Digital Twin in the Architecture, Engineering, and Construction Industry: A Bibliometric Review
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Bibliographic record
Abstract
The architecture, engineering, and construction industry (AEC) is embracing digitization in the design, construction, and operation of built assets with the growing prominence of information technologies, such as building information modeling (BIM), internet of things (IoT), and artificial intelligence (AI). In this context, plenty of research efforts have been dedicated to digital twin (DT) applications. This research synthesizes state-of-art on DT in the AEC industry through bibliometric analysis, aiming to identify the research trends, challenges, and knowledge gaps in this growing area. A total of 75 publications regarding DT was identified and retrieved from Scopus. Then, VOSviewer was used for bibliometric analysis, including (1) keyword co-occurrence, and (2) citation analysis of selected publications. The identified research clusters and most-cited publications were discussed to clarify research trends and future needs. The findings revealed that future research should be directed to (1) data interoperability, (2) AIoT, and (3) AI. Moreover, extra research efforts should also be given to the DT applications during the design and construction phases of construction projects. This research contributes to the body of knowledge by quantitatively exposing research trends and needs for DT in the AEC industry.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.022 | 0.051 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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