Stochastic modelling of non-stationary and dependent weather extremes for structural reliability analysis in the changing climate
Bibliographic record
Abstract
In recent times, the safety of infrastructure systems has been challenged by the increasing severity of extreme weather events caused by the effects of climate change . This trend is expected to continue, as shown by the simulations of future climate conditions under high-emission scenarios. The paper presents a general stochastic process , known as the Linear Extension of the Yule Process (LEYP), to model the non-stationary frequency and intensity of extremes. The LEYP model overcomes a major limitation of the classical Poisson process by including the statistical dependence among extreme events. The paper presents a probabilistic framework for non-stationary structural reliability analysis, which includes new results for the return period, waiting time for the next event, correlation coefficient , and the distribution of the maximum load in a given time interval. The examples provided in the paper demonstrate that even a modest degree of dependence can significantly reduce the interval between events and increase the probability of failure with time. Furthermore, the paper illustrates the non-stationary modelling of future precipitation data, as simulated by the Canadian Earth Systems Model (CanESM5). The results of this study are expected to be useful for revising current ”stationary” design codes and ensuring structural safety in the changing climate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".