Estimación de la respuesta probabilística de edificios de acero utilizando redes neuronales artificiales
Resumen
Resumen
Se presenta un enfoque para obtener con una precisión aceptable factores de transformación probabilísticos mediante el entrenamiento de un modelo de red neuronal artificial (RNA). Los factores de transformación probabilísticos se definen como la relación entre la respuesta sísmica de estructuras de múltiples grados de libertad (SMGL) y sus sistemas equivalentes de un grado de libertad (S1GL), asociadas a una tasa de excedencia anual prescrita. El enfoque se utiliza para predecir la respuesta sísmica de edificios de acero. Se proponen ecuaciones útiles para obtener factores de transformación probabilísticos en términos de la ductilidad y la distorsión máxima de entrepiso, se establecen como función de la tasa media anual de excedencia y del período fundamental de vibración. Se muestra que las redes neuronales artificiales son una herramienta útil para los procedimientos de diseño sísmico basados en la confiabilidad estructural y para mejorar la próxima generación de metodologías de diseño sísmico.
Abstract
An approach to obtain with acceptable accuracy probabilistic response transformation factors by training an artificial neural network (ANN) model is presented. The transformation factors are defined as the ratio of the seismic response of multi-degree-of-freedom structures and their equivalent single-degree-of-freedom systems, associated with a given annual exceedance rate. The approach is used for predicting the seismic response of steel framed buildings. Closed-form expressions to obtain probabilistic response transformation factors for maximum ductility and inter-story drift, as functions of their mean annual rate of exceedance, and of the fundamental vibration period of the structure, are proposed. It is shown that artificial neural networks are a useful tool for reliability-based seismic design procedures of framed buildings and for the improvement toward the next generation of earthquake design methodologies based on structural reliability.
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Revista Ingeniería y Tecnología UAS, Año 3 (2020), No. 3, es una publicación semestral editada por la Facultad de Ingeniería Culiacán, Calzada de las Américas y Universitarios, Ciudad Universitaria, s/n, Código Postal: 80040, Culiacán Rosales, Sinaloa. Teléfono: +52 (667) 7134053, http://ingenieria.uas.edu.mx, Correo Electrónico: karlalopez@uas.edu.mx Universidad Autónoma de Sinaloa. Editor responsable: Edén Bojórquez Mora. Reserva de Derechos al Uso Exclusivo: 04-2020-010915343800-203. ISSN: 2683-2445, otorgado por el Instituto Nacional de Derechos de Autor. Las opiniones expresadas por los autores no necesariamente reflejan la postura del editor de la publicación.