A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns

In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas the OSS was used to optimize the weights and bias of...

Descripción completa

Detalles Bibliográficos
Autores principales: Nguyen, Quang Hung, Ly, Hai-Bang, Tran, Van Quan, Nguyen, Thuy-Anh, Phan, Viet-Hung, Le, Tien-Thinh, Pham, Binh Thai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436240/
https://www.ncbi.nlm.nih.gov/pubmed/32751914
http://dx.doi.org/10.3390/molecules25153486

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436240/
https://www.ncbi.nlm.nih.gov/pubmed/32751914
http://dx.doi.org/10.3390/molecules25153486

Ejemplares similares