- Title
- Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
- Creator
- Chen, Wusi; Khandelwal, Manoj; Murlidhar, Bhatawdekar; Bui, Dieu; Tahir, Mahmood; Katebi, Javad
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/172098
- Identifier
- vital:14407
- Identifier
-
https://doi.org/10.1007/s00366-019-00731-2
- Identifier
- ISBN:0177-0667 (ISSN)
- Abstract
- In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geotechnical structures such as rock slope. To fulfill the aim of this study, a database of three model input parameters, i.e., p wave velocity, uniaxial compressive strength and Brazilian tensile strength and one model output, which is cohesion of limestone samples was prepared and utilized by GMDH. Different GMDH models with neurons and layers and selection pressure were tested and assessed. It was found that GMDH model number 4 (with 8 layers) shows the best performance among all of tested models between the input and output parameters for the prediction and assessment of rock cohesion with coefficient of determination (R2) values of 0.928 and 0.929, root mean square error values of 0.3545 and 0.3154 for training and testing datasets, respectively. Multiple regression analysis was also performed on the same database and R2 values were obtained as 0.8173 and 0.8313 between input and output parameters for the training and testing of the models, respectively. The GMDH technique developed in this study is introduced as a new model in field of rock shear strength parameters. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
- Publisher
- Springer
- Relation
- Engineering with Computers Vol. 36, no. 2 (2020), p. 783-793
- Rights
- Copyright © Springer-Verlag London Ltd., part of Springer Nature 2019
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- 0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 0802 Computation Theory and Mathematics; Brazilian tensile strength; GMDH; P-wave; Rock cohesion; Uniaxial compressive strength
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