Robotic Kinematics Analogy for Realignment of Defective Construction Assemblies
Bibliographic record
Abstract
Aligning and plumbing of construction assemblies is a fundamental problem because of reliance on manual solutions for geometric feedback control problem involved with practices such as pipefitting and steel structures erection.Where defective components and segments are not well controlled, the errors propagate in larger components and therefore cause more severe problems.In order to address such a challenging problem and tackle potential solutions, this paper presents a framework for automatic and systematic development of realignment actions required to achieve a target state by borrowing concepts from: (1) 3D imaging that enables the identification of the as-built status and then quantification of incurred discrepancies as a feedback by comparing the captured status with the designed state existing in the building information model (BIM); and (2) an inverse kinematics analogy that results in the calculation of required changes in the degrees of freedom defined where realignment and changes can be applied.Experimental results show that the framework can generate the required actions for achieving a desired state systematically and accurately.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".