RESEARCH ARTICLE
A Reliable PSO-based ANN Approach for Predicting Unconfined Compressive Strength of Sandstones
Yasin Abdi1, Ehsan Momeni2, *
Article Information
Identifiers and Pagination:
Year: 2020Volume: 14
Issue: Suppl-1, M5
First Page: 237
Last Page: 249
Publisher ID: TOBCTJ-14-237
DOI: 10.2174/1874836802014010237
Article History:
Received Date: 26/12/2019Revision Received Date: 16/01/2020
Acceptance Date: 04/03/2020
Electronic publication date: 24/08/2020
Collection year: 2020
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
The reliable determination of geomechanical parameters of rocks such as Unconfined Compressive Strength (UCS) using laboratory methods is problematic and time-consuming. In this regard, the construction of reliable predictive models for assessing the UCS is of advantage.
Objective:
The main purpose of this work is to propose the use of a reliable PSO-based ANN approach for predicting the UCS of sandstones.
Methods:
For this purpose, laboratory tests were performed on 60 sandstone specimens. The laboratory tests comprise P-wave velocity, dry density, Schmidt hardness and UCS. Apart from the latter, the other laboratory tests were set as model inputs. Prediction performance of the constructed model was assessed according to the criteria including coefficient of determination (R2), Root Mean Squared Error (RMSE) and Variance Account For (VAF).
Results:
Results (R2= 0.974 and RMSE = 0.086 and VAF = 97.5) showed the reliability of the constructed PSO-based ANN model to predict UCS of sandstones.
Conclusion:
Hence, this study recommends utilizing PSO-based ANN as a feasible tool for assessing UCS of sandstones. Nevertheless, further research is suggested for model generalization purposes.
