Development of a civil infrastructure resilience assessment framework and its application to a nuclear power plant
Bibliographic record
Abstract
Recent disruptive events, such as earthquakes or floods, have caused severe damage to civil infrastructure systems. Thus, there is a need to extend the focus of traditional design practices to include resilience-based design approaches which can help in defining preventive actions and measures to mitigate the consequences caused by such disruptive events. This paper presents a Civil Infrastructure Resilience Assessment Framework (CIRAF) to assess the seismic fragility and resilience of a single or multiple interconnected civil infrastructure systems following a disruptive event. Once the information regarding the infrastructure system, hazards, fragility functions, component's damage state correlation, recovery models, and upgrade models are identified, then the framework can be used to quantify the loss of functionality, recovery time, repair cost, and overall resilience using a Bayesian Network approach. A state-of-the-art engineering tool is also developed using the framework that would enable the stakeholders to compare different upgrade strategies through an easy to use web interface and thus easing the decision-making process. A case study of a hypothetical nuclear power generation system is used to assess the seismic fragility and resilience using the proposed framework.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".