- Title
- Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique
- Creator
- Yu, Zhi; Shi, Xiaohu; Miao, Xiaohu; Zhou, Jian; Khandelwal, Manoj
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176945
- Identifier
- vital:15218
- Identifier
-
https://doi.org/10.1016/j.ijrmms.2021.104794
- Identifier
- ISBN:1365-1609 (ISSN)
- Abstract
- For maximum metal recovery, considering the movement of ore and waste during the blasting process in loading design is meaningful for reducing ore loss and ore dilution in an open-pit mine. The blast-induced rock movement (BIRM) can be directly measured; nevertheless, it is time-consuming and relative expensive. To solve this problem, a novel intelligent prediction model was proposed by using dimensional analysis and optimized artificial neural network technique in this paper based on the BIRM monitoring test in Husab Uranium Mine, Namibia and Phoenix Mine, USA. After using dimensional analysis, five input variables and one output variable were determined with both considering the dimension and physical meaning of each dimensionless variable. Then, artificial neural network technique (ANN) technique was utilized to develop an accurate prediction model, and a metaheuristic algorithm namely the Equilibrium Optimizer (EO) algorithm was applied to search the optimal hyper-parameter combination. For comparison aims, a linear model and a non-linear regression model were also performed, and the comparison results show that the provided hybrid ANN-based model can yield better prediction performance. As a result, it can be concluded that the developed intelligent model in this article has the potential to predict BIRM during bench blasting, and the analysis method and modeling process in this paper can provide a reference for solving other engineering problems. © 2021 Elsevier Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
- Publisher
- Elsevier Ltd
- Relation
- International Journal of Rock Mechanics and Mining Sciences Vol. 143, no. (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 Elsevier Ltd.
- Rights
- 104794
- Subject
- 0905 Civil Engineering; 0914 Resources Engineering and Extractive Metallurgy; Artificial neural network (ANN); Bench blasting; Blast-induced rock movement (BIRM); Dimensional analysis (DA)
- Reviewed
- Funder
- The financial support from the National Natural Science Foundation Project of China (Grant Nos. 41807259, 72088101 and 51874350 ), the National Key R&D Program of China ( 2017YFC0602902 ), the Fundamental Research Funds for the Central Universities of Central South University ( 2018zzts217 ), and the Innovation-Driven Project of Central South University ( 2020CX040 ), are gratefully acknowledged.
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