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Aprendizaje activo mejorado Metamodelo de proceso gaussiano para estimar la probabilidad de cola unilateral de
Yunzhe Wang1, Yanwen Huang2, Yihang Huang3
1Department of Planning and Construction, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Scientific reports
|February 12, 2026
Resumen
Un nuevo algoritmo de aprendizaje global sensible a la cola (TS-GL) mejora la estimación de la probabilidad de eventos raros para megaestructuras. Este método mejora el análisis de seguridad de la infraestructura crítica al superar las técnicas existentes en precisión y eficiencia.
Área de la Ciencia:
- Ingeniería estructural Ingeniería estructural.
- La mecánica computacional es la mecánica computacional.
- Evaluación del riesgo Evaluación del riesgo.
Sus antecedentes:
- Las megaestructuras se enfrentan a desafíos de seguridad por fallas raras y de alto impacto.
- La estimación precisa de la respuesta estructural de baja probabilidad es difícil.
- Las fallas en las estructuras subterráneas, como los túneles del metro, pueden ser el resultado de una anchura de longitud insuficiente.
Objetivo del estudio:
- Desarrollar un nuevo marco para estimar con precisión las probabilidades de eventos raros en ingeniería estructural.
- Para mejorar la estimación de las probabilidades de cola unilaterales para las distribuciones de respuesta estructural.
- Proporcionar una herramienta práctica para el análisis de incertidumbre en la seguridad de infraestructuras críticas.
Principales métodos:
- Introducción del algoritmo de aprendizaje global sensible a la cola (TS-GL).
- TS-GL cuenta con un mecanismo de búsqueda centrado en la cola y una nueva función de peso.
- Investigación de las funciones de activación para la eficiencia computacional.
Principales resultados:
- TS-GL mejora significativamente la estimación de las probabilidades de cola unilateral en comparación con los métodos existentes.
- El algoritmo fue validado en la relación de enlace-deslizamiento en conexiones de acero-concreto.
- TS-GL demostró una precisión y eficiencia superiores a los metamodelos del proceso gaussiano basado en el aprendizaje activo (AL-GP) para la cuantificación de eventos raros.
Conclusiones:
- El algoritmo TS-GL ofrece una solución práctica y efectiva para el análisis de incertidumbre en infraestructura crítica.
- Este nuevo marco mejora la operación de seguridad y el mantenimiento de megaestructuras.
- Una mejor estimación de las probabilidades de eventos raros es crucial para prevenir fallas estructurales.
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