substantially improve project outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it