Optimum Assembly Planning for Modular Construction Using BIM and 3D Point Clouds
Bibliographic record
Abstract
Geometric and dimensional deviations often create challenges for component aggregation in the assembly of interchangeable components in modular construction. Although the components are designed interchangeably, once they are fabricated, there are inevitable discrepancies between the designed and built states. Such discrepancies create problems for fitting interchangeable modular components. This paper presents a framework for optimally planning the assembly of interchangeable components based on their as-built state. A 3D point cloud model is captured and the critical interfaces between modules are compared to the original state, integrated in the building information models (BIM), as 3D drawings. The optimization framework is implemented based on two different approaches: (1) minimization of the total deviation for minimizing rework, and (2) intervention of rework by finding the best matching component for each investigated slot. Results show that the method can be effectively used for reducing rework in modular construction by optimum assembly planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".