Open AccessRegression analysis of friction resistance coefficient under different support methods of roadway based on PSO-SVM
Author(s) -
Ying Song,
Ming Zhu,
Wei Ning,
Liya Deng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1941/1/012046
Subject(s) - support vector machine , particle swarm optimization , correlation coefficient , regression analysis , workload , coefficient of determination , engineering , ventilation (architecture) , regression , computer science , statistics , machine learning , mathematics , mechanical engineering , operating system
In order to solve the problem of large test workload by using the traditional mine ventilation measurement methods, the impact of support methods on the ventilation resistance coefficient was analyzed emphatically. The Particle Swarm Optimization Support Vector Machine(PSO-SVM)algorithm was used to model three types of supported roadways: wood-supported roadways, I-steel-supported roadways, and bolt-net-supported roadways. The roadway attribute parameters of the concrete supported roadway were analyzed, and the PSO-SVM regression model of the supporting parameters and the friction resistance coefficient under the specific supporting method was established, and its regression performance was analyzed. The results showed that the average relative errors of the regression results of wood-supported roadway, I-steel-supported roadway, and bolt-net-supported roadway were: -2.542%, 0.483%, and 1.605%. The friction resistance coefficient and the supporting parameters of the corresponding supporting roadway all showed a high degree of correlation. The research showed that the PSO-SVM regression model can more accurately regress the ventilation resistance coefficient and provide a new intelligent algorithm for the regression of ventilation resistance coefficient, which could have these important guiding significance for practical application.