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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2026 / Cilt: 20 Sayı: 59

KAYA SINIFLAMA SİSTEMLERİNİN REGRESYON VE SİNİR AĞLARI TEKNİĞİ İLE İLIŞKİLENDİRİLMESİ

REGRESSION ANALYSIS AND NEURAL NETWORK FITTING OF ROCK MASS CLASSIFICATION SYSTEMS

İbrahim Ferid Öge
Pages
354–368
Publication Date
2018-05-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Implementing the Neural Network fitting also overcame the scatter observed in the regression analysis

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Commonly used rock mass classification systems, Rock Mass Rating (RMR), Q-System, and Geological Strength Index (GSI) were used as input for simple regression and Neural-Network fitting.
📊
Key Findings & Data:The necessary data for this study, consisting of 250 sets of rock mass classification ratings, were collected from an excavation of an underground mine opening during a time interval of more than two years.
💡
Conclusion & Impact:Implementing the Neural Network fitting also overcame the scatter observed in the regression analysis
🔬Design:Makale
📍Scope:Comprehensive Sample
⚙️Variables:Özet: Yaygın olarak kullanılan kaya kütle sınıflama sistemlerinden sınıflaması · Kaya Kütle Puanı

Abstract

Commonly used rock mass classification systems, Rock Mass Rating (RMR), Q-System, and Geological Strength Index (GSI) were used as input for simple regression and Neural-Network fitting. The relationship between the classification systems can be used for the estimation of unknown classification ratings. The necessary data for this study, consisting of 250 sets of rock mass classification ratings, were collected from an excavation of an underground mine opening during a time interval of more than two years. The rock mass data belongs to the Pliocene-aged Deniş formation in Soma region of Manisa/Turkey. The ratings, basic and adjusted RMR, Q, Q', and GSI were chosen for the simple regression. Three of the equations are suggested to be taken into account due to their strong correlation of determination. These equations can be utilized especially if the rating Q is known and the adjusted RMR is intended to be estimated. Additionally, basic RMR rating can be estimated by considering the GSI as an input. Utilization of the Neural Networks resulted in an improved prediction capability with a greater predicted-measured coefficient of determination. Implementing the Neural Network fitting also overcame the scatter observed in the regression analysis

Özet

seçilmiştir. Bunlar arasından en yüksek determinasyon katsayısına sahip olan üç eşitliğin dikkate alınması önerilmektedir. Eşitlikler, özellikle Q puanı bilindiğinde ve düzeltilmiş RMR’nin kestirilmesinde faydalanılabilir. İlave olarak, Temel RMR puanı GSI kullanılarak kestirilebilmektedir. Sinir Ağı en iyileme uygulaması, iyileştirilmiş bir kestirim imkanını daha yüksek determinasyon katsayısı ile sağlamıştır. Sinir ağları en iyileme uygulaması, regresyonlardaki gözlemlenen saçınımın üstesinden gelinmesini de sağlamıştır

🔖

Cite This Article

Öge, İ. F. (2018). KAYA SINIFLAMA SİSTEMLERİNİN REGRESYON VE SİNİR AĞLARI TEKNİĞİ İLE İLIŞKİLENDİRİLMESİ. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 20 Sayı: 59, 354–368. https://ajindex.com/articles/1203780

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