A Stochastic Simulation Approach for the Integration of Risk and Uncertainty into Megaproject Cost and Schedule Estimates
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".