Building Information Modeling (BIM) and Robotic Manufacturing Technological Interoperability in Construction – A Cyclic Systematic Literature Review
Bibliographic record
Abstract
The architectural engineering and construction (AEC) industry is undergoing a digital transformation that progressively improves its performance, productivity, and competitiveness. This digital shift is accelerated through building information modeling (BIM) which facilitates technological integrations. BIM has significantly contributed to digitizing design and management activities. However, it has not yet sufficiently demonstrated its interoperability with digital manufacturing processes, such as robotic manufacturing (RM). It is from this perspective that this work will review the current literature’s stance on the technological interoperability of BIM and RM tools through the systematic literature review (SLR) method. This literature review aims to identify research avenues to operationalize RM through BIM tools in construction. The study conducted in this research is progressive; it builds on the identified research gaps and investigates potential research avenues to be undertaken. The results revealed that computational design (CD) could serve as a bridge between BIM and RM. They also revealed that RM is operationalizable in off-site construction (OSC) through BIM and CD.
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 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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".