A Stochastic Simulation Approach for the Integration of Risk and Uncertainty into Megaproject Cost and Schedule Estimates
Why this work is in the frame
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Bibliographic record
Abstract
During the estimation phase of megaprojects, many traits must be addressed and taken into account in order to obtain realistic results. Objectives, influential factors, and their interdependencies must be accurately identified and measured. As part of this process, it is crucial to determine the uncertainty around project objectives as well as risk events that may impact the project on various levels. This paper proposes a method of defining and incorporating the uncertainty and risk events around the two main objectives of any megaproject (i.e., cost and schedule). To serve this purpose, a stochastic event simulation model which operates based on Monte Carlo simulation and uses Microsoft Project and @Risk for Microsoft Excel as an integrated simulation platform has been developed. The goal of the proposed model is to incorporate uncertainty and risk into the project schedule and assess the variations of the model output with respect to the deterministic estimation of cost and schedule. The focus of this study is on megaprojects that incorporate both sequential construction activities as well as cyclical manufacturing activities. The project used to validate this approach is a nuclear plant Retube and Feeder Replacement (RFR) project in Ontario, Canada.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it