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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/1571

Title: Prediction of thermal conductivity of rock through physico-mechanical properties
Authors: SINGH, N
SINHA, S
SINGH, V K
Keywords: adaptive systems
backpropagation
fuzzy sets
heat transfer
statistical methods
Issue Date: 2005
Publisher: Elsevier
Citation: Building and Environment 42(1), 146-155
Abstract: The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Present study supports the use of artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) 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 geo environmental engineering field. In recent years, considerable effort has been made to develop techniques to determine these properties. Comparative analysis is made to analyze the capabilities among six different models of ANN and ANFIS. ANN models are based on feedforward backpropagation network with training functions resilient backpropagation (RP), one step secant (OSS) and Powell–Beale restarts (CGB) and radial basis with training functions generalized regression neural network (GRNN) and more efficient design radial basis network (NEWRB). A data set of 136 has been used for training different models and 15 were used for testing purposes. A statistical analysis is made to show the consistency among them. ANFIS is proved to be the best among all the networks tried in this case with average absolute percentage error of 0.03% and regression coefficient of 1, whereas best performance shown by the FFBP (RP) with average absolute error of 2.26%. Thermal conductivity is predicted using P-wave velocity, porosity, bulk density, uniaxial compressive strength of rock as input parameters.
URI: http://dx.doi.org/10.1016/j.buildenv.2005.08.022
http://hdl.handle.net/10054/1571
http://dspace.library.iitb.ac.in/xmlui/handle/10054/1571
ISSN: 0360-1323
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