Data-Driven Assessment of Complexity-Induced Risks in Infrastructure Projects
Bibliographic record
Abstract
Infrastructure projects are characterized by inherent complexities that often lead to their poor performance. Notwithstanding challenges posed by various risks and their interactions, the additional non-linear and dynamic interdependence-induced complexities make infrastructure projects susceptible to systemic risks—probable component disruption that can lead to cascade (system-level) disruptions. The study of teams/resource interdependence-induced systemic risks in an environment of interacting risks is scarce in the literature. In addition, several previous studies demonstrated that current risk interactions and systemic risk analysis models are impractical due to their complexity and limited theoretical application domains. In this respect, the current study fills this knowledge gap by developing a data-driven risk interactions and systemic risk management approach. This approach is formulated in three stages: (1) quantifying risk interactions and teams/resources interdependence; (2) building machine learning model (ML) models to predict project performance based on the quantified characteristics; and (3) devising relevant mitigation strategies. The study also includes a practical demonstration application of the approach to present a step-by-step demonstration for each stage—thus guiding practitioners to proactively safeguard against risk interactions and systemic risks. The current work contributes to the body of knowledge by laying out the foundations of investigating the compound phenomenon of risk interactions and systemic risks as well as by presenting an effective approach to achieve that endeavor. Overall, the current study introduces a reliable and practical approach to enhance the performance of infrastructure projects through interacting risks- and systemic risk-informed management strategies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".