Developing a Risk Breakdown Matrix for the Construction of On-Shore Wind Farm Projects
Bibliographic record
Abstract
Wind farm projects have recently gained popularity in many countries. However, since wind farms are a novel type of infrastructure for energy production for which limited historical data are available, numerous unique challenges are encountered during their construction. One of the main challenges involves risk management. Many researchers and practitioners have investigated on- and off-shore wind farm projects in terms of risk identification. However, they have mostly focused on off-shore wind farm projects; there is little research on risk identification for on-shore wind farm projects. To address this gap in the research, this paper develops a risk breakdown matrix (RBM) for the construction of on-shore wind farm projects. Due to a lack of research on risk identification for on-shore wind farm projects, in this paper, the case-based reasoning (CBR) technique is used to develop the RBM. First, the construction work packages (CWPs) of on-shore wind farm projects are identified. Then, by comparing the CWPs of these projects to those from other types of construction projects, the work-package-level risks that affect each CWP are identified based on the similarities between on-shore wind farm projects and other types of construction projects. The RBM developed in this paper can be used for the risk identification and risk management of on-shore wind farm projects. The contributions of this paper are twofold: First, it introduces CBR as a risk identification technique for on-shore wind farm or other similar construction projects, which is a topic that has not previously been comprehensively studied. Second, it identifies the work-package-level risks affecting these projects and maps each risk factor to the affected CWPs.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".