Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression

Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression

Abstract Concrete-filled steel tube columns (CFSTCs) are important elements in the construction sector and predictive analysis of their behavior is essential. Recent works have revealed the potential of metaheuristic-assisted approximators for this purpose. The main idea of this paper, therefore, is...

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Bibliographic Details
Main Authors: Yuzhen Liu, Yan Liang
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Subjects:
Soft computing
Neural network
Metaheuristic algorithms
Concrete column; Satin bowerbird optimizer
Online Access:https://doi.org/10.1038/s41598-024-58756-7

Internet

https://doi.org/10.1038/s41598-024-58756-7

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