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Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty
Active learning for structural reliability: survey, general framework and benchmark
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Conflicts of Interest
Identified Weaknesses
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Paper Summary
Paperzilla title
PC-Kriging + Subset Simulation: A Dynamic Duo for Reliability Analysis (But No Free Lunch!)
This paper proposes a modular framework for active learning reliability analysis and benchmarks 39 different strategies on 20 reliability problems. The study finds that PC-Kriging combined with subset simulation, the deviation number U learning function, and a combined stopping criterion generally performs best, although no single strategy is universally superior (no free lunch!). Over-calibrating reliability solvers when using surrogate models is shown to improve accuracy.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Limited Scope of Benchmark Problems
The benchmark problems are limited in scope, focusing primarily on low to moderate dimensional problems with single limit-state functions. This restricts the generalizability of the findings to more complex scenarios like high-dimensional, time-variant, or dynamic problems.
Limited Selection of Methods
Although 39 strategies were tested, they were built from a limited selection of methods within each module. A wider range of methods and combinations should be explored to strengthen the conclusions.
Overkill Settings and Practicality
The 'overkill' settings for reliability algorithms, while beneficial in this context, may not be practical in real-world applications with truly expensive models where computational budget is a primary concern. The trade-off between accuracy and computational cost needs further investigation under more realistic constraints.
Limited Investigation of Stopping Criteria
The choice of stopping criteria, while explored, could benefit from a more in-depth analysis regarding the impact of different thresholds and combinations of criteria. This would provide more robust guidance for practitioners.
Rating Explanation
This paper provides a valuable contribution to the field of structural reliability analysis by presenting a comprehensive survey of active learning methods, proposing a generalized framework, and conducting an extensive benchmark study. The modular framework and the insights gained from the benchmark offer practical guidance for practitioners. While the benchmark problems are limited in scope and the selection of methods could be broader, the study's strengths outweigh these limitations, justifying a rating of 4.
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Original Title:
Active learning for structural reliability: survey, general framework and benchmark
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July 14, 2025 at 10:44 AM
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