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

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...

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Bibliographic Details
Main Authors: Quang Hung Nguyen, Hai-Bang Ly, Van Quan Tran, Thuy-Anh Nguyen, Viet-Hung Phan, Tien-Thinh Le, Binh Thai Pham
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Molecules
Subjects:
concrete-filled steel tube column
machine learning
neural network
one step secant algorithm
optimization
Online Access:https://www.mdpi.com/1420-3049/25/15/3486

Internet

https://www.mdpi.com/1420-3049/25/15/3486

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