Please use this identifier to cite or link to this item:
Scopus Web of ScienceĀ® Altmetric
Type: Conference paper
Title: Predicting stress-strain relationships in stratified rock mass by using machine learning techniques
Author: Melkoumian, N.
Citation: Proceedings of the Tenth IASTED International Conference on Artificial Intelligence and Applications / M. H. Hamza (ed.): pp. 479-484
Publisher: ACTA Press
Publisher Place: USA
Issue Date: 2010
ISBN: 9780889868182
Conference Name: IASTED International Conference on Artificial Intelligence and Applications (10th : 2010 : Innsbruck, Austria)
Statement of
N.S. Melkoumian
Abstract: The paper suggests a method based on machine learning techniques to predict the stress-strain relationships in the stratified composite rock mass. To construct the input output dataset for the learning stage, published experimental data for different stratified composite rock specimens have been used. The parameters of the covariance function have been optimized maximizing the log of the marginal likelihood for the experimental results. In the inference stage the obtained parameters are used to statistically predict the stress-strain state of the same rock mass for new stresses not used in the learning stage. The predictions are compared with the actual experimental data aimed to evaluate the validity and applicability of the suggested method. The results demonstrate that by conducting a limited number of experiments and applying the proposed machine learning techniques, it is possible to predict stress-strain relationships for stratified composite rock masses for different combinations of parameters. Application of the proposed method can significantly decrease the number of experiments required for constructing reliable mechanical models for rocks. The uncertainty of the predictions will be high in the case of very limited number of experiments; however it will be significantly reduced once new experimental results are provided to the proposed Bayesian predictive model.
Keywords: Stratified rock
stress-strain state
machine learning.
Rights: Copyright status unknown
DOI: 10.2316/p.2010.674-153
Published version:
Appears in Collections:Aurora harvest 5
Civil and Environmental Engineering publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.