Automatic Selection Tool of Quality Control Specifications for Off-site Construction Manufacturing Products: A BIM-based Ontology Model Approach
Bibliographic record
Abstract
Construction manufacturing specifications play an important role in assessing quality requirements on a construction project. However, working with these specifications can be overly complicated and error prone to the large amount of regulations and codes that need to be considered and their inter-dependencies. In building information modelling (BIM), the model is a digital representation of a complex construction product and contains precise product information data. The data is currently embedded into the model as properties for parametric building objects that are exchangeable among project operators. Some effort has been previously done to enhance the BIM model to obtain construction-oriented data and linking information that is crucial to manufacturing and quality control and assurance with BIM modelling still remains a challenge. This study proposes an extension to the current BIM-based product-oriented ontology model to include manufacturing processes and inspection, and quality control specifications. By automatically identifying which specifications are applicable to certain products and to extract the requirements imposed, this approach can support and enable automatic decision making in quality inspection and control tasks, which solely depend on information and knowledge from construction specifications. This approach is tested and validated using a light-gauge steel frame wall under Canadian construction standards and regulations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".