Quality Index and Contractor Adjustment Factor of Highway Projects in Egypt
Bibliographic record
Abstract
Pay factors relate quality to actual pay. Quality measures are generally used by highway agencies for the acceptance of pavement construction. Material properties, smoothness and other characteristics of the constructed pavement will generally vary somewhat from the specified design values because construction operations are influenced by many factors. Such variance will affect pavement quality and it is performance. Furthermore, the highway agency and road users will be affected. This study aims at creating a new contractor adjustment factor (C.A.F) for the highway construction projects in Egypt in relation to the quality index (Q.I) of the constructed pavement. The philosophy of the developed method has been structured with respect to the percent reduction in life in years between (as-constructed) and (as-designed) pavement cross sections through the application of KENLAYER software. Furthermore the results obtained from this method will be compared with the results of the Egyptian Code for Urban and Rural Roads for determining contractor discount factor in order to show the fairness of the developed method. The analysis of the study results shows a noticeable difference between the suggested method (C.A.F) and the traditional method of the Egyptian Code for Urban and Rural Roads, therefore it is recommended to replace the traditional method with the new payment method because it is consider the pavement cross section as one unit. Keywords: Quality Measures, Pay Adjustment Factor, Pavement Damage Ratio and KENLAYER Software.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".