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Farklı Numune Geometrileri Arasında Tek Eksenli Basınç Dayanımı Tahmini İçin Makine Öğrenmesine Dayalı Dönüşüm Modeli Geliştirilmesi   
Yazarlar (5)
Dr. Öğr. Üyesi Murat IŞIK Dr. Öğr. Üyesi Murat IŞIK
Kırşehir Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA
Kırşehir Ahi Evran Üniversitesi, Türkiye
Deniz Akbay
Türkiye
Murat Sert
Afyon Kocatepe Üniversitesi, Türkiye
Doç. Dr. Gökhan EKİNCİOĞLU Doç. Dr. Gökhan EKİNCİOĞLU
Kırşehir Ahi Evran Üniversitesi, Türkiye
Devamını Göster
Özet
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 ...
Anahtar Kelimeler
Makale Türü Özgün Makale
Makale Alt Türü Diğer hakemli uluslarası dergilerde yayınlanan tam makale
Dergi Adı INTERNATIONAL JOURNAL OF ARCHITECTURE AND ENGINEERING
Dergi ISSN 2822-6895
Dergi Tarandığı Indeksler Road, EuroPub
Makale Dili Türkçe
Basım Tarihi 12-2025
Cilt No 5
Sayı 2
Sayfalar 295 / 307
Makale Linki https://e-arceng.com/index.php/arceng/article/view/61
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