Estimation of Failure Probability by Limit State Sampling
Bibliographic record
Abstract
This paper presents a novel approach, referred to as Limit State Sampling, for estimating failure probabilities of engineering structures. The majority of methods used to evaluate failure probabilities involve a large number of simulations of the structural model. In situations with low failure probability and numerically complex structural models, this can become a computationally unpractical task. The Limit State Sampling approach is developed here with the intention of reducing the number of simulations of the structural model in the process of evaluation of the failure probability. This is performed by introducing a pseudo probabilistic density function with the purpose of sampling around the failure limit state. Samples from the pseudo probability density function are then used to construct a surrogate model of the structural behavior at the failure limit state. Finally, the failure probability is estimated by utilizing the efficiency of the surrogate model, with reduced computational expense. The novelty of the approach comes from the formulation of the pseudo probability density function and the application to the probabilistic analysis of structures.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".