PREDICTION OF AXIAL LOAD CAPACITY OF CONCRETE-FILLED STEEL TUBES WITH CIRCULAR SECTIONS UNDER AXIAL LOAD BY USING ARTIFICIAL NEURAL NETWORKS AND RANDOM FOREST METHODS

Cumhur Cosgun

Makale detayları

PREDICTION OF AXIAL LOAD CAPACITY OF CONCRETE-FILLED STEEL TUBES WITH CIRCULAR SECTIONS UNDER AXIAL LOAD BY USING ARTIFICIAL NEURAL NETWORKS AND RANDOM FOREST METHODS

Yazarlar:

Cumhur Cosgun

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

İnşaat Mühendisliği

Dergi:

Mühendislik Bilimleri ve Tasarım Dergisi Vol.11 Issue 2, pp.564-574

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001291979

pISSN:

1308-6693

Anahtar kelimeler:

Özet:

 This paper focuses on the prediction of the ultimate compressive capacity of axially loaded concrete-filled steel CFST tube section columns using machine learning ML techniques. The use of CFST columns in the construction industry has been popular due to their superior structural performance both under axial loads as well as under lateral seismic loads. Studies carried out on the contribution of CFST members on lateral seismic resistance have revealed that the ideal combination of stiffness and ductility inherent in concrete and steel, respectively, results in superior performance under lateral loads. The evaluation of the load-carrying capacity of structural members ML-based predictive techniques has been popular among researchers. In this study, the case of CFST columns under axial loading is studied. The dataset needed for the prediction was acquired from the related existing research which provided the results of 215 experimental studies. The ML techniques that were used included two prevalent techniques namely Artificial Neural Networks ANN and Random Forest RF. In this study, the axial compressive capacity was predicted using these techniques and finally, the performances of the techniques were compared. Overall, the RF prediction technique was found to be in very close agreement with the experimental results acquired from the literature.

 This paper focuses on the prediction of the ultimate compressive capacity of axially loaded concrete-filled steel CFST tube section columns using machine learning ML techniques. The use of CFST columns in the construction industry has been popular due to their superior structural performance both under axial loads as well as under lateral seismic loads. Studies carried out on the contribution of CFST members on lateral seismic resistance have revealed that the ideal combination of stiffness and ductility inherent in concrete and steel, respectively, results in superior performance under lateral loads. The evaluation of the load-carrying capacity of structural members ML-based predictive techniques has been popular among researchers. In this study, the case of CFST columns under axial loading is studied. The dataset needed for the prediction was acquired from the related existing research which provided the results of 215 experimental studies. The ML techniques that were used included two prevalent techniques namely Artificial Neural Networks ANN and Random Forest RF. In this study, the axial compressive capacity was predicted using these techniques and finally, the performances of the techniques were compared. Overall, the RF prediction technique was found to be in very close agreement with the experimental results acquired from the literature.

 This paper focuses on the prediction of the ultimate compressive capacity of axially loaded concrete-filled steel CFST tube section columns using machine learning ML techniques. The use of CFST columns in the construction industry has been popular due to their superior structural performance both under axial loads as well as under lateral seismic loads. Studies carried out on the contribution of CFST members on lateral seismic resistance have revealed that the ideal combination of stiffness and ductility inherent in concrete and steel, respectively, results in superior performance under lateral loads. The evaluation of the load-carrying capacity of structural members ML-based predictive techniques has been popular among researchers. In this study, the case of CFST columns under axial loading is studied. The dataset needed for the prediction was acquired from the related existing research which provided the results of 215 experimental studies. The ML techniques that were used included two prevalent techniques namely Artificial Neural Networks ANN and Random Forest RF. In this study, the axial compressive capacity was predicted using these techniques and finally, the performances of the techniques were compared. Overall, the RF prediction technique was found to be in very close agreement with the experimental results acquired from the literature.

 Bu çalışmada, makine öğrenme teknikleri kullanılarak içi beton dolu dairesel kesitli çelik boruların BDÇK basınç altındaki nihai eksenel yük kapasiteleri tahmin edilmiştir. BDÇK kolonlar hem eksenel yükler, hem de yatay yükler altındaki performanslarından dolayı yapılarda çok tercih edilmektedirler. Bunun başlıca nedeni betonun ve çeliğin süneklilik ve rijitlik özelliklerinden kaynaklanmaktadır. Özellikle deprem etkisi altındaki yapısal elemanların davranışı yapının toptan davranışını etkilemektedir. Yapısal elemanların yük taşıma kapasitesinin makine öğrenme yöntemleri kullanılarak değerlendirilmesi araştırmacılar arasında oldukça popüler hale gelmiştir. Bu çalışma ile eksenel yük etkisi altındaki BDÇK kolonların eksenel yük kapasitesi yapay sinir ağları YSA ve rassal orman RO makine öğrenme yöntemleri kullanılarak tahmin edilmeye çalışılmış ve literatürdeki deney sonuçları ile karşılaştırılmıştır. Kapasite tahmini için literatürdeki 215 deney sonucu kullanılarak makine öğrenme yöntemleri arasında kıyaslama yapılmış, karşılaştırma sonucunda RO yönteminin daha iyi sonuç verdiği görülmüştür.

Makale detayları

PREDICTION OF AXIAL LOAD CAPACITY OF CONCRETE-FILLED STEEL TUBES WITH CIRCULAR SECTIONS UNDER AXIAL LOAD BY USING ARTIFICIAL NEURAL NETWORKS AND RANDOM FOREST METHODS

Yazarlar:

Cumhur Cosgun

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

İnşaat Mühendisliği

Dergi:

Mühendislik Bilimleri ve Tasarım Dergisi Vol.11 Issue 2, pp.564-574

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001291979

pISSN:

1308-6693

Concrete Filled Steel Tube

Axial Load Capacity

Machine Learning.İçi Beton Dolu Çelik Boru

Eksenel Yük Kapasitesi

Makine Öğrenmesi

Sayı

Makale detayları

PREDICTION OF AXIAL LOAD CAPACITY OF CONCRETE-FILLED STEEL TUBES WITH CIRCULAR SECTIONS UNDER AXIAL LOAD BY USING ARTIFICIAL NEURAL NETWORKS AND RANDOM FOREST METHODS

Yazarlar: Cumhur Cosgun

Yayıncı: Süleyman Demirel University

Ana Başlıklar: İnşaat Mühendisliği

Dergi: Mühendislik Bilimleri ve Tasarım Dergisi Vol.11 Issue 2, pp.564-574

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001291979

pISSN: 1308-6693

Concrete Filled Steel Tube

Axial Load Capacity

Machine Learning.İçi Beton Dolu Çelik Boru

Eksenel Yük Kapasitesi

Makine Öğrenmesi

Sayı

Yazarlar:

Cumhur Cosgun

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

İnşaat Mühendisliği

Dergi:

Mühendislik Bilimleri ve Tasarım Dergisi Vol.11 Issue 2, pp.564-574

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001291979

pISSN:

1308-6693