Otomatik Üretim Teknolojisine Uygun Betonların Basınç Dayanımlarının Makine Öğrenmesi Yöntemiyle Belirlenmesi

Beton, yapı malzemeleri arasında mekanik özellikleri en karmaşık kompozitlerin başında gelmektedir. Betonun en temel mekanik özelliği ise basınç dayanımıdır. Betonarme yapılarda betonun basınç dayanımını hızlı, doğru, maliyetsiz ve zahmetsizce belirlemek oldukça önemli bir hedeftir. Bu çalışmada, beton basınç dayanımı belirtilen amaçlar göz önünde bulundurularak görüntü işleme ve makine öğrenmesi yöntemleri ile tahmin edilmektedir. Önerilen yöntemde uygulama yapılabilmesi için otomatik üretim teknolojisine uygun 48 adet küp numune hazırlanmıştır. Hazırlanan beton numunelerinde uçucu kül ve lif değişkenlerinin oluştuğu 6 farklı beton tipi kullanılmıştır. Beton numunelerinden elde edilen görüntüler ilk olarak ön işlem adımlarından geçerek veriseti hazırlanmıştır. Sonraki aşamada, beton görüntülerinin öznitelikleri Gri Seviye Eş Oluşum Matrisi (GLCM) yöntemi kullanılarak çıkarılmıştır. Elde edilen öznitelikler içinden rasgele seçilen %80’i eğitim, geri kalanı ise test seti olarak belirlenmiştir. Son adımda ise, K-En Yakın Komşu (KNN) algoritması uygulanarak betonda kullanılan malzemelerin değişimi ile beton basınç dayanımı ve görüntülerinde oluşan farklılıklar araştırılmıştır. Önerilen yöntem 100 kez tekrarlanarak doğruluk oranlarının ortalaması alınmıştır. Bulunan sonuçlar otomatik üretim teknolojisine uygun beton görüntüleri ile basınç dayanımı arasında %79.7’lik başarım oranı sağlanmıştır. Bu sayede gelişimini her geçen gün artıran otomatik üretim teknolojisine uygun yapıların betonun en önemli mekanik özelliklerinden biri olan basınç dayanımı tahminleri, görüntüler üzerinden yapılabileceği ortaya konulmuştur.

Anahtar Kelimeler:

Makine Öğrenmesi, Görüntü İşleme, Mekanik Özellikler, 3D Beton

Determination of Compressive Strength of Concretes Suitable for Automatic Production Technology by Machine Learning Method

Concrete is one of the most complex composites with mechanical properties among building materials. The most basic mechanical property of concrete is compressive strength. It is a very important goal to determine the compressive strength of concrete in reinforced concrete structures quickly, accurately, inexpensively and effortlessly. In this study, concrete compressive strength is estimated by image processing and machine learning methods, taking into account the mentioned purposes. In order to be able to apply the proposed method, 48 cube samples were prepared in accordance with automatic production technology. In the prepared concrete samples, 6 different concrete types were used in which fly ash and fiber variables were formed. The images obtained from the concrete samples were first prepared by going through the preprocessing steps. In the next step, the features of the concrete images were extracted using the Gray Level Co-occurrence Matrix (GLCM) method. Of the features obtained, 80% randomly selected were determined as training and the rest as test set. In the last step, by applying the K-Nearest Neighbor (KNN) algorithm, the changes in the materials used in the concrete and the differences in the concrete compressive strength and appearance were investigated. The proposed method was repeated 100 times and the average of the accuracy rates was taken. The results obtained showed a accuracy rate of 79.7% between the concrete images suitable for automatic production technology and the compressive strength. In this way, it has been revealed that the pressure strength estimations, which is one of the most important mechanical properties of concrete, can be made through images of structures suitable for automatic production technology, which increases its development day by day.

Keywords:

Machine Learning, Image Processing, Mechanical Properties, 3D Concrete,

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