Current Status of Digital Twins in the AEC Industry and Opportunities in Construction Project Management from a Literature Review and a Quebec Perspective
Bibliographic record
Abstract
The Digital Twin (DT) concept is increasingly regarded as a revolutionary technological tool for enhancing productivity across various industries.While there is a significant trend exploring its applications in sectors such as manufacturing, automotive and others.In the Architecture, Engineering, and Construction (AEC) industry, its development remains in its early stage, particularly during the design and execution phases.Despite high expectations surrounding its potential, the level of implementation in construction remains low, accompanied by skepticism among some stakeholders regarding its tangible benefits.To explore both the theoretical potential and the practical challenges for DT implementation in construction, this study draws on the analysis of two sources of information.The first source is the scientific literature from which peer-reviewed papers were analyzed to understand the key aspects of the DT concept, including its definition, capabilities, intended purposes, barriers and challenges.The second source consists of semi-structured interviews conducted with a small sample of stakeholders of Quebec's AEC Industry.These interviews aimed to assess stakeholders' knowledge, acceptance, perception and challenges related to DT concept.This study compares the findings from the global literature with a sample of local stakeholders of the Canadian AEC industry.The results of this study indicate a high level of acceptance of DT within Quebec's construction sector.However, they also reveal several factors -most of them related to being in the early stages of adoption-that may help explain the limited implementation of DT in construction projects. 1.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.034 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".