Simulation-based approach for risk assessment in onshore wind farm construction projects
Bibliographic record
Abstract
Wind farm projects are one of the fastest growing sources for renewable energy in Canada. The construction phase of wind farm projects is associated with numerous risks, which may lead to unpredictable consequences during project execution. Uninformed decisions made in response to such risks can lead projects to deviate from original objectives, resulting in project time and cost overruns. Quantitative risk analysis using simulation techniques can provide insight on risk exposure and its magnitude. Simulation-based approaches for risk assessment have been widely and successfully applied to model and quantify the risks associated with different types of construction projects. This research presents a Monte Carlo-Critical Path Method simulation model to quantify the impact of risks on the project cost and time specifically for wind farm construction projects. An in-house developed simulation engine, SimphonyProject.NET,1is used to simulate the construction processes of wind farm projects along with the risks affecting the project cost and time. The result of this research will assist decision makers in the wind energy industry to effectively estimate the time and cost contingencies of onshore wind farm projects.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".