Active learning for structural reliability: survey, general framework and benchmark
Descripción general
Resumen del artículo
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.
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Scientists found better ways for computers to figure out how strong things like bridges are, so we don't need endless tests. They learned that some specific computer methods work best together, but no single method works perfectly for all situations.
Posibles conflictos de intereses
None identified
Limitaciones identificadas
Explicación de la calificación
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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