Applying the Concept of Selective Assembly to Modular Construction to Mitigate Impacts of Component Variability
Bibliographic record
Abstract
As adoption of offsite methods of production continues to grow within the construction industry, optimization techniques from manufacturing are increasingly being utilized analogously for increasing productivity, reducing rework, and improving assembly processes. This paper demonstrates how the concept of selective assembly can be applied in modular construction as a potential assembly optimization technique. Rather than specifying and controlling tight fabrication tolerances, the selective assembly process groups components into bins or categories of compatible dimensional and geometric properties in order to find an optimal arrangement of interchangeable components in an assembly. This concept has traditionally proven to be more cost effective in certain manufacturing applications than using rigorous specification and control of tight fabrication tolerances. Using a laser scanner for asbuilt data acquisition, a modular steel bridge is analyzed as a case study to demonstrate how the concept of selective assembly can be applied in modular construction. The results of this case study show that selective assembly has potential to reduce rework in certain modular construction applications.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".