Near Optimum Selection of Module Configuration for Efficient Modular Construction
Bibliographic record
Abstract
Modular construction has received considerable attention in recent years. This has been attributed to its impact on cost and time reduction and improved productivity and quality of constructed facilities. Modular construction can also result in improved safety on construction jobsites and reduced material waste. Most recent work in this field focused cranes selection and location, more suited scheduling methods and issues pertinent to logistics, without due consideration to optimized modules configuration. This paper introduces a newly developed unified modular suitability index to accomplish a near optimum selection of module configuration for efficient modular residential construction. The developed modular suitability index (MSI) utilizes five indices; 1) connections index (CI) that evaluates the module connections using the matrix clustering technique along with the bond energy algorithm, 2) transportation dimensions index (TDI) that accounts for the module dimensions’ effects on transportation, 3) transportation shipping distance index (TSDI) to evaluate the distance between modules fabrication and assembly facility and the project construction site, 4) crane cost penalty index (CCPI) to evaluate the crane cost relevant to the module placing rate, and 5) concrete volume index (CVI) to evaluate the project’s foundation concrete quantities. Calculating the modular suitability index (MSI) provides a unified indicator for the project stakeholders to assess the suitability of different modular configuration and support near optimum modules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".