- Title
- Stability evaluation of dump slope using artificial neural network and multiple regression
- Creator
- Bharati, , Ashutosh; Ray, Arunava; Khandelwal, Manoj; Rai, Rajesha; Jaiswal, , Ashok
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186651
- Identifier
- vital:16908
- Identifier
-
https://doi.org/10.1007/s00366-021-01358-y
- Identifier
- ISBN:0177-0667 (ISSN)
- Abstract
- The present paper focuses on designing an artificial neural network (ANN) model and a multiple regression analysis (MRA) that could be used to predict factor of safety of dragline dump slope. To implement these two models, the dataset was utilized from the numerical simulation results of dragline dump slopes, wherein 216 dragline dump slope models were simulated using a numerical modeling technique employed with the finite element method. The finite element model was incorporated a combination of three geometrical parameters, namely, coal-rib height (Crh), dragline dump slope height (Sh), and dragline dump slope angle (Sa) of the dump slope. The predicted results derived from the MRA and ANN models were compared with the results obtained from the numerical simulation of the dump slope models. Moreover, to compare the validity of both the models, various performance indicators, such as variance account for (VAF), determination coefficient (R2), root mean square error (RMSE), and residual error were calculated. Based on these performance indicators, the ANN model has shown a higher prediction accuracy than the MRA model. The study reveals that the ANN model developed in this research could be handy in designing the dragline dump slopes at the preliminary stage. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- Engineering with Computers Vol. 38, no. (2022), p. 1835-1843
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021, The Author(s)
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and computing sciences; Artificial neural network; Dragline dumps; Finite element method; Multiple regression analysis
- Full Text
- Reviewed
- Hits: 432
- Visitors: 488
- Downloads: 82
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE2 | Accepted version | 624 KB | Adobe Acrobat PDF | View Details Download |