Farklı Numune Geometrileri Arasında Tek Eksenli Basınç Dayanımı Tahmini İçin Makine Öğrenmesine Dayalı Dönüşüm Modeli Geliştirilmesi
Authors/Creators
Description
Tek eksenli basınç dayanımı (TBD), mühendislik yapılarının tasarımı ve inşası, yeraltı kazıları, şev stabilitesi gibi projelerde kaya mühendisliği için en önemli tasarım parametrelerinden biridir. TBD testi için gerekli olan numunelerin bazı ulusal ve uluslararası standartlarda önerildiği şekilde hazırlanması gerekmektedir. Özellikle kaya yapısı zayıf ve kırılgan olduğunda istenilen boyutlarda ve sayıda numune temin edebilmek mümkün olmamaktadır. Böyle durumlarda, farklı standartlarda önerilmiş alternatif numune boyutları ve geometrileri tercih edilebilmektedir. Numune şekli ve boyutunun TBD üzerindeki etkisi literatürde uzun süredir araştırılmakta olup, bu etki büyük ölçüde anlaşılmıştır. Ancak farklı geometrilere sahip numunelerden elde edilen TBD değerleri arasındaki ilişkinin literatürde ortaya konmadığı görülmektedir. Özellikle kaya örnekleri üzerinde yapılan karşılaştırmalı çalışmaların olmadığı fark edilmiştir. Literatürde önerilen bazı dönüşüm denklemlerinin, silindirik ve kübik numuneler arasındaki dayanım geçişini de yeterli doğrulukla tahmin edemediği belirlenmiştir. Bu çalışmada, kübik numunelerden elde edilen TBD değerlerinden silindirik numunelere ait değerlerin tahmin edilmesi amacıyla çeşitli makine öğrenmesi (ML) temelli regresyon algoritmaları uygulanmıştır. Doğrusal regresyon, ağaç tabanlı modeller, ansambl öğrenme yöntemleri, çekirdek tabanlı algoritmalar ve dayanıklı regresyon teknikleri karşılaştırmalı olarak değerlendirilmiştir. Modellerin performansı, 5 katlı çapraz doğrulama yöntemi ile belirlenmiş ve başarı ölçütleri olarak R², MAE, MAPE ve RMSE kullanılmıştır. Elde edilen bulgular, özellikle Huber Regressor ve SVR gibi modellerin yüksek doğrulukla tahmin sağladığını göstermekte; klasik dönüşüm katsayılarına kıyasla daha dar hata aralıkları ve güçlü genelleme yeteneği sunduklarını ortaya koymaktadır. Bu sonuçlar, makine öğrenmesi tabanlı modellerin, beton ve kaya gibi heterojen malzemelerde farklı numune geometrileri arasında TBD dönüşümünü sağlamak için etkin bir araç olabileceği sonucuna varılmıştır.
Abstract (English)
Uniaxial compressive strength (UCS) is one of the most critical design parameters in rock engineering applications, including the design and construction of engineering structures, underground excavations, and slope stability. Specimens required for UCS testing must be prepared in accordance with various national and international standards. However, when the rock structure is weak or brittle, obtaining the required number and size of specimens may not be feasible. In such cases, alternative specimen geometries and sizes recommended by different standards are often adopted. Although the influence of specimen shape and size on UCS has been extensively studied in the literature and is relatively well understood, the relationship between UCS values obtained from different geometries remains largely unexplored. Notably, there is a lack of comparative studies focusing specifically on rock samples. Furthermore, some transformation equations proposed in the literature have proven inadequate in accurately estimating the strength conversion between cylindrical and cubic specimens. In this study, various machine learning (ML)-based regression algorithms were applied to predict UCS values for cylindrical specimens using UCS values obtained from cubic specimens. A comparative evaluation was conducted using linear regression, tree-based models, ensemble learning methods, kernel-based algorithms, and robust regression techniques. Model performances were assessed through 5-fold cross-validation using R², MAE, MAPE, and RMSE as evaluation metrics. The findings reveal that models such as the Huber Regressor and Support Vector Regression (SVR) provided highly accurate predictions, with narrower error margins and stronger generalization capacity compared to classical transformation coefficients. These results suggest that ML-based models offer an effective and reliable approach offering a robust alternative to conventional transformation equations, especially in engineering contexts where direct experimental testing is limited or impractical.
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Additional details
Additional titles
- Translated title (English)
- Development of a Machine Learning-Based Conversion Model for Uniaxial Compressive Strength Prediction Between Different Specimen Geometries
References
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