MODELING NON-LINEAR RELATIONSHIPS IN ROCK MASS RATING (RMR) SYSTEM: A METHODOLOGICAL STACKING-BASED HYBRID APPROACH

Abstract

Rock Mass Rating (RMR) is a fundamental classification system in rock engineering. However, the traditional RMR method relies on a simple arithmetic summation of parameters, often failing to fully capture complex non-linear interactions and field uncertainties. To overcome these limitations, this study proposes a novel stacking-based hybrid prediction model. Three machine learning algorithms with distinct paradigms—Random Forest (RF), Gradient Boosting (XGBoost), and Support Vector Regression (SVR)—were optimized via Bayesian Optimization. They were trained on a 2000-sample dataset generated through Monte Carlo simulation, which strictly preserved physical correlations. The proposed hybrid model achieved high predictive accuracy (R2 = 0.928, RMSE = 4.56), demonstrating significant superiority over the traditional RMR method (R2 = 0.842, RMSE = 6.76). Furthermore, feature importance analysis challenged conventional assumptions by identifying "discontinuity condition" as the most critical parameter affecting the RMR score, followed by rock quality designation (RQD). Ultimately, this study strongly supports that multi-model hybrid architectures offer a more flexible, precise, and reliable alternative to classical summation methods in rock mechanics.

Keywords

• Rock Mass Rating (RMR)
• Machine Learning
• Stacking.
• Monte Carlo simulation
• Bayesian optimization

KAYA KÜTLE DERECESİ (RMR) SİSTEMİNDE DOĞRUSAL OLMAYAN İLİŞKİLERİN MODELLENMESİ: METODOLOJİK YIĞINLAMA TABANLI HİBRİT YAKLAŞIM

Abstract

Kaya Kütle Derecesi (RMR), kaya mühendisliğinde en yaygın kullanılan sınıflandırma sistemlerinden biridir. Ancak geleneksel RMR yöntemi, parametrelerin basit aritmetik toplamına dayandığı için doğadaki karmaşık ve doğrusal olmayan ilişkileri tam olarak yansıtamamaktadır. Bu çalışmada, klasik yöntemlerin sınırlarını aşmak amacıyla yığınlama (stacking) tabanlı hibrit bir tahmin modeli geliştirilmiştir. Çalışma kapsamında; hiperparametreleri Bayesian optimizasyon ile ayarlanan Rastgele Ormanlar (RF), Gradyan Artırma (XGBoost) ve Destek Vektör Regresyonu (SVR) algoritmaları kullanılmıştır. Bu modeller, fiziksel etkileşimlerin korunduğu ve saha belirsizliklerinin Monte Carlo simülasyonu ile modellendiği 2000 adetlik sentetik bir veri seti üzerinde eğitilmiştir. Elde edilen bulgulara göre, geliştirilen hibrit model (R2 = 0,928 ve RMSE = 4,56), geleneksel RMR yöntemine (R2 = 0,842 ve RMSE = 6,76) kıyasla belirgin bir üstünlük sağlamıştır. Ayrıca öznitelik önem analizinde, literatürdeki genel kanının aksine RMR puanını en çok etkileyen parametrenin "süreksizlik durumu" olduğu, bunu "kaya kalite indeksi (RQD)"nin izlediği tespit edilmiştir. Sonuç olarak bu çalışma; kaya mekaniğinde çoklu model mimarisine dayalı hibrit yaklaşımların, klasik toplama yöntemlerine göre daha esnek, hassas ve güvenilir bir alternatif sunduğunu güçlü biçimde desteklemektedir.

Keywords

• Kaya Kütle Derecesi (RMR)
• Makine Öğrenimi
• Yığınlama
• Monte Carlo Simulasyonu
• Bayesian Optimizasyonu

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