Managing Interdependence-Induced Systemic Risks in Infrastructure Projects
Bibliographic record
Abstract
Complex by nature, infrastructure megaprojects rarely meet stakeholders’ expectations. A key characteristic of such complexity is the interdependence among different project stakeholders (e.g., contractors) where disruption of one contractor’s work may instigate (system-level) systemic risks, resulting in poor key performance indicators (KPIs) of the whole project. Attributed to the lack of appropriate analysis and quantification tools, managing the systemic risks resulting from such interdependence remains challenging. The current study fills this knowledge gap through proactive systemic risk management (i.e., early identification, analysis, mitigation, and continuous monitoring). The study enhanced and operationalized a previously developed conceptual framework, in which interdependence was quantified through employing complex network theoretic measures. Specifically, the current study correlated contractor interdependence to project KPIs in order to assess associated project systemic risks. Subsequently, a metaheuristic optimization technique was employed to reorganize the project’s schedule—effectively managing interdependence-induced risks. To demonstrate the utility of the developed methodology, a power infrastructure project was considered. Finally, the study provides valuable insights to improve the performance of complex infrastructure projects through proactive systemic risk management, ultimately enhancing the overall project’s hyper resilience (i.e., to interdependence-induced disruptions).
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".