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Assessment of Progressive Collapse Resistance in Reinforced Concrete Structures Using Machine Learning Models
Evaluación de la resistencia al colapso progresivo en estructuras de hormigón armado mediante modelos de aprendizaje automático
DOI:
https://doi.org/10.15446/ing.investig.120363Keywords:
progressive collapse, machine learning, reinforced concrete, nonlinear structural analysis (en)colapso progresivo, aprendizaje automático, hormigón armado, análisis estructural no lineal (es)
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Ensuring structural integrity in buildings and infrastructure under extreme loading conditions represents a pivotal challenge in modern civil engineering. Exposure to natural disasters, accidental impacts, and deliberate attacks can result in the application of unprecedented stresses, which may ultimately lead to progressive collapse and catastrophic failures. While traditional analytical methods are reliable, they often prove inadequate in meeting the increasing demand for rapid and accurate assessments in complex scenarios. However, recent advances in computational tools, particularly machine learning (ML), offer a new approach to address these challenges. In this study, nonlinear static analyses of 250 reinforced concrete systems are conducted within pushdown procedures. Load factor and vertical drift capacities of systems are obtained and accepted as target outputs for ML based predictions. By leveraging data-driven models, it becomes possible to predict structural behavior under extreme conditions with greater precision and efficiency. This study builds on this emerging field, aiming to provide novel insights into collapse mechanisms and robustness through advanced machine learning techniques.
Garantizar la integridad estructural de los edificios y las infraestructuras en condiciones de carga extremas representa un reto fundamental en la ingeniería civil moderna. La exposición de las estructuras a desastres naturales, impactos accidentales y ataques deliberados puede dar lugar a la aplicación de tensiones sin precedentes, que en última instancia pueden provocar un colapso progresivo y fallos catastróficos. Aunque los métodos analíticos tradicionales son fiables, a menudo resultan inadecuados para satisfacer la creciente demanda de evaluaciones rápidas y precisas en escenarios complejos. Sin embargo, los recientes avances en herramientas computacionales, en particular el aprendizaje automático, ofrecen un nuevo enfoque para abordar estos retos. En este estudio, se llevan a cabo análisis estáticos no lineales de 250 sistemas de hormigón armado mediante procedimientos de empuje hacia abajo. El factor de carga y las capacidades de deriva vertical de los sistemas se obtienen y aceptan como resultados objetivo para las predicciones basadas en técnicas de aprendizaje automático. Al aprovechar los modelos basados en datos, es posible predecir el comportamiento estructural en condiciones extremas con mayor precisión y eficiencia. Este estudio se basa en este campo emergente y tiene como objetivo proporcionar nuevos conocimientos sobre los mecanismos de colapso y la robustez mediante técnicas avanzadas de aprendizaje automático.
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Copyright (c) 2026 Efe Selman, Çağatay Berke Erdaş
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