Beton-dolgulu çelik tüplü kompozit kısa kolonların nihai yük taşıma kapasitesinin ilgililik vektör makinesine dayalı tahmini için modelleme yaklaşımı

Year 2021, Volume: 10 Issue: 2, 615 - 626, 27.07.2021

Çiğdem Avcı-karataş

https://doi.org/10.28948/ngumuh.759297

Cited By: 4

Abstract

Bu makalede, dairesel kesitli beton-dolgulu çelik tüplü kompozit kısa kolonların eksenel basınç yükleri altındaki nihai yük taşıma kapasitesini tahmin etmekte ilgililik vektör makinesinin (İVM) uygulanabilirliği incelenmiştir. Destek vektör makinesinin bir eklentisi olarak İVM, regresyon ve sınıflandırmada sağlam çözümler elde etmek için Bayesyen yaklaşımını kullanmaktadır. MATLAB yazılımı ve 150 adet daha önceki çalışmalarda sunulan kapsamlı deneysel veriler kullanılarak ve bu verilerin uygun şekilde düzenlenmesiyle, dairesel kesitli beton-dolgulu çelik tüplü kompozit kısa kolonların nihai yük taşıma kapasitesini tahmin etmek için bir model geliştirilmiştir. Verilerin düzenleme ve doğrulama için gruplandırılmasında azami özen gösterilmiştir. Sırasıyla, düzenleme için yaklaşık %80 veri seti ve doğrulama için %20 veri seti kullanılmıştır. Sonuçlar, beton-dolgulu çelik tüplü kompozit kolon elemanının tahmini nihai eksenel basınç yük taşıma kapasitesinin, ilgili deneysel verilerle kıyaslanabilir olduğunu ve aradaki yüzde farkının yaklaşık ∓%11 olduğunu göstermektedir.

Keywords

Beton-dolgulu çelik tüplü kompozit kısa kolonlar, Nihai eksenel yük taşıma kapasitesi, İlgililik vektör makinesi (İVM), Lineer olmayan regresyon algoritması

Modeling approach for estimation of ultimate load capacity of concrete-filled steel tube composite stub columns based on relevance vector machine

Abstract

In this paper, the applicability of relevance vector machine (RVM) has been explored to predict the ultimate axial load capacity of concrete-filled steel tube composite stub columns (CFSTCSCs) with circular sections under axial compression loadings. As an extension of support vector machine, RVM employs Bayesian inference to achieve parsimonious solutions for regression and classification. By using MATLAB software and 150 comprehensive experimental data presented in the previous studies, a model to predict the ultimate load of circular CFSTCSCs was developed by properly training the data. Utmost care has been taken in grouping the data for training and validation. About 80% dataset for training and 20% dataset for validation have been used, respectively. The results show that the predicted ultimate axial compression load capacity of CFSTCSC members is comparable with that of the corresponding experimental data and the percentage difference is about ∓11%.

Keywords

Concrete-filled steel tube composite stub columns (CFSTCSCs), Ultimate axial load capacity, Relevance vector machine (RVM), Nonlinear regression algorithm

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