Article(id=1242129812685390703, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242129802367402820, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2013.02.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1350489600000, receivedDateStr=2012-10-18, revisedDate=1356451200000, revisedDateStr=2012-12-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1358438400000, onlineDateStr=2013-01-18, pubDate=1358438400000, pubDateStr=2013-01-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1358438400000, onlineIssueDateStr=2013-01-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774076424997, creator=sys-migrate, updateTime=1774076424997, updator=sys-migrate, issue=Issue{id=1242129802367402820, tenantId=1146029695717560320, journalId=1146031591421210625, year='2013', volume='31', issue='2', pageStart='3', pageEnd='95', issueExtLink='null', onlineDate='null', pubDate='1358438400000', pubDateStr='2013-01-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774076422537, creator='sys-migrate', updateTime=1774076422537, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=50, endPage=55, ext={EN=ArticleExt(id=1242129817982800824, articleId=1242129812685390703, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Application of Unascertained Clustering Method in the Thickness Prediction of Excavation Damaged Zone, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=Firstly, the forecast method of unascertained clustering is optimized and then is applied to the thickness prediction of Excavation Damaged Zone (EDZ). Afterwards, combined with the characteristics of underground pressure and the support theory of EDZ, five major factors of roadway, that is, depth, span, the intensity of surrounding rock, rock joint development degree, and excavation basal area are regarded as the discriminant factors for predicting the thickness of EDZ. Based on 17 groups of measured data, a classification model of EDZ thickness and the uncertainty measurement function of each factor as well as its weights are obtained. Meanwhile, the computing formula for the forecasting value is also given. Then, the classification grade for waiting forecast sample is estimated by the unascertained measurement distance, the forecasting value of EDZ thickness is also able to be calculated by combining the average value of EDZ thickness with each classification patterns. With the inspection, the computation results show that the average relative errors of the method are 5.13% and they are 13.61% and 10.17%, respectively by the methods of neutral network and support vector machine, respectively. In order to further test its reliability, the method is used to make prediction on the EDZ in Maluping mine and the predictive value is compared with the measured value as well. The results indicate that predicted value fits measured value quite well; unascertained clustering method is reliable and practical has been proved and it could be applied to the actual engineering., authors=ZHAO Guoyan, WU Hao, authorsList=ZHAO Guoyan;WU Hao, 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=O/PJITV5QQ8pZtQ4BIKp0w==, pdfFileSize=1612778, 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=1242129815428469674, articleId=1242129812685390703, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=未确知聚类方法及其在松动圈厚度预测中的应用, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=对未确知聚类方法进行优化,并将其应用于巷道围岩松动圈厚度的预测之中.结合巷道矿压显现特点和松动圈支护理论,选取巷道埋深、巷道跨度、围岩强度、围岩节理裂隙发育程度和巷道掘进断面积5个主要影响因素作为松动圈厚度的判别指标.利用17组实测数据确定松动圈厚度的分类模式和各判别指标的单指标测度函数以及指标权重,给出了松动圈厚度预测值的计算公式.基于预测对象的多指标综合测度,通过未确知测度距离判断预测对象松动圈所属的分类等级,结合各分类模式下样本松动圈厚度的均值计算松动圈厚度的预测值.经计算检验,该方法预测样本集松动圈厚度的平均相对误差为5.13%,而用神经网络方法和支持向量机方法预测该样本集松动圈厚度的平均相对误差分别为13.61%和10.17%.为进一步检验该方法的可靠度,将其应用于马路坪矿某巷道松动圈的预测中并将预测值与实测值做了比较.结果表明,采用该方法得出的预测值和实测值基本吻合.可见该方法是可靠实用的,可以在实际工程中推广应用., authors=赵国彦, 吴浩, authorsList=赵国彦;吴浩, authorCompany=中南大学资源与安全工程学院,长沙 410083, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=/yMxvxIv8AQt9vxxLuvqpw==, pdfFileSize=1612778, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, 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