Beton-Dolgulu Çelik Tüplü Kompozit Kolonların Nihai Eksenel Yük Taşıma Kapasitesi Tahmininde MARS, RVM ve ANN-Tabanlı Modellenmesinin Karşılaştırılması

Abstract

Beton-dolgulu çelik tüplü kompozit kolonlar (BDÇTKK), özellikle büyük eğilme rijitlikleri, süneklik ve enerji sönümleme kapasitesi bakımından yapı davranışını iyileştirici/geliştirici bir tercih haline gelmiştir. Çok değişkenli adaptif regresyon eğrileri (MARS), ilgililik vektör makinesi (RVM), ve yapay sinir ağları (ANN)-tabanlı modellere dayalı pratik tasarım metodolojisi yaklaşımları arasındaki karşılaştırmalar Avcı Karataş tarafından önceki çalışmalarında sunulmuştur. Bu araştırma makalesinde, literatürde geliştirilmiş üç boyutlu (3D) doğrusal olmayan sonlu elemanlar yöntemi (FEM) (3D-FEM) ve basitleştirilmiş sayısal/numerik modelleme (NM) sonuçları, dairesel ve kısa/stub BDÇTKK’ın nihai yük taşıma kapasitesinin tahmin edilmesine yönelik yazarın söz konusu bu çalışmalarında sunulan hesaplama yöntemleriyle karşılaştırılmıştır. Modellemede daha doğru bir tahmin sağlamak için dairesel BDÇTKK'ın geometrik ve mekanik özelliklerinden kapsamlı bir deneysel veri seti/kümesi sunulmuştur. Kompozit kolon yükseklik, kesit çapı, çelik tüp et kalınlığı, çelik akma ve kuşatılmamış beton basınç dayanımları, çelik ve beton elastisite modülü parametreleri, deneysel veri setinin geometrik ve malzeme karakteristikleridir. Dairesel kısa BDÇTKK’ın, 3D-FEM, NM ile MARS, RVM ve ANN-tabanlı modellemeye dayalı tahmin edilen nihai eksenel basınç yükü kapasitesinin, deneysel olarak ölçülen değerlerle karşılaştırılabilir olduğu, bu özgün çalışma kapsamında detaylı olarak incelenmiştir. Tahmin edilen ile deneysel nihai eksenel yük oranlarının minimum ve maksimum değerleri, MARS-tabanlı modelde, ((P_u^MARS)⁄(P_u^E )), 0.87 ile 1.10 aralığında, RVM-tabanlı modelde, (P_u^RVM/P_u^E), 0.90 ile 1.06 arasında, ANN-tabanlı modelde, ((P_u^ANN)⁄(P_u^E )), 0.92 ile 1.04 arasında değiştiği bulunmuştur. MARS ve RVM-tabanlı modeller kadar güçlü istatistiksel modelleme araçlarından biri olan ANN-tabanlı modellemeden, bu makale kapsamında incelenen deneysel veri sonuçlarıyla en uyumlu ve yakın performans sonuçları elde edilmiştir.

Keywords

• Çok değişkenli adaptif regresyon eğrileri (MARS)
• İlgililik vektör makinesi (RVM)
• Yapay sinir ağı (ANN)
• Doğrusal olmayan sonlu elemanlar modeli (FEM)
• Beton-dolgulu çelik tüplü kompozit kolonlar

Comparing MARS, RVM, and ANN-based modeling approaches with existing computation approaches for estimating the ultimate capacity of concrete-filled steel tube composite columns

Abstract

The ductility and energy absorption characteristics of concrete-filled steel tube columns (CFSTCCs) make these columns a good choice. In this paper, the three-dimensional (3D) nonlinear finite element method (FEM) (3D-FEM) modeling, and simplified numerical modeling results, are compared to those of the computation methods presented in previous studies on estimating the ultimate load capacity of circular stub concrete-filled steel tube composite columns (CFSTCCs). Another comparison between practical design methodology approaches based on advanced analyses, namely, multivariate adaptive regression splines (MARS), relevance vector machine (RVM), and artificial neural network (ANN)-based models were also presented by Avci-Karatas. In order to improve the accuracy of the modeling process and achieve more precise predictions, a thorough set of experimental data was collected. This data encompassed the geometrical and mechanical properties of circular CFSTCC, including parameters such as height, diameter, thickness, steel yield stress, unconfined concrete strength, and Young's modulus for steel. In the present study, it is found that the predicted ultimate axial compression load capacity of circular stub CFSTCCs based on 3D-FEM, numerical modeling, and MARS, RVM, and ANN-based modeling is comparable with the experimentally measured values. In the MARS-based model, the minimum and maximum values of the predicted-to-experimental ultimate axial load ratios ((P_u^MARS)⁄(P_u^E )) were found to range from 0.87 to 1.10. For the RVM-based model, the ratios (P_u^RVM/P_u^E) varied between 0.90 and 1.06. Similarly, in the ANN-based model, the ratios ((P_u^ANN)⁄(P_u^E )) ranged from 0.92 to 1.04. As powerful statistical modeling tools as MARS- and RVM-based models are, ANN-based models, achieve high computational efficiency in terms of accuracy in the context of this paper.

Keywords

• Multivariate adaptive regression splines (MARS)
• Relevance vector machine (RVM)
• Artificial neural network (ANN)
• Nonlinear finite element model (FEM)
• Concrete-filled steel tube composite columns

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