Application of knowledge discovery in database (KDD) techniques in cost overrun of construction projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Currently, cost overrun is a global challenge to completing construction projects successfully. To overcome this problem, earlier studies investigated factors of cost overrun. Knowledge Discovery in Data (KDD) and data mining techniques have been implemented effectively in various research areas to extract novel and valuable knowledge from historical data but have only recently been implemented in the construction industry. The aim of this research is to develop a model that predicts project cost overrun using a suitable data mining technique and cost overrun factors as predictors. A review of the literature identified twelve factors that can be easily measured and analyzed in construction projects. A case study was performed to validate the model with an actual data set of executed projects. The resulting model is simple, interpretable, and relatively accurate (60.87%), and it uses three steps of data mining – clustering, feature selection, and classification. These steps improve model performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it