An Integrated Planning and Control Framework (IPCF) for Construction Projects—Step 1: Development of the Construction Data Hub (CDH)
Bibliographic record
Abstract
Construction projects generate a significant volume of scattered data in various formats. However, having a large amount of data is insufficient; there is a need to obtain the appropriate metadata to enable extracting useful knowledge from it. Therefore, professionals need a consistent data acquisition model to gather comprehensive data from multiple projects and organizations in a format ready for applying machine learning. This research proposes an Integrated Planning and Control Framework (IPCF) to implement the concept of “From Data to Decision (FD2D)” in the construction industry. The first step of the framework is the development of the Construction Data Hub (CDH). The CDH seeks to collect data from twelve dimensions that impact the project’s planning and control. It relies on using the industry-accepted concept of work packages, which is the optimum level of detail for data acquisition. To validate the CDH, a machine learning model that utilizes the data collected through the CDH is developed to analyze the factors influencing construction project profit. The study revealed six significant profit-influencing factors. These factors might assist estimators in predicting profit margins during the early estimation stage, instead of relying on intuition or uniform rates, which are not always reliable methods.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".