|本期目录/Table of Contents|

[1]王海峰,李夕兵,董陇军,等.基于支持向量机的采空区稳定性分级[J].中国安全生产科学技术,2014,10(10):154-159.[doi:10.11731/j.issn.1673-193x.2014.10.026]
 WANG Hai-feng,LI Xi-bing,DONG Long-jun,et al.Classification of goaf stability based on support vector machine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(10):154-159.[doi:10.11731/j.issn.1673-193x.2014.10.026]
点击复制

基于支持向量机的采空区稳定性分级
分享到:

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年10期
页码:
154-159
栏目:
职业安全卫生管理与技术
出版日期:
2014-10-31

文章信息/Info

Title:
Classification of goaf stability based on support vector machine
作者:
王海峰李夕兵董陇军刘抗仝慧贤
(中南大学 资源与安全工程学院,湖南 长沙 410083)
Author(s):
WANG Hai-feng LI Xi-bing DONG Long-jun LIU Kang TONG Hui-xian
(School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China)
关键词:
支持向量机采空区稳定性未确知测度分级
Keywords:
support vector machine goaf stability uncertainty measurement classification
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2014.10.026
文献标志码:
A
摘要:
针对采空区稳定性分级的影响因素众多且关系复杂的特点,提出采用支持向量机理论对采空区稳定性进行分级。根据分级评价指标选取原则,选取岩体结构、地质构造、岩石的质量指标、地下可见水、地下水体、周边开采的影响、相邻空区的情况、工程布置、跨度、面积、高度、矿柱的尺寸及布置、埋藏深度和采空区的规格14个影响因子,建立了采空区稳定性评价指标体系,引入支持向量机理论,选择有向无环图方式构造多类分类器,得到采空区稳定性分级的支持向量机模型。将该模型用于山东黄金矿业西山矿区的25个采空区进行分级,并与未确知测度方法的分级情况
Abstract:
For the reason that many factors with complex relationship influence the classification of goaf stability, the method of support vector machine was proposed. The 14 indexes i.e. , rock structure, geological structure, quality indicators of rocks, visible underground water, groundwater body, effect of peripheral mining, engineering layout, span, area, height, pillar size and layout, buried deep, specification of goaf were selected to establish the index system for evaluation based on the selection principle of grading index. A comprehensive classification model of goaf stability was established by the theory of support vector machine with directed acyclic graph. This model was employed to classify 25 goafs in Xishan mine of Shandong Gold Mining, and the classification states were compared with that by the unascertained measure classification method. The results showed that support vector machine is reasonable , better reflects the practice and can be applied to the actual engineering.

参考文献/References:

[1]周宏伟,谢和平,左建平.深部高地应力下岩石力学行为研究进展[J].力学进展,2005,35(1):91-99 ZHOU Hong-wei, XIE He-ping, ZUO Jian-ping. Developments in researches on mechanical behaviors of rocks under the condition of high ground pressure in the depths[J]. Advances in Mechanics, 2005,35(1):91-99
[2]王安建,王高尚.矿产资源与国家经济发展[M].北京:地震出版社,2002
[3]国家安全生产监督管理局编.安全评价[M].北京:煤炭工业出版社,2002
[4]刘艳红,罗周全.采空区失稳的安全流变-突变理论分析[J],工业安全与环保,2009,35(9):5-7 LIU Yan-hong, LUO Zhou-quan. Analysis of cavity instability based on safety rheology-mutation [J], Industrial Safety and Environmental Protection, 2009,35(9):5-7
[5]HU Yu-xi, LI Xi-bing. Bayes discriminant analysis method to identify risky of complicated goaf in mines and its application[J]. Transactions of Nonferrous Metals Society of China.2012,22(2):425-431
[6]王新民,段瑜,彭欣,等.采空区灾害危险度的模糊综合评价[J].矿冶研究与开发,2005,25(2):83-85 WANG Xin-min, DUAN Yu, PENG Xin, et al. Fuzzy synthetic assessment of the danger degree of mined-out area disaster[J].Mining Research and Development, 2005, 25(2): 83-85
[7]宫凤强,李夕兵,董陇军,等.基于未确知测度理论的采空区危险性评价研究[J].岩石力学与工程学报,2008,27(2):323-330 GONG Feng-qiang, LI Xi-bin, DONG Lon-jun, et al.Underground goaf risk evaluation based on uncertainty measurement theory[J]. Chinese Journal of Rock Mechanics and Engineering, 2008, 27(2): 323-330
[8]唐胜利,唐皓,郭辉.基于BP神经网络的空洞型采空区隐定性评价研究[J].西安科技大学学报,2012,32(2):234-238 TANG Sheng-li, TANG Hao, GUO Hui. Stability of empty mine goaf based on BP neural network[J] . Journal of Xi’an University of Science and Technology,2012,32(2):234-238
[9]丁然.支持向量机多类分类算法研究[D].黑龙江:哈尔滨理工大学,2012
[10]刘锋.基于粗糙集的支持向量机分类方法[D].江西:景德镇陶瓷学院,2010
[11]Hsu Chih-Wei,Lin Chih-Jen. A comparison of methods for multi-class support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):415-425
[12]Kebel U.Pairwise classification and support vector machines[C].Advances in Kernel Methods-Support Vector Learning. Cambridge: MIT Press,1999:255-258
[13]Platt J, Cristianini N, Shawe-Taylor J.Large margin DAGs for multiclass classification [J].Advances in Neural information Process System,2000,(12):547-553
[14]Bennett K P,Blue J A. A support vector machine approach to decision tree[J].Rensselaer Polytechnic Institute,1997,(3):97-100
[15]薛宁静.多类支持向量机分类器对比研究[J].计算机工程与设计,2011,32(5):1792-1795 XUE Ning-jing. Comparison of multi-class support vector machines[J]. Computer Engineering and Design, 2011,32(5):1792-1795
[16]郭永乐.金属矿复杂采空区稳定性分级及其智能预测研究[D].长沙:中南大学,2012
[17]长沙拓金科技发展有限公司.大宝山井下安全现状综合评估研究报告[R].长沙:中南大学资源与安全工程学院,2005

相似文献/References:

[1]金珠,马小平.基于核校准和SVM的煤矿安全组织管理因素分析[J].中国安全生产科学技术,2011,7(3):16.
 JIN Zhu,MA Xiao-ping.Analysis of organizational administrative factors in coal mine safety based on kernel alignment and SVM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(10):16.
[2]张明丽,姚继涛.基于支持向量机建筑施工安全预警模型的研究[J].中国安全生产科学技术,2011,7(3):58.
 ZNANG Ming-li,YAO Ji-tao.Study on Warning model of construction safety based on SVM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(10):58.
[3]程爱宝,古德生,刘洪强.基于AHP与粗糙集理论的采空区稳定性影响因素权重分析[J].中国安全生产科学技术,2011,7(9):50.
 CHENG Ai-bao,GU De-sheng,LIU Hong-qiang.Weights analysis of factors affecting the stability of Mined-out areas based on analytic hierarchy process and rough sets theory[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(10):50.
[4]杨惠,陈利平,谢传欣,等.烃类及其衍生物闪点、沸点的定量构效关系[J].中国安全生产科学技术,2011,7(9):68.
 YANG Hui,CHEN Li-ping,XIE Chuan-xin,et al.QSPR study for predicting flash points and boiling points of hydrocarbon and their derivatives[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(10):68.
[5]杨力,陆红娟,张鑫,等.多类支持向量机在煤矿安全评价中的应用研究[J].中国安全生产科学技术,2012,8(4):111.
 YANG Li,LU Hong juan,ZHANG Xin,et al.Application research of multiclass support vector machines in coal mine safety evaluation[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2012,8(10):111.
[6]甘旭升,端木京顺,高建国.基于相关向量机的飞行安全评价方法[J].中国安全生产科学技术,2012,8(12):143.
 GAN Xu sheng,DUANMU Jing shun,GAO Jian guo.Flight safety evaluation method based on relevance vector machine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2012,8(10):143.
[7]甘旭升,端木京顺,丛伟,等.基于支持向量机的飞行安全隐患危险性评价[J].中国安全生产科学技术,2010,6(3):206.
 GAN Xu-sheng,DUANMU Jing-shun,CONG Wei,et al.Fatalness assessment of flight safety hidden danger based on support vector machine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2010,6(10):206.
[8]陈莹,蒋军成,潘勇,等.混合液体火灾爆炸危险性——闪点预测与实验研究[J].中国安全生产科学技术,2010,6(2):8.
 CHEN Ying,JIANG Jun-cheng,PAN Yong,et al.Fire and Explosion risk of mixture——flash point prediction and experimental study[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2010,6(10):8.
[9]姚舜才,朱红青,沈静,等.支持向量机多重分类救生舱环境评价研究[J].中国安全生产科学技术,2013,9(4):44.[doi:10.11731/j.issn.1673-193x.2013.04.008]
 YAO Shun cai,ZHU Hong qing,et al.Study on dynamic environment assessment for coal refuge chamber based on support vector machine multiclassification[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2013,9(10):44.[doi:10.11731/j.issn.1673-193x.2013.04.008]
[10]杨力,耿纪超,汪克亮.模糊支持向量机在煤与瓦斯突出预测中的研究[J].中国安全生产科学技术,2014,10(4):103.[doi:10.11731/j.issn.1673-193x.2014.04.018]
 YANG Li,GENG Ji chao,WANG Ke liang.Research on coal and gas outburst prediction using fuzzy support vector machines[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(10):103.[doi:10.11731/j.issn.1673-193x.2014.04.018]

备注/Memo

备注/Memo:
-
更新日期/Last Update: 2014-12-12