PCA-Based Hybrid Intelligence Models for Estimating the Ultimate Bearing Capacity of Axially Loaded Concrete-Filled Steel Tubes

In order to forecast the axial load-carrying capacity of concrete-filled steel tubular (CFST) columns using principal component analysis (PCA), this work compares hybrid models of artificial neural networks (ANNs) and meta-heuristic optimization algorithms (MOAs). In order to create hybrid ANN model...

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Detalles Bibliográficos
Autores principales: Khan, Kaffayatullah, Biswas, Rahul, Gudainiyan, Jitendra, Amin, Muhammad Nasir, Qureshi, Hisham Jahangir, Arab, Abdullah Mohammad Abu, Iqbal, Mudassir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503460/
https://www.ncbi.nlm.nih.gov/pubmed/36143788
http://dx.doi.org/10.3390/ma15186477

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503460/
https://www.ncbi.nlm.nih.gov/pubmed/36143788
http://dx.doi.org/10.3390/ma15186477

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