Developing BIM-Based Linked Data Digital Twin Architecture to Address a Key Missing Factor: Occupants
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 reviews the concept of Digital Twins (DTs) and related studies in the construction industry and identifies three key factors that is missing from the current practices. The missing factors are: (1) inadequate consideration of occupants in DT models, (2) lack of the inclusion of unstructured data, and (3) absence of Linked Data technologies. To address these issues, architecture for the design of DTs is proposed and partially implemented in a case study. The proposed architecture utilizes semantic web technologies and proposes a linked data approach to integrate different data sources of a DT. Further, the architecture leverages machine learning approaches to dynamically update and enrich the linked data platform and automate its maintenance. The case study takes the first step to integrate BIM and unstructured data generated by occupants (as work orders) using a linked-data approach. The research sets the path for future works in the domain of building DTs.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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