Production prediction of conventional and global positioning system–based earthmoving systems using simulation and multiple regression analysis
Bibliographic record
Abstract
Accurate estimation of construction production, which is composed of productivity and unit costs, allows construction planners and managers to have excellent control over current processes and to correctly predict the production of similar projects in the future. Due to the need for accurate production estimation, selection of the appropriate construction technology is a critical factor in the success of a project. This paper presents a methodology for developing a model capable of predicting productivity and unit costs using several procedures, such as actual data collection, input data generation using construction simulation, and multiple regression analysis. An earthmoving operation was analyzed to estimate the proposed methodology’s prediction of construction production. A global positioning system (GPS)–based earthmoving system was selected as the new construction technology to be compared with the conventional system, to evaluate the decision-making process at a jobsite. The proposed methodology is expected to provide users with a basis for selecting appropriate technology. The case study presented in this paper demonstrates how to utilize the proposed methodology and analyze its predicted results.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".