- Title
- Comparative evaluation of empirical approaches and artificial intelligence techniques for predicting uniaxial compressive strength of rock
- Creator
- Li, Chuanqi; Zhou, Jian; Dias, Daniel; Du, Kun; Khandelwal, Manoj
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198033
- Identifier
- vital:18982
- Identifier
-
https://doi.org/10.3390/geosciences13100294
- Identifier
- ISSN:2076-3263 (ISSN)
- Abstract
- The uniaxial compressive strength (UCS) of rocks is one of the key parameters for evaluating the safety and stability of civil and mining structures. In this study, 386 rock samples containing four properties named the load strength (PLS), the porosity (Pn), the P-wave velocity (Vp), and the Schmidt hardness rebound number (SHR) are utilized to predict the UCS using several typical empirical equations (EA) and artificial intelligence (AI) methods, i.e., 16 single regression (SR) equations, 2 multiple regression (MR) equations, and the random forest (RF) models optimized by grey wolf optimization (GWO), moth flame optimization (MFO), lion swarm optimization (LSO), and sparrow search algorithm (SSA). The root mean square error (RMSE), determination coefficient (R2), Willmott’s index (WI), and variance accounted for (VAF) are used to evaluate the predictive performance of all developed models. The evaluation results show that the overall performance of AI models is superior to empirical approaches, especially the LSO-RF model. In addition, the most important input variable is the Pn for predicting the UCS. Therefore, AI techniques are considered as more efficient and accurate approaches to replace the empirical equations for predicting the UCS of these collected rock samples, which provides a reliable and effective idea to predict the rock UCS in the filed site. © 2023 by the authors.
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Relation
- Geosciences (Switzerland) Vol. 13, no. 10 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- 3705 Geology; 3706 Geophysics; Artificial intelligence; Empirical approaches; Lion swarm optimization (LSO); Random forest (RF); Uniaxial compressive strength (UCS)
- Full Text
- Reviewed
- Funder
- The study reported here is financially supported by China Scholarship Council (Grant No. 202106370038).
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