Enhanced digital twins (EDTs) approach based on advanced inspection and technology adoption to achieve sustainable construction processes
Bibliographic record
Abstract
The current trend in the Architecture, Engineering, and Construction (AEC) industry is undergoing digitalisation rapidly, meeting the requirements of sustainability while increasing efficiency, reducing costs and improving decision-making. This transformation is based on the connection between the physical environment and the virtual digital systems. However, Digital Twins (DTs) can represent this connection, and despite its importance, currently DTs are hindered by several challenges related to the accuracy of data, long workflows and fragmented digital ecosystems. To this end, this study proposes an innovative approach, “Enhanced Digital Twin (EDT)”, to overcome the traditional DTs’ limitations. To achieve this, a scientometric review is conducted based on quantitative mixed methods using VOSviewer and RStudio to analyse the collected database from Scopus of 342 peer-reviewed journal articles from 2020 to 2024. This was further supported through the analysis of similar applied case studies of relevant literature based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Findings represent that EDT can offer advantages through three main categories: construction, building and human aspects. The article also outlines future research directions and highlights some key benefits and challenges. Also, this article contributes originally to the existing body of knowledge by bridging the gap between digital technology and sustainable practices through proposing a holistic framework for SDGs-based EDT in line with the United Nations Sustainable Development Goals (UN SDGs). The latter supports the implementation of the EDT, reaching the SDGs, and the engagement of stakeholders in all phases of the building life cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".