İÇİ BETON DOLU DAİRESEL KESİTLİ ÇELİK BORULARIN EKSENEL YÜK KAPASİTELERİNİN YAPAY SİNİR AĞLARI VE RASSAL ORMAN YÖNTEMLERİ İLE TAHMİNİ

Abstract

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.

Keywords

• İçi Beton Dolu Çelik Boru
• Eksenel Yük Kapasitesi
• Makine Öğrenmesi

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

Abstract

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.

Keywords

• Concrete Filled Steel Tube
• Axial Load Capacity
• Machine Learning.

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