Strength prediction of similar materials to ionic rare earth ores based on orthogonal test and back propagation neural network

AbstractThis 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.

[1]  T. Singh,et al.  Prediction of compressive strength and elastic modulus of carbonate rocks , 2016 .

[2]  Peichao Zheng,et al.  Optimization of experimental conditions by orthogonal test design in a laser-induced breakdown experiment to analyze aluminum alloys , 2014 .

[3]  Osama A. Hodhod,et al.  Prediction of creep in concrete using genetic programming hybridized with ANN , 2018 .

[4]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[5]  Pengcheng Jiao,et al.  New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming , 2018 .

[6]  Kui Zhao,et al.  Experimental Research on the Impact of Ion Exchange and Infiltration on the Microstructure of Rare Earth Orebody , 2017 .

[7]  Zhigao Xu,et al.  The basic research on the weathered crust elution-deposited rare earth ores , 2012 .

[8]  T. N. Singh,et al.  Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties , 2017 .

[9]  Aminul Islam Laskar,et al.  Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network , 2015 .

[10]  Qian Wang,et al.  Coupling Strategies Investigation of Hybrid Atomistic-Continuum Method Based on State Variable Coupling , 2017 .

[11]  R. Jamshidi Chenari,et al.  ANN prediction of some geotechnical properties of soil from their index parameters , 2015, Arabian Journal of Geosciences.

[12]  Danial Jahed Armaghani,et al.  Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks , 2015 .

[13]  Khandaker M. A. Hossain,et al.  Regression and artificial neural network models for strength properties of engineered cementitious composites , 2016, Neural Computing and Applications.

[14]  Ali Demir,et al.  Prediction of Hybrid fibre-added concrete strength using artificial neural networks , 2015 .

[15]  Hasan Koyuncu,et al.  A New Breakpoint in Hybrid Particle Swarm-Neural Network Architecture: Individual Boundary Adjustment , 2016, Int. J. Inf. Technol. Decis. Mak..

[16]  Kui Zhao,et al.  Experimental measurements of the permeability characteristics of rare earth ore under the hydro-chemical coupling effect , 2018, RSC advances.

[17]  Uneb Gazder,et al.  Predicting compressive strength of blended cement concrete with ANNs , 2017 .

[18]  Hamid Eskandari-Naddaf,et al.  ANN prediction of cement mortar compressive strength, influence of cement strength class , 2017 .

[19]  Jogoth Ali,et al.  Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks. , 2017, Journal of pharmaceutical sciences.

[20]  Luo Xian-ping.  Present situation of fundamental theoretical research on leaching process of weathered crust elution-deposited rare earth ore , 2012 .

[21]  Juan Luis Pérez-Ordóñez,et al.  Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming , 2016 .

[22]  Aminaton Marto,et al.  Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization , 2014, Arabian Journal of Geosciences.