Quantifying the Monetary Impact of Schedule and Cost Risks in Construction Projects
Bibliographic record
Abstract
The competitive nature of construction projects coupled with the tight budgets and limited resources make them good candidates for failure if not managed properly.This is in addition to the complex, fragmented, and multidisciplinary nature of projects involving thousands of tasks and details and many participants.These immanent facts promote the emergence of risks in every single construction project.These risks should be effectively managed to mitigate their impact and avoid or reduce delays and cost overruns.While risk management is a difficult and challenging task and requires careful considerations throughout the life cycle of a project, it is important to be proactive and have a risk management plan to tackle this crucial issue by regularly monitoring risk events with an effective communication mechanism among the various project stakeholders, despite the high cost associated with the risk managing process.The majority of research efforts focused on the identification and assessment of risk events with limited efforts addressing the issue of quantifying the effect of these risk events and suggesting effective responses to them.This paper focusses on the use of the program evaluation and review technique (PERT) and the earned value analysis to quantify the impact of the severity of risk events and estimate the expected cost at completion.The study also suggests practical responses to mitigate the impact of risk events.A spreadsheet is developed to help project managers manage risk events and estimate their monetary impact on the project's duration and cost.The developed spreadsheet is expected to help construction contractors mitigate risks in construction projects and be more competitive.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".