Prediction Method of Stope Stability Based on NPCA-GA-BP Neural Network
Received date: 2021-05-07
Revised date: 2021-08-30
Online published: 2022-06-17
Stope stability is a geological mechanics problem that cannot be ignored in mining，and its stability directly affects the safety of mine production and engineering decision-making.Therefore，scientific prediction of stope stability plays a crucial role in mining safety.The stability of stope is a typical nonlinear problem.Since BP neural network has the virtue of tackling complex nonlinear systems，it can be applied to stope stability prediction.Nevertheless，the existing prediction methods either only focus on optimizing the weights and thresholds of the neural network or only consider that the stability of the stope is under the influence of multiple factors and the influencing indexes have a strong correlation，but do not consider the two methods in an integrated manner.Hence，the prediction accuracy of stope stability based on neural network is low，which cannot provide valid support for mine management.Due to the highly nonlinear characteristics of the mining stability system，the traditional principal component analysis will lose a large amount of information.Therefore，we propose a stope stability prediction method using nonlinear principal component analysis combined with BP neural network optimized by the genetic algorithm，which effectively improves the prediction accuracy of stope stability.The nonlinear principal component analysis method performs nonlinear dimensionality reduction on the impact indicators of stope stability，replacing the original multiple indicators with a few principal components that retain the original information，simplifying the neural network structure，and improving the operational efficiency.GA aims to optimize the initial weights and thresholds of the BP neural network to overcome the defects of unstable initial weight thresholds and further improve the accuracy of quarry stability prediction.Taking the measured data of a mine as an example，the effectiveness of the proposed method is verified.The comparison results show that the average relative errors of NPCA-GA-BP and GA-BP models are 10.5% and 7.6% lower than those of BP models，respectively，indicating that the BP neural network is optimized by the genetic algorithm can significantly improve the prediction accuracy.The average relative error of the NPCA-GA-BP model is 2.9% lower than that of the GA-BP model，indicating that the dimension of variables is reduced and the prediction accuracy is increased through nonlinear principal component analysis.It can be concluded that the NPCA-GA-BP prediction method has a higher prediction accuracy of stope stability，and has certain guiding significance for realizing intelligent mine.
Raoqing XIE , Jianhong CHEN , Wenfeng XIAO . Prediction Method of Stope Stability Based on NPCA-GA-BP Neural Network[J]. Gold Science and Technology, 2022 , 30(2) : 272 -281 . DOI: 10.11872/j.issn.1005-2518.2022.02.052
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