Kayaçların termal iletkenliğinin regresyon analizleri ve yapay sinir ağları kullanılarak değerlendirilmesi

Öz

Bu çalışma, doğal taşların (𝑘) termal iletkenliğini regresyon analizleri ve yapay sinir ağları (YSA) yoluyla araştırmıştır. Bu amaçla, yukarıda belirtilen analiz yöntemleri için çok sayıda veri seti derlemek için kapsamlı bir literatür araştırması yapılmıştır. Kuru yoğunluk (ρd), etkin gözeneklilik (ne), tek eksenli basınç dayanımı (UCS) ve darbe dalga hızı (Vp), gibi farklı fizikomekanik kaya özelliklerine dayanarak, 𝑘'nin değerlendirilmesi için yedi tahmin modeli (M1-M7) kurulmuştur. Regresyona dayalı modeller (M1-M5), dikkate alınan kaya özelliklerinin doğal taşların 𝑘 değerini farklı derecelerde etkilediğini göstermiştir. Özellikle, ne ve Vp'nin doğal taşların 𝑘'sini tahmin etmek için yüksek oranda bağıntılı parametreler olduğu bulunmuştur. Kurulan modellerin performansı da çeşitli istatistiksel göstergeler kullanılarak değerlendirilmiştir. İstatistiksel değerlendirmeler, YSA tabanlı modellerin (M6, M7), M1-M5 modellerinden daha tutarlı sonuçlar verdiğini göstermiştir. Ayrıca, kullanıcıların bunları daha verimli bir şekilde uygulayabilmeleri için YSA tabanlı modeller için matematiksel ifadeler de bu çalışmada verilmiştir. Böylelikle, bu çalışmanın, doğal taşların ısı iletimi hakkında pratik ve anlaşılır bilgiler sağlayacağı düşünülmektedir ve farklı kaya özelliklerinin bir fonksiyonu olarak doğal taşların 𝑘'sının nasıl modelleneceğine dair bir vaka çalışması olarak tanımlanabilir.

Anahtar Kelimeler

• Isı iletkenliği
• Doğal taş
• regresyon analizi
• Yapay sinir ağları

Assessment of thermal conductivity of rocks using regression analyses and artificial neural networks

Abstract

This study investigated the thermal conductivity of natural stones (𝑘) through regression analyses and artificial neural networks (𝐴𝑁𝑁). In order to gather a sizable number of datasets for the aforementioned analytic methodologies, a thorough literature review was carried out. Based on different physicomechanical rock characteristics, like dry density (𝜌𝑑), effective porosity (𝑛𝑒), uniaxial compressive strength (𝑈𝐶𝑆), and pulse wave velocity (𝑉𝑝), seven estimated models (M1-M7) were created for the evaluation of 𝑘. The regression-based models (M1-M5) demonstrated that the considered rock properties influence the 𝑘 of natural stones at different degrees. Notably, the 𝑛𝑒 and 𝑉𝑝 were found to be highly correlative parameters for estimating the 𝑘 of natural stones. A number of statistical indicators were used to assess the performance of the developed models. The statistical evaluations indicated that the ANN-based models (M6, M7) provided more consistent results than the M1-M5 models. In addition, the mathematical expressions for ANN-based models were also given in the present study to let users carry out them more efficiently. In this case, this study is thought to ensure applicable and comprehensible information on the heat conduction of natural stones and can be described as a research study on how to model the 𝑘 of natural stones as a factor of various rock characteristics.

Keywords

• Thermal conductivity
• Natural stone
• Regression analysis
• Artificial neural networks

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