Parallel Systems and Structural Frames Realignment Planning and Actuation Strategy
Bibliographic record
Abstract
Parallel structural systems and assemblies are challenging to erect, align and plumb on construction sites due to their complex geometries and current heuristic realignment strategies. Examples of parallel systems include complicated pipe modules and pipe racks in the industrial construction sector. This paper presents a generalized approach analogous to robotics and inverse kinematics for building parallel systems’ realignment planning, introduced using a series approach. In addition to the calculation of a realignment strategy, feasible applications of such a strategy are also investigated in this paper. The framework for realigning parallel systems has two primary steps: (1) as-built status identification by capturing the geometric state of construction assemblies using three-dimensional (3D) imaging theories, and (2) realignment calculation and actuation based on degrees of freedom (DOFs) defined during the development of the kinematics chains of assemblies. A Quasi-Newton-Raphson (QNR) method is employed for solving the kinematics equation of the inverse kinematics analogy. Experimental results show that the developed algorithms are sufficiently accurate to capture any incurred geometrical discrepancies in parallel construction assemblies and proactively calculate and plan for efficient realignment strategies. Generalization of realignment calculation for parallel systems and realignment actuation are the key contributions of this work.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".