Comparative Analysis of Digital Technology in Architectural, Engineering Construction Industries Across Six Continents of the World: A Global Perspective
Bibliographic record
Abstract
This paper investigates integrating and comparing digital technology in the architectural, engineering, and construction (AEC) industry on the world's six continents, concentrating on the adoption of designs, points of interest, and suggestions for AEC instruction. The study draws insights from current research and industry reports to underline the five most recent popular digital technologies—building Information Modeling (BIM), 3D Printing, the Internet of Things (IoT), Digital twins, and GIS—and their significance and the importance of aligning construction education with industry innovations. The subject utilizes an online survey, exhaustive online information search (using search engines), and choices of journals for the investigation. To begin with, the five biggest economies nations of each continent, but Antarctica was partially utilized for comparison in this subjective research to complete the seven continents of the world. The result appears that North America (US and Canada) and Europe (UK, France, and Germany) are the driving pioneers and early adopters of digital technology in architecture, engineering, and construction. Asia (China, Seoul) The AEC market is adopting this digital technology spontaneously. Oceania (except Australia) is behind Asia in the adoption rate; South America and Africa are the late adopters of this digital technology in the industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".