Makine Öğrenimi Yöntemleriyle Bazaltlarda Tek Eksenli Sıkışma Dayanımının Değerlendirilmesi ve Performanslarının Karşılaştırılması

Year 2023, Volume: 11 Issue: 2, 1059 - 1074, 30.04.2023

Nurgül Gültekin , Ayhan Doğan

https://doi.org/10.29130/dubited.1173624

Cited By: 1

Abstract

Tek eksenli sıkışma dayanımı (UCS) mühendislik projelerinde en önemli tasarım parametrelerinden biri olup; bir çok projede ve sınıflama sistemlerinde doğrudan kullanılan bir parametredir. UCS’nin elde edilmesindeki güçlükler göz önüne alındığında; makine öğrenimi temelli yaklaşımlar ile tahmin edilmesi dikkat çekmektedir. Çalışma kapsamında bazalt bloklarından alınan 137 adet karot örneği üzerinde gerçekleştirilen laboratuvar deney sonuçları kullanılarak iki ayrı model elde edilmiştir. Bu modellerde görünür gözeneklilik (n), p dalga hızı (Vp) ve birim hacim ağırlık (n) değerleri girdi parametreleri olup; makine öğrenimi yöntemleri ile UCS tahmin edilmeye çalışılmıştır. Bu amaçla; Gauss Süreç Regresyonu (GSR), Destek Vektör Makineleri (DVM) ve Ağaç Toplulukları Yöntemleri (AT) olmak üzere üç farklı makine öğrenimi yöntemi kullanılmıştır. İki ayrı modele ait beş farklı veri seti için uygulanan üç ayrı makine öğrenimi yönteminin performanslarının değerlendirmesinde R2 (determinasyon katsayısı), RMSE (kök ortalama kare hata), MSE (ortalama kare hata) ve MAE (ortalama mutlak hata) performans indisleri kullanılmıştır. Buna göre; genel olarak üç ayrı makine öğrenimi yönteminin de UCS’ nin tahmininde başarılı olduğu değerlendirilmiş olmakla birlikte AT yönteminin genel olarak daha yüksek tahmin performansı verdiği belirlenmiştir.

Keywords

Tek eksenli sıkışma dayanımı, Destek vektör makineleri, Gauss süreç yönetimi, Ağaç toplulukları

Evaluation of Uniaxial Compressive Strength of Basalts using Machine Learning Methods and Comparison of Their Performances

Abstract

Uniaxial compressive strength (UCS) is one of the most critical design parameters of engineering projects, which is directly used parameter in many projects and classification systems. Considering the difficulties in obtaining the UCS, it is remarkable that it is estimated using machine learning-based approaches. In this study, two different models were constructed using laboratory results of the 137 core samples. Apparent porosity (n), p wave velocity (Vp), and unit weight (n) values are the input parameters in these models; the UCS was tried to estimated by machine learning-based methods. For this purpose, three different machine learning methods, such as Gaussian Process Regression (GSR), Support Vector Machine (SVM), and Ensembles of Tree (ET) were employed. R2 (Coefficient of Determination), RMSE (Root Mean Square Error), MSE (Mean Square Error), and MAE (Mean Absolute Error) performance indices were used to evaluate the performances of three different machine learning methods for five different data sets of two different models. According to these assessments, it was determined that all three different machine learning methods were successful for estimating UCS in general; however, the ET method generally had higher prediction performance.

Keywords

Uniaxial compressive strength, Support vector machine, Gaussian process regression, Ensembles of tree

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