Application of machine learning approach for logistics cost estimation in panelized construction
Bibliographic record
Abstract
Logistics operations in panelized construction are vital daily tasks that connect the panel manufacturing facility to the job site. Although logistics operations are both important and prevalent in panelized construction, the cost of logistics has yet to be fully understood by either industry or academia due to the complicated relationship between multiple factors in logistics demands and operations. In practice, logistics is considered as an overhead cost that consists of various indirect or fixed costs in the panelized construction operation. As a result, logistics cost estimates are rendered inaccurate when subjected to project changes. Considering the number of construction projects over the course of a year, inaccurate logistics cost estimates are significant. Previous studies have shown that a machine learning approach could be used to predict costs that are influenced by multiple factors. To fill knowledge gaps in both research and practice, in this study machine learning based on historical logistics data is used to accurately predict logistics costs for a given project. The results from this study indicate that machine learning can be a reliable tool to predict logistics costs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".