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Machine Learning Prediction of Flexural Behavior of UHPFRC

To evaluate the possibility of predicting the flexural behaviour of UHPFRC, four analytical models were developed, based on artificial neural networks (ANN), to predict the first cracking tension or Limit of Proportionality (LOP), its corresponding deflection (δLOP), ultimate strength or Modulus of...

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Bibliographic Details
Published in:RILEM Bookseries
Institution:Escuela Colombiana de Ingeniería
Main Authors: Abellán García, Joaquín, Fernández Gómez, Jaime A., Torres Castellanos, Nancy, Núñez López, Andrés M., Estructuras y Materiales
Format: Capítulo - Parte de Libro
Language:English
Published: Springer Nature 2020
Subjects:
Aprendizaje automático (Inteligencia artificial)
Hormigón armado
Análisis estructural (Ingeniería)
Flexibilidad
Resistencia de materiales
Machine learning
Reinforced concrete
Structural analysis (Engineering)
Flexure
Strength of materials
UHPFRC
LOP
MOR
PCA
Online Access:https://repositorio.escuelaing.edu.co/handle/001/1811
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