- Title
- Application of an expert system to predict thermal conductivity of rocks
- Creator
- Khandelwal, Manoj
- Date
- 2012
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160724
- Identifier
- vital:12274
- Identifier
-
https://doi.org/10.1007/s00521-011-0573-y
- Identifier
- ISBN:0941-0643
- Abstract
- 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 support vector machine (SVM). Training of the SVM network was carried out using 102 experimental data sets of various rocks, whereas 25 new data sets were used for the testing of the TC by SVM model. Multivariate regression analysis (MVRA) has also been carried out with same data sets that were used for the training of SVM model. SVM 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 SVM and MVRA was 0. 994 and 0. 918, respectively, whereas MAE was 0. 0453 and 0. 2085 for SVM and MVRA, respectively. © 2011 Springer-Verlag London Limited.
- Publisher
- Springer
- Relation
- Neural Computing and Applications Vol. 21, no. 6 (2012), p. 1341-1347
- Rights
- Copyright © 2011 Springer-Verlag London Limited.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0906 Electrical and Electronic Engineering; 1702 Cognitive Science; Density; Multivariate regression analysis (MVRA); P-wave; Porosity; Support vector machine (SVM); Thermal conductivity; UCS
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