Article(id=1242127907934503269, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242127895448064607, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2011.26.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1310572800000, receivedDateStr=2011-07-14, revisedDate=1313251200000, revisedDateStr=2011-08-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1316275200000, onlineDateStr=2011-09-18, pubDate=1316275200000, pubDateStr=2011-09-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1316275200000, onlineIssueDateStr=2011-09-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774075970869, creator=sys-migrate, updateTime=1774075970869, updator=sys-migrate, issue=Issue{id=1242127895448064607, tenantId=1146029695717560320, journalId=1146031591421210625, year='2011', volume='29', issue='26', pageStart='3', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='1316275200000', pubDateStr='2011-09-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774075967893, creator='sys-migrate', updateTime=1774075967893, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=38, endPage=42, ext={EN=ArticleExt(id=1242127911864566129, articleId=1242127907934503269, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Intelligent Identification of Rock Mass Stability Based on the Fractal Characteristics of Rock Joints, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=The fracture data of ore and rock joints are analyzed on the basis of plentiful interior mines geological survey information. And fractal characteristics of spacing and orientation distribution of rock mass joints are showed by using the fractal theory. In order to find the new methods for rock mass discrimination, an intelligent identification model, which embodies the relations among rock mass stability and uniaxial compressive strength, uniaxial tensile strength, internal friction angle, cohesion force, elastic module, fractal dimension of rock joints spacing, and fractal dimension of occurrence distribution, is established by using the neural network based on chaos optimization algorithm. Fractal dimensions of rock joints spacing and occurrence distribution that embody the all-distributing information of rock mass are showed as follows: The lower the fractal dimension value of joints spacing is, the better the rock mass integrity is. And the lower the fractal dimension value of orientation distribution is, the smaller the joints dispersion degree is. It means that the lower the both of fractal dimension values are, the better the stability of rock mass will be. According to rock mechanics parameters and joints fractal characters, rock mass stability under different geological conditions could be predicted; and a basis for engineering supporting design and construction could be provided by using the intelligent identification model., authors=SHEN Yan, LI Xibing, LIU Zhixiang, authorsList=SHEN Yan;LI Xibing;LIU Zhixiang, authorCompany=School of Resources and Safety Engineering, Central South University, Changsha 410083, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=xQ+RyHETGRArH2aKPg9PPg==, pdfFileSize=1404095, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242127910828577437, articleId=1242127907934503269, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于岩石节理分形特性的岩体稳定性智能辨识, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=调查国内大量矿山地质资料,分析其矿岩节理裂隙数据,采用分形理论揭示岩体节理间距和产状分布的分形特性.采用混沌算法优化神经网络,建立矿岩稳定性与其岩石单轴抗压强度、单轴抗拉强度、内摩擦角、内聚力、弹性模量、岩体节理间距分形维数和节理产状分形维数的智能辨识模型,寻求岩体判别新方法.研究表明,岩体节理间距分形维数和产状分布分形维数可反映岩体节理空间分布的整体信息;节理间距分形维数越小,岩体完整性越好;产状分布分形维数越低,节理分散程度越小;即二者分形维数越小,岩体稳定性越好.智能辨识模型可以根据矿岩力学参数、岩体节理分形特征,预测不同地质条件下的岩体质量,为工程支护设计及施工提供依据., authors=申延, 李夕兵, 刘志祥, authorsList=申延;李夕兵;刘志祥, authorCompany=中南大学资源与安全工程学院,长沙 410083, correspAuthors=null, authorNote=null, correspAuthorsNote=李夕兵(中国科协所属全国学会个人会员登记号：S330730015M,S032200319M),教授,研究方向为岩石动力学与矿山工程灾害控制,电子信箱：xbli@mail.csu.edu.cn, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=J49ipCwWuhzHNgb9rRgmLA==, pdfFileSize=1404095, pdfExtLink=null, richHtmlUrl=null, 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