Strength prediction of similar materials to ionic rare earth ores based on orthogonal test and back propagation neural network
This paper aims to predict the strength of materials similar to the ionic rare earth (IRE) ores [hereinafter referred as similar materials (SM)]. A 4 × Y × 2 back propagation neural network (BPNN) prediction model, based on 18 groups of samples of the SM with different mix proportions, was used to describe their strength. The BPNN modelling scheme includes four input layer neurons, representing the amounts of kaolinite, potassium feldspar, anorthose and mica, and two output layer neurons corresponding to the strength indices c and φ of the samples after 6 h leaching. Comparing the training and prediction errors, it is verified that the error in predicted strength is minimized when the number of hidden layer neurons Y equals 9. The correlation coefficient R of the prediction model is as high as 0.998, and the maximum relative errors of the strength indices (c and φ) are 4.11% and 4.26%, respectively. Orthogonal tests show that the BPNN is a reliable and accurate method to predict the strength of SM. Featuring uniform dispersion, comparability and nonlinear optimization, the proposed method sheds further light on the strength prediction of IRE ores.
KeywordsBack propagation neural network (BPNN) Orthogonal test Ionic rare earth (IRE) Similar materials (SM) Strength prediction
WZ was involved in conceptualization; YD was involved in data curation; YD and CZ was involved in formal analysis; WZ, ZK and XW were contributed to funding acquisition; WZ was involved in project administration; AC was involved in supervision; WZ and YD was involved in writing–original draft; AC was involved in writing–review and editing.
This study was funded by the National Natural Science Foundation of China (51504102, 51764014), the Postdoctoral Research Foundation of China (2017M622099), the National Key Technologies Research & Development Program (2017YFC0804601), the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering (Z017024), the China Scholarship Council (201708360152), the Program of Qingjiang Excellent Young Talents, Jiangxi University of Science and Technology (JXUSTQJYX2017002), the Program of Hundred People Voyage, Jiangxi Province (20180375).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Armaghani DJ, Hajihassani M, Mohamad ET (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian J Geosci 7(12):5383–5396Google Scholar
- Ceylan R, Koyuncu H (2016) A new breakpoint in hybrid particle swarm-neural network architecture: individual boundary adjustment. Int J Inf Technol Decis Mak 15(6):1313–1343Google Scholar
- Chi RA, Tian J, Luo XP (2012) The basic research on the weathered crust elution-deposited rare earth ores. Nonferr Met Sci Eng 3(4):1–13Google Scholar
- Demir A (2015) Prediction of hybrid fibre-added concrete strength using artificial neural networks. Comput Concr 15(4):503–514Google Scholar
- Dong Y, Yang ZQ, Gao Q (2018) Strength forecasting of backfillinng materials by BP neural network model collaborated with orthogonal experiment. Mater Rev 32(6):1032–1036Google Scholar
- Eskandari-Naddaf H, Kazemi R (2017) ANN prediction of cement mortar compressive strength, influence of cement strength class. Constr Build Mater 138:1–11Google Scholar
- Gazder U, Al-Amoudi OSB, Khan SMS (2017) Predicting compressive strength of blended cement concrete with ANNs. Comput Concr 20(6):627–634Google Scholar
- González-Taboada I, González-Fonteboa B, Martínez-Abella F (2016) Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming. Constr Build Mater 106:480–499Google Scholar
- Gordan B, Armaghani DJ, Hajihassani M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97Google Scholar
- Guo ZQ, Jin JF, Qin YH (2017a) Experimental research on one-dimensional horizontal infiltration rules of ion-adsorption rare earth. Nonferr Met Sci Eng 8(2):102–106Google Scholar
- Guo ZQ, Jin JF, Wang GS (2017b) Basic theory of leaching kinetics on the weathered crust elution-deposited rare earth ores. Nonferr Met Sci Eng 8(5):127–132Google Scholar
- Hodhod OA, Said TE, Ataya AM (2018) Prediction of creep in concrete using genetic programming hybridized with ANN. Comput Concr 21(5):513–523Google Scholar
- Hossain KMA, Anwar MS, Samani SG (2018) Regression and artificial neural network models for strength properties of engineered cementitious composites. Neural Comput Appl 29(9):631–645Google Scholar
- Madhubabu N, Singh PK, Kainthola A (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202–213Google Scholar
- Momeni E, Armaghani DJ, Hajihassani M (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63Google Scholar
- Mozumder RA, Laskar AI (2015) Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Comput Geotech 69:291–300Google Scholar
- Muddle J, Kirton SB, Parisini I (2017) Predicting the fine particle fraction of dry powder inhalers using artificial neural networks. J Pharm Sci 106(1):313–321Google Scholar
- Sharma LK, Vishal V, Singh TN (2017) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement 102:158–169Google Scholar
- Soleimani S, Rajaei S, Jiao P (2018) New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming. Measurement 113:99–107Google Scholar
- Tian J, Tang XK, Yin JQ (2012) Present situation of fundamental theoretical research on leaching process of weathered crust elution-deposited rare earth ore. Nonferr Met Sci Eng 3(4):48–52Google Scholar
- Tizpa P, Chenari RJ, Fard MK (2015) ANN prediction of some geotechnical properties of soil from their index parameters. Arabian J Geosci 8(5):2911–2920Google Scholar
- Wang XJ, Zhuo YL, Deng SQ (2017) Experimental research on the impact of ion exchange and infiltration on the microstructure of rare earth orebody. Adv Mater Sci Eng 2017(1):1–8Google Scholar
- Wang XJ, Zhuo YL, Zhao K (2018) Experimental measurements of the permeability characteristics of rare earth ore under the hydro-chemical coupling effect. RSC Adv 8(21):11652–11660Google Scholar
- Zheng P, Liu H, Wang J (2014) Optimization of experimental conditions by orthogonal test design in a laser-induced breakdown experiment to analyze aluminum alloys. Anal Methods 6(7):2163–2169Google Scholar