A Model to Predict Crosscut Stress Based on an Improved Extreme Learning Machine Algorithm

Xiaobo Liu, Lei Yang and Xingfan Zhang
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Xiaobo Liu: Intelligent Mine Research Center, Northeastern University, Shenyang 110004, China
Lei Yang: School of Civil Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Xingfan Zhang: Intelligent Mine Research Center, Northeastern University, Shenyang 110004, China

Energies, 2019, vol. 12, issue 5, 1-15

Abstract: The analysis of crosscut stability is an indispensable task in underground mining activities. Crosscut instabilities usually cause geological disasters and delay of the project. On site, mining engineers analyze and predict the crosscut condition by monitoring its convergence and stress; however, stress monitoring is time-consuming and expensive. In this study, we propose an improved extreme learning machine (ELM) algorithm to predict crosscut’s stress based on convergence data, for the first time in literature. The performance of the proposed technique is validated using a crosscut response by means of the FLAC 3D finite difference program. It is found that the improved ELM algorithm performs higher generalization performance compared to traditional ELM, as it eliminates the random selection for input weights. Furthermore, a crosscut construction project in an underground mine, Yanqianshan iron mine, located in Liaoning Province (China), is selected as the case study. The accuracy and efficiency of the improved ELM algorithm has been demonstrated by comparing predicted stress data to measured data on site. Additionally, a comparison is conducted between the improved ELM algorithm and other commonly used artificial neural network algorithms.

Keywords: crosscut; stress; convergence; artificial neural network; extreme learning machine; FLAC 3D (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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