A Prediction Model for Construction Project Time Contingency
Bibliographic record
Abstract
Construction projects often suffer from high level of uncertainty in many fronts: time, cost, quality, safety, etc. Most of the conducted research focuses on estimating cost contingency. Even though construction project scheduling has received extensive attention of researchers, time contingency was not treated well in the literature. In order to meet the deadline of a project, an accurate scheduling should be sought. Due to the nature of construction projects, scheduling should be flexible enough to accommodate changes without negatively affecting the overall duration of the project. As such, the objectives of the research presented in this paper are to assess the factors that affect scheduling contingency and to develop a simple model that can be used in estimating the expected time contingency of a construction project. A survey was conducted on sixteen construction companies in Montréal, Québec, Canada. The data obtained from the survey was then processed using Analytic Hierarchy Processes (AHP) to develop a time contingency model. Results show that the predicted time contingency matches with 87% the estimated contingency for real projects. The developed time contingency model showed robust 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".