A Preliminary Investigation into Automated Identification ofStructural Steel Without A Priori Knowledge
Bibliographic record
Abstract
One of the most prohibiting factors when attempting to reuse structural steel members or systems of members is the time and labour required to accurately determine dimensions.Current practices dictate that all required measurements are recorded by hand using tape measures and calipers which adds a significant cost to reused steel.To mitigate this cost, a semi-automated method for identifying structural steel components and systems is proposed that uses data acquired in the form of a 3D point cloud.Current research in the field of automated object recognition currently has two major limitations: (1) a priori knowledge, such as a building information model (BIM) is required, or (2) only simple, flat surfaces can be identified.The purpose of this study is to preliminarily investigate the possibility of automating the process of (1) cross section identification, (2) end connection geometry of bolted connections, and (3) relative component position of multi-component, planar structural systems such as trusses.Cross section identification is performed by creating filters that match standard structural sections and then convolving them over images of the cross section data.The end connection geometry is identified using Hough algorithms to detect lines and circles representing the limits of the component and the bolt holes, respectively.Planar structural systems are identified using Hough algorithms to detect lines which represent the components of the system.The results from the proposed methods show a strong potential for fully automated processes to be able to identify structural steel components and systems without a priori knowledge.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".