Developing a BIM-enabled robotic manufacturing framework to facilitate mass customization of prefabricated buildings
Bibliographic record
Abstract
Industrialized construction has been accepted as an effective production method for building project stakeholders to improve installation quality. Recent advancements in industrialized construction have focused on parametric designs for manufacturing and assembly to ensure accurate information flows and workflows across different project stages, however, they have not adequately addressed the challenges in mass customization of building projects to meet the diverse needs of communities. This study develops a technological framework based on Building Information Modeling (BIM) processes for mass customization of prefabricated buildings, which consists of parametric design and robotic manufacturing (RM) information flows to improve design flexibility and manufacturing precision. A proof of concept case study of a single-family house built with Light Gauge Steel (LGS) wall frames was conducted to demonstrate the usability of the proposed framework. Findings show that the BIM-RM framework not only helps bridge the technological interoperability gap between BIM and RM programs but also contributes to improved scalability, efficiency, and cost-effectiveness of design-to-manufacturing processes in construction projects. • Integrated workflow for mass customization design and production of prefabricated building projects. • Parametric design translated into robotic fabrication information to reduce manual effort in information exchange. • Consistent mapping of the coordinate systems between design and robotic fabrication software platforms. • Light-gauge steel wall assembly to prove the usability of the integrated workflows for mass customization. • Automation and simulation of wall studs assembly to prove the flexibility of digital fabrication processes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".