# Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks  
[PERSON]  
1 Department of Civil Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz 5166616471, Iran 1  
[PERSON]  
2 Department of Mining Engineering, School of Mining and Geosciences, Nazarbayev University,  
Nur-Sultan 01000, Kazakhstan 2  
[PERSON]  
1 Department of Civil Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz 5166616471, Iran 1  
[PERSON]  
2 Department of Mining Engineering, School of Mining and Geosciences, Nazarbayev University,  
Nur-Sultan 01000, Kazakhstan 2  
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wave) velocity (\(V_{p}\)) [2; 4; 13], uniaxial compression strength (UCS) [2; 14], saturating fluid characteristic [15], quality and geometry of the contact between the grains [16; 17; 18], discontinuities [19], temperature [20; 21; 22; 23], and atmospheric pressure [24]. In situ measurements of TC need specialized equipment, which is very expensive and can be time-consuming [25]. However, due to the scale effect, even if the effects of the stress level, pore fluid pressure, temperature, and permeability of rocks can be considered, the laboratory values may substantially differ from those of the in situ measurements [26; 27; 24]. Therefore, in recent years, several researchers proposed methods to estimate the in situ TC by measuring a rock's effective properties (e.g., [2; 28; 29; 30; 31]). Measuring the uniaxial compressive strength (UCS), density, porosity, and P-wave velocity of a rock is not as difficult as measuring in situ TC. These properties can also be obtained by laboratory or indirect methods, such as using the Schmidt hammer for the determination of the UCS [24]. In this case, the following items can be found without performing TC core experiments at several depths [30]:  
* Predicting and/or in-time monitoring of the heat flux;
* Extracting the temperature profile;
* Evaluating the saturation content of the formation. [PERSON], [PERSON], and [PERSON] [2] conducted tests on rock samples that are mostly used in building constructions. They investigated the relationship between TC and rock properties, specifically for the P-wave velocity, UCS, density, and porosity. They proposed equations for the prediction of the TC regarding each of the four above-mentioned parameters, but not by simultaneously considering all parameters (multivariable equation). They found that TC has a direct relation to P-wave, UCS, and density, whereas it has an inverse relation to porosity. In another study, [PERSON], [PERSON], and [PERSON] [4] performed laboratory tests on 12 different rock samples. They found that the type of a rock's mineral composition could also affect its TC to a high degree. In recent years, modeling using machine learning (ML) methods to better fit with actual measurements has become more popular with scientists than other modeling methods [32; 33; 34; 35; 36; 37]. ML methods are applied to different issues in geotechnical engineering [38; 39; 40; 41].