Prediction of the Void Ratio Parameter in Mineral Tailings Using Gene Expression Programming
Joint Authors
Heshmati R., Ali Akbar
Salehzadeh, Hossein
Shahidi, Mehdi
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-17
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Mineral tailing deposits are one of the most important issues in the field of geotechnical engineering.
The void ratio of mineral tailings is an essential parameter for investigating the geotechnical behavior of tailings.
However, there has not yet been a comprehensive empirical formulation for initial prediction of the void ratio of mineral tailings.
In this study, the void ratio of various types of mineral waste is estimated by using gene expression programming (GEP).
Therefore, taking into consideration the effective physical parameters that affect the estimation of this parameter, eight different models are presented.
A reliable experimental database collected from different sources in the literature was applied to develop the GEP models.
The performance of the developed GEP models was measured based on coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE).
According to the results, the model with effective stress σ′, initial void ratio (e0), and parameters of R2 = 0.92, MAE = 0.109, and RMSE = 0.180 performed the best.
Finally, a new empirical formulation for the initial prediction of the void ratio parameter is proposed based on the aforementioned analyses.
American Psychological Association (APA)
Heshmati R., Ali Akbar& Salehzadeh, Hossein& Shahidi, Mehdi. 2020. Prediction of the Void Ratio Parameter in Mineral Tailings Using Gene Expression Programming. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1123631
Modern Language Association (MLA)
Heshmati R., Ali Akbar…[et al.]. Prediction of the Void Ratio Parameter in Mineral Tailings Using Gene Expression Programming. Advances in Civil Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1123631
American Medical Association (AMA)
Heshmati R., Ali Akbar& Salehzadeh, Hossein& Shahidi, Mehdi. Prediction of the Void Ratio Parameter in Mineral Tailings Using Gene Expression Programming. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1123631
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1123631