Automatic Classification of Design Conflicts Using Rule-based Reasoning and Machine LearningAn Example of Structural Clashes Against the MEP Model
Bibliographic record
Abstract
Automatic Classification of Design Conflicts Using Rule-based Reasoning and Machine LearningAn Example of Structural Clashes Against the MEP Model Ying-Hua Huang and Will Y. Lin Pages 324-331 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: With the emergence of 3D technologies in a recent decade, BIM software makes it easy to detect those conflicts in the early stage of a project. Clash detection in BIM software is now a common task. Among those conflicts found by BIM software, however, a relatively high percentage belongs to pseudo conflictswhich are permissible or tolerable, but BIM software does not reveal this information. Thus, currently BIM managers have to manually inspect every detected conflict to classify the type of conflict. Some researchers urged an automated process to facilitate this laborious process. This study implemented both a rule-based reasoning system and machine learning classifiers to help classify those BIM-detected conflicts. Preliminary testing results indicate that machine learning algorithms can achieve comparable results to a traditional rule-based system, but with much less costs and energy in developing. Keywords: Clash detection, Machine learning, Rule-based reasoning, BIM DOI: https://doi.org/10.22260/ISARC2019/0044 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".