Sedimanter Kayaçların Tek Eksenli Basınç Dayanımının Leeb Sertliği Kullanılarak Yapay Sinir Ağları ve SVM Regresyon Analizi ile Tahmin Edilmesi

Öz

Kayaların tek eksenli basınç dayanımı (UCS), yeraltı ve yerüstü mühendislik projelerinden önce yapıların tasarımı ve stabilitesi için belirlenmesi gereken bir kaya özelliğidir. Bununla birlikte, standartlaştırılmış numune hazırlama, gerekli ekipman vb. eksikliklerden dolayı kayaların UCS gibi özelliklerine doğrudan belirlemem mümkün olmamaktadır. Bu durumda, kayaçların UCS'si sertlik, ultrases hızı gibi indeks test yöntemleri ile tahmin edilir. Kayaçların sertliğini belirlemek diğer özelliklere göre nispeten daha pratik, hızlı ve ucuzdur. Bu çalışmada, sedimanter kayaçların UCS'si yapay sinir ağları (ANN) ve SVM regresyon analizi kullanılarak Leeb sertliğinin bir fonksiyonu olarak tahmin edilmiştir. Önerilen yapay sinir ağı ve SVM regresyon modelleri ile daha doğru ve hızlı tahmin değerleri elde edilmesi amaçlanmıştır. Çalışmada oluşturulan modellerin daha iyi eğitilmesi için literatürdeki çalışmalardan veriler derlenerek veri sayısı artırılmıştır. İki farklı yöntemle elde edilen modellerin tahmin ettiği UCS değerleri ile ölçülen UCS değerleri istatistiksel olarak karşılaştırılmıştır. ANN ve SVM regresyonu ile oluşturulan modellerin UCS değerlerini tahmin etmede güvenilir bir şekilde kullanılabileceği ortaya konmuştur.

Anahtar Kelimeler

• Leeb sertliği
• tek eksenli basınç dayanımı
• sedimanter kayaçlar
• yapay sinir ağı
• SVM regresyonu

Estimating Uniaxial Compressive Strength of Sedimentary Rocks with Leeb hardness Using SVM Regression Analysis and Artificial Neural Networks

Öz

Uniaxial compressive strength (UCS) of rock materials is a rock property that should be determined for the design and stability of structures before underground and aboveground engineering projects. However, it is impossible to determine the properties of rocks such as UCS directly due to the lack of standardized sample preparation, necessary equipment, etc. In this case, the UCS of rocks is estimated by index test methods such as hardness, ultrasound velocity, etc. Determining the hardness of rocks is relatively more practical, fast, and inexpensive than other properties. In this study, the UCS of sedimentary rocks was estimated as a function of Leeb hardness using artificial neural networks (ANN) and SVM regression analysis. With the proposed neural network and SVM regression models, it is aimed to obtain more accurate and faster prediction values. To better train the models created in the study, the number of data was increased by compiling data from the studies in the literature. The UCS values predicted by the models obtained with two different methods and the measured UCS values were statistically compared. It was proved that the models created with ANN and SVM regression can be used reliably in predicting UCS values.

Anahtar Kelimeler

• Leeb hardness
• uniaxial compressive strength
• sedimentary rocks
• artificial neural network
• SVM regression

Kaynakça

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