Special Issue on Complex Engineered Networks: Reliability, Risk, and Uncertainty
Bibliographic record
Abstract
Complex engineered networks form a technological skeleton of our modern society. Examples include electric power grids, road and airline networks, cellular grids, and various distribution networks, such as water, gas, and petroleum networks. These distributed complex systems with many interconnected components provide critical services for everyday life, such as water, food, energy, transport, communication, banking, and finance. As a result of technological progress and worldwide urbanization and globalization processes, the dependence of our society on these complex systems spanning cities, countries, and even continents constantly grows. Given the critical role that engineered networks play in the functioning of our world, there is an increasing demand for these systems to be highly reliable and resilient. A deep understanding of their actual capabilities to withstand natural hazard, such as earthquakes, tsunamis, and hurricanes, and man-made threats, e.g., accidents and terrorism, is crucial. The related issues of resilient network design and operation are also closely related to sustainability problems which are of increasing importance today. In particular, the degree to which an engineered network subjected to internal or external stresses (e.g., cascading failures or seismic hazards) is capable of keeping (or recovering) the service demanded needs to be quantitatively estimated. A failure of a critical infrastructure to provide the required service could lead to a range of serious consequences for business, government, and the community. Quantitative assessment of network reliability and associated risks and uncertainties is therefore a key aspect of system design, optimization, and operation.This Special Issue of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems: Part B is dedicated to reliability, risk, and uncertainties in complex engineered networks. It consists of eight papers written by leading researchers, academics, and practicing engineers from Australia, Canada, Finland, France, Germany, Italy, Norway, and the U.S. on recent advances in the interdisciplinary field of complex engineered networks. The Special Issue covers a broad spectrum of research topics, including robust design and resilience analysis of critical infrastructures, reliability of technological networks in the presence of cascading failures, resilience of electricity distribution networks against extreme weather conditions, and strategies for reducing risks associated with attacks on airport terminals.The Guest Editors would like to greatly thank the authors for their valuable contributions, the Editor, Professor Bilal Ayyub, for his inspiring leadership, and the Assistant to the Editor, Deena Ziadeh, for her fantastic technical support. We sincerely hope that the readership of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems: Part B will enjoy this Special Issue, and that it will help to advance our understanding of complex engineered networks.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.021 |
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".