Prediction of thermal conductivity of rocks by soft computing

International Journal of Earth Sciences - Tập 100 - Trang 1383-1389 - 2010
Manoj Khandelwal1
1Department of Mining Engineering, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, India

Tóm tắt

The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Knowledge of the thermal conductivity of rocks is necessary for the calculation of heat flow or for the longtime modeling of geothermal resources. In recent years, considerable effort has been made to develop artificial intelligence techniques to determine these properties. Present study supports the application of artificial neural network (ANN) in the study of thermal conductivity along with other intrinsic properties of rock due to its increasing importance in many areas of rock engineering, agronomy, and geoenvironmental engineering field. In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network with 4-7-1 architecture was trained and tested using 107 experimental data sets of various rocks. Twenty new data sets were used for the validation and comparison of the TC by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by ANN and MVRA were 0.984 and 0.914, respectively, whereas MAE was 0.0894 and 0.2085 for ANN and MVRA, respectively.

Tài liệu tham khảo

Canakcı H, Demirboga R, Karakoc BM, Sirin O (2007) Thermal conductivity of limestone from Gaziantep (Turkey). Build Environ 42:1777–1782

Clauser C, Huenges E (1995) Thermal conductivity of rocks and minerals. Rock physics and phase relations: a handbook of physical constants. Americal Geophysical Union

Demirci A, Gorgulu K, Duruturk YS (2004) Thermal conductivity of rocks and its variation with uniaxial and triaxial stress. Int J Rock Mech Min Sci 41:1133–1138

Gorgulu K (2004) Determination of relationships between thermal conductivity and material properties of rocks. J Univ Sci Technol Beijing 11:297

Gul IH, Maqsood A (2006) Thermophysical properties of diorites along with the prediction of thermal conductivity from porosity and density data. Int J Thermophys 27(2):614–626

Harmathy TZ (1970) Thermal properties of concrete at elevated temperatures. J Mater 5:47–74

Hasan A (1999) Optimising insulation thickness for buildings using life cycle cost. Appl Energy 63:115–124

Incropera FP, Dewitt DP (1990) Fundamentals of heat and mass transfer. Wiley, New York

Khandelwal M (2008) Evaluation and prediction of blast induced ground vibration and frequency for surface mine—a neural network approach. PhD. thesis, Indian Institute of Technology, Bombay, India (unpublished)

Khandelwal M, Singh TN (2009) Prediction of blast induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222

Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, New Delhi

Lide DR (1998) Chemical rubber company handbook of chemistry and physics, 79th edn. CRC Press, Boca Raton

MacKay DJC (1992) Bayesian interpolation. Neural Comput 4:415–447

Maulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39

Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45(8):1446–1453

Ozkahraman HT, Selver R, Isik EC (2004) Determination of the thermal conductivity of rock from P-wave velocity. Int J Rock Mech Min Sci 41:703–708

Simpson PK (1990) Artificial neural system—foundation, paradigm, application and implementations. Pergamon Press, New York

Singh TN, Sinha S, Singh VK (2007) Prediction of thermal conductivity of rock through physico-mechanical properties. Build Environ 42:146–155

Troschke B, Burkhardt H (1998) Thermal conductivity models for two-phase systems. Phys Chem Earth 23(3):351–355

Verma BP (2000) Rock mechanics for engineers. Khanna, New Delhi, p 108

Yasar E, Erdogan Y (2008) Strength and thermal conductivity in lightweight building materials. Bull Eng Geol Environ 67:513–519

Yasar E, Erdogan Y, Guneyli H (2008) Determination of thermal conductivity from physico-mechanical properties. Bull Eng Geol Environ 67:219–225

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Thông tin xuất bản

International Journal of Earth Sciences

Tập 100

1383-1389

Thông tin tác giả