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

Nurgül Gültekin; Ayhan Doğan

Makale detayları

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

Yazarlar:

Nurgül Gültekin | Ayhan Doğan

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.11 Issue 2, pp.1059-1074

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001288325

pISSN:

2148-2446

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 Tek eksenli sıkışma dayanımı UCS mühendislik projelerinde en önemli tasarım parametrelerinden biri olup; bir çok projede ve sınıflama sistemlerinde doğrudan kullanılan bir parametredir. UCSnin 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.

Makale detayları

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

Yazarlar:

Nurgül Gültekin | Ayhan Doğan

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.11 Issue 2, pp.1059-1074

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001288325

pISSN:

2148-2446

Uniaxial compressive strength

Support vector machine

Gaussian process regression

Ensembles of treeTek eksenli sıkışma dayanımı

Destek vektör makineleri

Gauss süreç yönetimi

Ağaç toplulukları

Sayı

Makale detayları

Yazarlar: Nurgül Gültekin | Ayhan Doğan

Yayıncı: Duzce University

Ana Başlıklar: Mühendislik

Dergi: Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.11 Issue 2, pp.1059-1074

Belge Tipi: Makale

Yayın Tarihi: 4/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001288325

pISSN: 2148-2446

Sayı

Yazarlar:

Nurgül Gültekin | Ayhan Doğan

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.11 Issue 2, pp.1059-1074

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001288325

pISSN:

2148-2446