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Article(id=1322595005244486036, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322594813749350688, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1711900800000, receivedDateStr=2024-04-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793260821877, onlineDateStr=2026-10-29, pubDate=1740412800000, pubDateStr=2025-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793260821877, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793260821877, creator=13701087609, updateTime=1793260821877, updator=13701087609, issue=Issue{id=1322594813749350688, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='2', pageStart='1', pageEnd='288', issueExtLink='null', onlineDate='null', pubDate='1740412800000', pubDateStr='2025-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793260776220, creator='13701087609', updateTime=1793260776220, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=9, endPage=16, ext={EN=ArticleExt(id=1322595007043842453, articleId=1322595005244486036, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Analysis on Influencing Factors of Filling Pipelines Wearing in Deep Mine Based on DEMATEL-ISM, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to better reveal the causes of filling pipelines wearing in deep mines, 12 risk influencing factors were summarized and identified based on literature analysis, standard method and expert discussion method. Using DEMATEL (decision-making trial and evaluation laboratory) and ISM (interpretative structural modeling) integrated analysis method, through the processing of index factors data, the influence degrees of index factors and the characteristics of index causal attributes were determined, and each influencing factor was decomposed according to three stages to construct a multi-level hierarchical structure model. The results are drawn as follows. The DEMATEL-ISM method can comprehensively and objectively reflect the correlation of index factors, and the corresponding hierarchical structure can be divided into 4 layers and 3 stages. The inner diameter of the pipeline and the filling times line are the fundamental causes and key influencing factors. The absolute roughness of the pipelines, the proportion of coarse particles, the mass concentration of the slurry, the ratio of slurry flow rate to critical flow rate, and the corrosivity of the filling slurry are the transitional causes. The deflection rate, the weighted average particle size, the pipelines laying quality, the aggregate shape and so on are the direct causes. The research results can provide a theoretical reference for formulating targeted measures to prevent and reduce pipelines wearing and failure.

, authors=Zhenhua ZHANG1, Zhaowen DU2, Xuehua CHEN3, authorsList=Zhenhua ZHANG, Zhaowen DU, Xuehua CHEN, authorCompany=null, correspAuthors=Zhaowen DU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, 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=1322595009350709672, articleId=1322595005244486036, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于DEMATEL-ISM的深部矿山充填管道磨损风险影响因素分析, columnId=1226462294582936453, journalTitle=矿业研究与开发, columnName=深部、深海矿产资源勘探与开发, runingTitle=null, highlight=null, articleAbstract=

为更好地揭示深部矿山充填管道磨损致因，基于文献分析法、标准法和专家讨论法，归纳识别了12个风险影响因素。应用DEMATEL(决策实验室分析法)和ISM(解释结构模型)集成分析法，通过对指标因素数据进行处理，确定指标因素影响程度和指标因果属性特征，并将各影响因素按照直接、过渡、根本3个阶次进行分解，构建多层级递阶结构模型。结果表明：DEMATEL-ISM法能够较为全面客观地体现指标因素的关联性，相应的递阶结构可以划分为4层3阶，其中管道内径、充填倍线为根本致因和关键影响因素；管道绝对粗糙度、粗颗粒占比、料浆质量浓度、料浆流速与临界流速之比、充填料浆腐蚀性等是过渡致因；偏斜率、加权平均粒径、管道铺设质量、骨料形状等是直接致因。研究结果可为制定针对性措施预防和降低管道磨损失效提供理论参考。

, authors=张振华1, 杜兆文2, 陈学华3, authorsList=张振华, 杜兆文, 陈学华, authorCompany=null, correspAuthors=杜兆文, authorNote=

张振华（1986—），男，辽宁开原人，工程师，主要从事矿山绿色智能开采技术及矿山压力与矿井动力灾害防治研究。E-mail:

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杜兆文（1991—），男，山东临沂人，博士，主要从事充填开采、采煤方法研究。E-mail:
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张振华（1986—），男，辽宁开原人，工程师，主要从事矿山绿色智能开采技术及矿山压力与矿井动力灾害防治研究。E-mail:

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张振华（1986—），男，辽宁开原人，工程师，主要从事矿山绿色智能开采技术及矿山压力与矿井动力灾害防治研究。E-mail:

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Risk evaluation of urban gas pipelines based on grey relevance theory and DEMATEL [J]. Journal of Safety and Environment, 2023, 23(9):3036-3043., articleTitle=Risk evaluation of urban gas pipelines based on grey relevance theory and DEMATEL, refAbstract=null)], funds=[Fund(id=1322595017160503769, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, awardId=52204141, language=CN, fundingSource=国家自然科学基金项目(52204141), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322595009879191977, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, xref=1., ext=[AuthorCompanyExt(id=1322595009891774890, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, companyId=1322595009879191977, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.Zhaojin Nonferrous Mining Co., Ltd., Yantai, Shandong 265400, China), AuthorCompanyExt(id=1322595010118267307, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, companyId=1322595009879191977, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.招金有色矿业有限公司，山东　烟台市　265400)]), AuthorCompany(id=1322595011493999020, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, xref=2., ext=[AuthorCompanyExt(id=1322595011523359149, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, companyId=1322595011493999020, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.School of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China), AuthorCompanyExt(id=1322595011535942062, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, companyId=1322595011493999020, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.山东科技大学 能源与矿业工程学院，山东　青岛市　266590)]), AuthorCompany(id=1322595011850514863, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, xref=3., ext=[AuthorCompanyExt(id=1322595011867292080, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, companyId=1322595011850514863, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.School of Mining, Liaoning University of Engineering and Technology, Fuxin, Liaoning 123000, China), AuthorCompanyExt(id=1322595011884069297, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, companyId=1322595011850514863, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.辽宁工程技术大学 矿业学院，辽宁　阜新市　123000)])], figs=[ArticleFig(id=1322595014035747273, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=EN, label=Fig.1, caption=Implementation process of DEMATEL-ISM model, figureFileSmall=LdoocXasFg8KgftqURf/7A==, figureFileBig=0m/SBnCwAK9fxhr3u591PQ==, tableContent=null), ArticleFig(id=1322595014132216266, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=CN, label=图1, caption=DEMATEL-ISM模型实现过程, figureFileSmall=LdoocXasFg8KgftqURf/7A==, figureFileBig=0m/SBnCwAK9fxhr3u591PQ==, tableContent=null), ArticleFig(id=1322595015965127115, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=EN, label=Fig.2, caption=Causal relationship of influencing factors between centrality and causality, figureFileSmall=WvsJdam40i/v5kGzD8gz5w==, figureFileBig=oGYy4iFSPGcnTMj3tSs9Og==, tableContent=null), ArticleFig(id=1322595016053207500, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=CN, label=图2, caption=影响因素中心度-原因度因果关系, figureFileSmall=WvsJdam40i/v5kGzD8gz5w==, figureFileBig=oGYy4iFSPGcnTMj3tSs9Og==, tableContent=null), ArticleFig(id=1322595016149676493, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=EN, label=Fig.3, caption=Hierarchical structure of influencing factors, figureFileSmall=kwKOank3pzwGvvVDtGEuGA==, figureFileBig=O2oQTFhiiYIgoyWhL7epzg==, tableContent=null), ArticleFig(id=1322595016229368270, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=CN, label=图3, caption=影响因素递阶结构, figureFileSmall=kwKOank3pzwGvvVDtGEuGA==, figureFileBig=O2oQTFhiiYIgoyWhL7epzg==, tableContent=null), ArticleFig(id=1322595016313254351, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=EN, label=Table 1, caption=

Summary of influencing factors

, figureFileSmall=null, figureFileBig=null, tableContent=
编号影响因素编号影响因素
X1料浆质量浓度X7管道铺设的不平整度
X2料浆密度X8管道内径
X3偏斜率X9粗颗粒占比
X4管道绝对粗糙度X10料浆流速与临界流速之比
X5充填倍线X11充填骨料形状
X6加权平均粒径X12充填料浆腐蚀性
), ArticleFig(id=1322595016392946128, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=CN, label=表1, caption=

影响因素汇总

, figureFileSmall=null, figureFileBig=null, tableContent=
编号影响因素编号影响因素
X1料浆质量浓度X7管道铺设的不平整度
X2料浆密度X8管道内径
X3偏斜率X9粗颗粒占比
X4管道绝对粗糙度X10料浆流速与临界流速之比
X5充填倍线X11充填骨料形状
X6加权平均粒径X12充填料浆腐蚀性
), ArticleFig(id=1322595016489415121, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=EN, label=Table 2, caption=

Comprehensive impact matrix

, figureFileSmall=null, figureFileBig=null, tableContent=
指标X1X2X3X4X5X6X7X8X9X10X11X12
X10.427 20.476 60.558 00.565 70.503 40.467 40.586 60.529 10.487 10.565 70.466 40.391 5
X20.439 30.345 70.483 50.501 90.456 70.412 00.514 20.470 60.416 20.494 60.408 40.335 5
X30.416 70.383 60.389 20.494 30.436 20.403 10.486 60.444 80.414 60.487 20.389 40.318 4
X40.458 40.423 20.473 90.433 30.460 70.450 30.529 50.500 10.471 60.512 00.425 30.365 4
X50.534 50.483 70.601 20.598 30.471 40.526 00.615 60.593 10.534 00.612 10.507 90.433 4
X60.437 90.400 30.456 30.460 30.412 60.348 50.485 60.462 40.441 70.478 50.397 20.332 7
X70.459 00.424 20.477 90.504 70.464 00.440 40.441 10.490 20.439 60.507 60.413 90.385 1
X80.533 90.494 50.580 60.572 70.548 10.503 60.594 50.478 00.511 40.595 60.474 90.411 8
X90.464 30.426 20.500 60.500 20.483 70.463 60.523 70.504 00.386 00.519 10.429 00.352 7
X100.483 10.441 40.526 20.546 80.515 70.462 20.538 50.512 10.462 40.458 30.432 50.377 1
X110.450 20.414 10.479 70.486 60.439 70.432 70.492 40.483 40.442 00.487 80.344 30.334 7
X120.428 40.392 70.487 60.488 60.446 10.398 80.500 30.446 30.397 80.478 00.383 40.285 7
), ArticleFig(id=1322595016569106898, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=CN, label=表2, caption=

综合影响矩阵

, figureFileSmall=null, figureFileBig=null, tableContent=
指标X1X2X3X4X5X6X7X8X9X10X11X12
X10.427 20.476 60.558 00.565 70.503 40.467 40.586 60.529 10.487 10.565 70.466 40.391 5
X20.439 30.345 70.483 50.501 90.456 70.412 00.514 20.470 60.416 20.494 60.408 40.335 5
X30.416 70.383 60.389 20.494 30.436 20.403 10.486 60.444 80.414 60.487 20.389 40.318 4
X40.458 40.423 20.473 90.433 30.460 70.450 30.529 50.500 10.471 60.512 00.425 30.365 4
X50.534 50.483 70.601 20.598 30.471 40.526 00.615 60.593 10.534 00.612 10.507 90.433 4
X60.437 90.400 30.456 30.460 30.412 60.348 50.485 60.462 40.441 70.478 50.397 20.332 7
X70.459 00.424 20.477 90.504 70.464 00.440 40.441 10.490 20.439 60.507 60.413 90.385 1
X80.533 90.494 50.580 60.572 70.548 10.503 60.594 50.478 00.511 40.595 60.474 90.411 8
X90.464 30.426 20.500 60.500 20.483 70.463 60.523 70.504 00.386 00.519 10.429 00.352 7
X100.483 10.441 40.526 20.546 80.515 70.462 20.538 50.512 10.462 40.458 30.432 50.377 1
X110.450 20.414 10.479 70.486 60.439 70.432 70.492 40.483 40.442 00.487 80.344 30.334 7
X120.428 40.392 70.487 60.488 60.446 10.398 80.500 30.446 30.397 80.478 00.383 40.285 7
), ArticleFig(id=1322595016652992979, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=EN, label=Table 3, caption=

“Four degrees” calculation results of DEMATEL model

, figureFileSmall=null, figureFileBig=null, tableContent=
指标因素影响度Fi影响度排序被影响度Hi被影响度排序中心度Mi原因度Ni中心度排序因素属性
X16.024 735.532 8711.557 50.491 86原因因素
X25.278 695.106 11010.384 70.172 410原因因素
X35.064 0126.014 6411.078 6–0.950 77结果因素
X45.503 966.153 6311.657 5–0.649 75结果因素
X56.511 315.638 2612.149 50.873 02原因因素
X65.114 0115.308 6910.422 6–0.194 69结果因素
X75.447 876.308 7111.756 5–0.860 94结果因素
X86.299 625.914 1512.213 70.385 61原因因素
X95.553 255.404 6810.957 80.148 68原因因素
X105.756 346.196 4211.952 7–0.440 13结果因素
X115.287 485.072 81110.360 20.214 611原因因素
X125.133 8104.324 0129.457 80.809 812原因因素
), ArticleFig(id=1322595016762044884, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=CN, label=表3, caption=

DEMATEL模型“四度”计算结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标因素影响度Fi影响度排序被影响度Hi被影响度排序中心度Mi原因度Ni中心度排序因素属性
X16.024 735.532 8711.557 50.491 86原因因素
X25.278 695.106 11010.384 70.172 410原因因素
X35.064 0126.014 6411.078 6–0.950 77结果因素
X45.503 966.153 6311.657 5–0.649 75结果因素
X56.511 315.638 2612.149 50.873 02原因因素
X65.114 0115.308 6910.422 6–0.194 69结果因素
X75.447 876.308 7111.756 5–0.860 94结果因素
X86.299 625.914 1512.213 70.385 61原因因素
X95.553 255.404 6810.957 80.148 68原因因素
X105.756 346.196 4211.952 7–0.440 13结果因素
X115.287 485.072 81110.360 20.214 611原因因素
X125.133 8104.324 0129.457 80.809 812原因因素
), ArticleFig(id=1322595016833348053, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=EN, label=Table 4, caption=

Comprehensive impact matrix

, figureFileSmall=null, figureFileBig=null, tableContent=
指标X1X2X3X4X5X6X7X8X9X10X11X12
X11.427 20.476 60.558 00.565 70.503 40.467 40.586 60.529 10.487 10.565 70.466 40.391 5
X20.439 31.345 70.483 50.501 90.456 70.412 00.514 20.470 60.416 20.494 60.408 40.335 5
X30.416 70.383 61.389 20.494 30.436 20.403 10.486 60.444 80.414 60.487 20.389 40.318 4
X40.458 40.423 20.473 91.433 30.460 70.450 30.529 50.500 10.471 60.512 00.425 30.365 4
X50.534 50.483 70.601 20.598 31.471 40.526 00.615 60.593 10.534 00.612 10.507 90.433 4
X60.437 90.400 30.456 30.460 30.412 61.348 50.485 60.462 40.441 70.478 50.397 20.332 7
X70.459 00.424 20.477 90.504 70.464 00.440 41.441 10.490 20.439 60.507 60.413 90.385 1
X80.533 90.494 50.580 60.572 70.548 10.503 60.594 51.478 00.511 40.595 60.474 90.411 8
X90.464 30.426 20.500 60.500 20.483 70.463 60.523 70.504 01.386 00.519 10.429 00.352 7
X100.483 10.441 40.526 20.546 80.515 70.462 20.538 50.512 10.462 41.458 30.432 50.377 1
X110.450 20.414 10.479 70.486 60.439 70.432 70.492 40.483 40.442 00.487 81.344 30.334 7
X120.428 40.392 70.487 60.488 60.446 10.398 80.500 30.446 30.397 80.478 00.383 41.285 7
), ArticleFig(id=1322595016921428438, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=CN, label=表4, caption=

整体影响矩阵

, figureFileSmall=null, figureFileBig=null, tableContent=
指标X1X2X3X4X5X6X7X8X9X10X11X12
X11.427 20.476 60.558 00.565 70.503 40.467 40.586 60.529 10.487 10.565 70.466 40.391 5
X20.439 31.345 70.483 50.501 90.456 70.412 00.514 20.470 60.416 20.494 60.408 40.335 5
X30.416 70.383 61.389 20.494 30.436 20.403 10.486 60.444 80.414 60.487 20.389 40.318 4
X40.458 40.423 20.473 91.433 30.460 70.450 30.529 50.500 10.471 60.512 00.425 30.365 4
X50.534 50.483 70.601 20.598 31.471 40.526 00.615 60.593 10.534 00.612 10.507 90.433 4
X60.437 90.400 30.456 30.460 30.412 61.348 50.485 60.462 40.441 70.478 50.397 20.332 7
X70.459 00.424 20.477 90.504 70.464 00.440 41.441 10.490 20.439 60.507 60.413 90.385 1
X80.533 90.494 50.580 60.572 70.548 10.503 60.594 51.478 00.511 40.595 60.474 90.411 8
X90.464 30.426 20.500 60.500 20.483 70.463 60.523 70.504 01.386 00.519 10.429 00.352 7
X100.483 10.441 40.526 20.546 80.515 70.462 20.538 50.512 10.462 41.458 30.432 50.377 1
X110.450 20.414 10.479 70.486 60.439 70.432 70.492 40.483 40.442 00.487 81.344 30.334 7
X120.428 40.392 70.487 60.488 60.446 10.398 80.500 30.446 30.397 80.478 00.383 41.285 7
), ArticleFig(id=1322595016992731607, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=EN, label=Table 5, caption=

Hierarchical division results of indicator factors

, figureFileSmall=null, figureFileBig=null, tableContent=
因素(Xi)可达集合R(Xi)先行集合Q(Xi)共同集C(Xi)层级
X1X1,X2,X3,X4,X7,X8,X10X1,X5,X8,X10X1,X8,X103
X2X2,X7X1,X2,X8X22
X3X3X1,X3,X5,X8,X10,X12X31
X4X4,X7X1,X4,X5,X8,X10,X12X42
X5X1,X3,X4,X5,X6,X7,X8,X9,X10,X11X5,X8X5,X84
X6X6X5,X6X61
X7X7X1,X2,X4,X5,X7,X8,X9,X10,X12X71
X8X1,X2,X3,X4,X5,X7,X8,X10X1,X5,X8X1,X5,X84
X9X7,X9X5,X9X92
X10X1,X3,X4,X7,X10X1,X5,X8,X10X1,X103
X11X11X5,X11X111
X12X3,X4,X7,X12X12X123
), ArticleFig(id=1322595017064034776, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322595005244486036, language=CN, label=表5, caption=

指标因素层次结构划分结果

, figureFileSmall=null, figureFileBig=null, tableContent=
因素(Xi)可达集合R(Xi)先行集合Q(Xi)共同集C(Xi)层级
X1X1,X2,X3,X4,X7,X8,X10X1,X5,X8,X10X1,X8,X103
X2X2,X7X1,X2,X8X22
X3X3X1,X3,X5,X8,X10,X12X31
X4X4,X7X1,X4,X5,X8,X10,X12X42
X5X1,X3,X4,X5,X6,X7,X8,X9,X10,X11X5,X8X5,X84
X6X6X5,X6X61
X7X7X1,X2,X4,X5,X7,X8,X9,X10,X12X71
X8X1,X2,X3,X4,X5,X7,X8,X10X1,X5,X8X1,X5,X84
X9X7,X9X5,X9X92
X10X1,X3,X4,X7,X10X1,X5,X8,X10X1,X103
X11X11X5,X11X111
X12X3,X4,X7,X12X12X123
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基于DEMATEL-ISM的深部矿山充填管道磨损风险影响因素分析
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张振华 1 , 杜兆文 2 , 陈学华 3
矿业研究与开发 | 深部、深海矿产资源勘探与开发 2025,45(2): 9-16
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矿业研究与开发 |深部、深海矿产资源勘探与开发 2025 , 45 (2) : 9 -16
基于DEMATEL-ISM的深部矿山充填管道磨损风险影响因素分析
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张振华1 , 杜兆文2 , 陈学华3
作者信息
  • 1.招金有色矿业有限公司，山东　烟台市　265400
  • 2.山东科技大学 能源与矿业工程学院，山东　青岛市　266590
  • 3.辽宁工程技术大学 矿业学院，辽宁　阜新市　123000
通讯作者:
杜兆文（1991—），男，山东临沂人，博士，主要从事充填开采、采煤方法研究。E-mail:
作者简介:

张振华（1986—），男，辽宁开原人，工程师，主要从事矿山绿色智能开采技术及矿山压力与矿井动力灾害防治研究。E-mail:

Analysis on Influencing Factors of Filling Pipelines Wearing in Deep Mine Based on DEMATEL-ISM
Zhenhua ZHANG1 , Zhaowen DU2 , Xuehua CHEN3
Affiliations
  • 1.Zhaojin Nonferrous Mining Co., Ltd., Yantai, Shandong 265400, China
  • 2.School of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
  • 3.School of Mining, Liaoning University of Engineering and Technology, Fuxin, Liaoning 123000, China
出版时间: 2025-02-25
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为更好地揭示深部矿山充填管道磨损致因，基于文献分析法、标准法和专家讨论法，归纳识别了12个风险影响因素。应用DEMATEL(决策实验室分析法)和ISM(解释结构模型)集成分析法，通过对指标因素数据进行处理，确定指标因素影响程度和指标因果属性特征，并将各影响因素按照直接、过渡、根本3个阶次进行分解，构建多层级递阶结构模型。结果表明：DEMATEL-ISM法能够较为全面客观地体现指标因素的关联性，相应的递阶结构可以划分为4层3阶，其中管道内径、充填倍线为根本致因和关键影响因素；管道绝对粗糙度、粗颗粒占比、料浆质量浓度、料浆流速与临界流速之比、充填料浆腐蚀性等是过渡致因；偏斜率、加权平均粒径、管道铺设质量、骨料形状等是直接致因。研究结果可为制定针对性措施预防和降低管道磨损失效提供理论参考。

DEMATEL-ISM  /  充填管道磨损  /  影响因素  /  多层级递阶结构

In order to better reveal the causes of filling pipelines wearing in deep mines, 12 risk influencing factors were summarized and identified based on literature analysis, standard method and expert discussion method. Using DEMATEL (decision-making trial and evaluation laboratory) and ISM (interpretative structural modeling) integrated analysis method, through the processing of index factors data, the influence degrees of index factors and the characteristics of index causal attributes were determined, and each influencing factor was decomposed according to three stages to construct a multi-level hierarchical structure model. The results are drawn as follows. The DEMATEL-ISM method can comprehensively and objectively reflect the correlation of index factors, and the corresponding hierarchical structure can be divided into 4 layers and 3 stages. The inner diameter of the pipeline and the filling times line are the fundamental causes and key influencing factors. The absolute roughness of the pipelines, the proportion of coarse particles, the mass concentration of the slurry, the ratio of slurry flow rate to critical flow rate, and the corrosivity of the filling slurry are the transitional causes. The deflection rate, the weighted average particle size, the pipelines laying quality, the aggregate shape and so on are the direct causes. The research results can provide a theoretical reference for formulating targeted measures to prevent and reduce pipelines wearing and failure.

DEMATEL-ISM  /  Filling pipelines wearing  /  Influencing factor  /  Multi-level hierarchical structure
张振华, 杜兆文, 陈学华. 基于DEMATEL-ISM的深部矿山充填管道磨损风险影响因素分析. 矿业研究与开发, 2025 , 45 (2) : 9 -16 .
Zhenhua ZHANG, Zhaowen DU, Xuehua CHEN. Analysis on Influencing Factors of Filling Pipelines Wearing in Deep Mine Based on DEMATEL-ISM[J]. Mining Research and Development, 2025 , 45 (2) : 9 -16 .
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0　引言
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充填采矿法能够最大程度提高矿产资源综合利用率和减少地表尾矿堆积，进一步提升地下金属矿山开采安全性[1]。应用充填采矿法回采矿石或进行采空区治理，首先需要构建一套可靠、完善的充填系统，然后依靠管输系统将料浆输送到地下采场。但随着矿山开采逐渐深部化，充填管网系统日趋复杂，高质量浓度料浆在输送过程中会对管道产生剧烈摩擦、冲蚀作用，容易引发磨损、跑浆等管道失效事故[2-3]。因此，开展深部矿山充填管道磨损风险影响因素分析，研究充填管道磨损机理，及时采取针对性防范措施，进一步延缓和减少磨损速度与磨损量，对于保证矿山企业安全、高效、经济生产意义重大。
目前，在充填管道磨损风险理论研究方面，常英杰等[4]运用REM法研究了管道内径、弯径比等管道参数对充填管道磨损的影响机理，优化设计了管道参数。石宏伟等[5]分析了自流充填法浆体、管材、倍线等与管道磨损的影响关系，并运用数值模拟软件研究了水平管道及弯管主要磨损位置，最后提出了降低管道磨损的针对性措施。毛明发等[6]利用控制变量法，定量分析了管径、体积浓度、密度、充填倍线等因素对管道磨损的影响程度，结果显示内径、倍线是管道磨损的主要诱因。WANG XL等[7]通过自制磨损试验仪分别探究了料浆质量浓度（76%~84%）、料浆流速（1.0~2.5 m/s）、粗骨料含量（0~35%）、粗骨料颗粒粒径（1.05~7.34 mm）4个单指标因素对管道磨损的影响，结果显示磨损率随料浆流速和粗骨料颗粒粒径增大呈非线性增加，与料浆质量浓度和粗骨料含量呈非单调趋势。通过分析发现，现有的理论研究成果大多通过理论模型、数值模拟、试验设计等途径研究单一指标因素的管道磨损机理，但是，对指标因素内在作用关系缺乏全面深入的研究与分析。而实际生产中，充填管道磨损失效不仅与某个单指标因素有关，而是多个指标因素综合影响的结果。DEMATEL法可以研究系统中各指标因素的因果关系，准确识别关键指标因素，而ISM法可以为复杂问题的分析构建层次分明的结构模型，了解因素之间的影响关系。在矿业领域研究方面，郭慧敏等[8]应用DEMATEL-ISM法定量分析了煤矿瓦斯爆炸致因。李广利等[9]构建了矿工不安全情绪形成因子DEMATEL-ISM模型。罗周全等[10]利用DEMATEL-ISM法开展了矿山人机系统事故影响因素分析。
鉴于此，本文将 DEMATEL-ISM集成分析法引入到充填管道磨损风险影响因素分析中，研究各指标因素的作用机理，实现充填管道磨损风险影响因素的层次分解，构建指标因素多层级递阶结构模型，以期为制定针对性预防措施和减少管道磨损失效的发生提供理论参考。
1　充填管道磨损风险因素识别
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首先，通过知网、万方、Web of Science等数据库检索“充填管道磨损”“充填管道失效”“充填管道冲蚀”等与本文研究内容相关联的关键词，从相应研究成果中筛选、提取指标因素18 个。然后，根据《金属非金属矿山充填工程技术标准》(GB/T 51450–2022)等相关现行标准规范，确定指标因素17个。最后，组织行业专家讨论，对 35个指标因素去重、归纳、总结处理。最终，从浆体特性、管道本质属性、管路现场安装质量和充填技术4个方面，结合工程实际，综合确定了 12个影响因素。其中，料浆特性方面，主要考虑料浆质量浓度、粗颗粒占比、骨料形状及浆体自身腐蚀性等因素，实践证明管道磨损速度随上述因素的增大而加快；在管道属性方面，主要包括管径、管道材质等因素，一般情况下，耐磨型管道使用寿命更长，而同流速下，管径越小管路摩阻越大，管道发生破损概率大大增加；在施工质量方面，将钻孔偏斜率、管道铺设安装质量纳入指标因素范围，偏斜率越小，垂管安装质量的垂直度和同心度越好，磨损速度慢；在充填技术方面，主要选取了充填倍线与料浆流速等因素，充填倍线越小，管输流速越大，管道受浆体冲击力越大，管道磨损越严重。具体风险影响因素见表1。
2　研究方法
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DEMATEL(决策实验室分析法)是基于图论和矩阵运算，通过计算指标因素中心度和原因度，量化分析复杂系统中指标因素间的内在关联性，筛选和识别关键影响因素，但是单纯使用 DEMATEL无法科学体现各指标因素的层次关系和影响路径[11-12]。ISM(解释结构模型)通过可达矩阵实现各指标因素的多层级递阶结构可视化，为揭示指标因素间的逻辑因果关系提供科学指导，但是该方法不能全面客观体现各层次指标因素在系统中的重要程度[13]。将 DEMATEL 和 ISM 法集成，能够充分发挥两大模型在定性分析方面的突出优势，弥补各自的缺陷，既能客观量化指标因素的重要性并确定指标因素属性特征，又能揭示层级关系，使复杂、逻辑不清等问题的分析与处理更加科学合理。DEMATEL-ISM模型具体实施步骤见图1。
2.1　DEMATEL法
本文选取了12个影响因素，构建了充填管道磨损风险影响因素体系，指标体系相对复杂，各指标因素对管道磨损失效的影响程度存在差异，且指标因素之间相互作用和交互影响，导致难以准确筛选关键影响因素。DEMATEL法主要依据专家对指标因素相互影响程度赋值，然后经过一系列计算，用中心度确定关键影响关系，用原因度揭示各指标因素间的相互影响关系。DEMATEL方法具体计算过程如下[11-12]。
（1）建立直接影响矩阵A。
根据筛选的充填管道磨损风险影响因素指标体系，组织该领域的评估专家，按照0~4标度法，量化各指标因素间的相互影响程度。0~4标度法规定：0表示两指标因素间没有显著影响，1表示两指标因素间影响程度较小，2表示两指标因素间影响程度一般，3表示两指标因素间影响程度较强，4表示两指标因素间具有强烈影响。通过汇总专家影响程度赋值，构建直接影响矩阵A(A=[aij]n×n)。
（2）计算综合影响矩阵T。
取直接影响矩阵A(A=[aij]n×n)行和的最大值，对直接影响矩阵A进行规范化处理，使指标因素规范值区间为[0,1]，得到规范化影响矩阵B。
根据规范化矩阵B(B=[bij]n×n)，结合单位矩阵E，利用MATLAB软件，计算综合影响矩阵T(T=[tij]n×n)。
因为0≤bij≤1，所以Bn−1会趋近于零矩阵，故上式可简化为：
（3）计算指标因素“四度”。
为量化充填管道磨损风险影响因素之间的相互影响程度，需要计算各指标的影响度Fi和被影响度Hi。影响度表示第i个指标对其他指标的影响程度，被影响度表征第i个指标受其他影响因素的影响程度。根据DEMATEL模型原理，对综合影响矩阵T(T=[tij]n×n)中的tij值按行累加，得到第i个指标因素的影响度Fi，按列求和计算第i个指标因素的被影响度Hi。
DEMATEL模型通过指标因素中心度值Mi衡量第i个指标因素在充填管道磨损风险影响因素体系中的重要性，中心度值Mi越大，表示该指标在整个评价体系中越关键，是识别评价体系中核心影响因素的主要判据。DEMATEL模型利用原因度Ni整体反映第i个指标因素在评价体系中的综合影响度，并根据原因度Ni的正、负情况，将指标因素划分原因因素和结果因素两类，当原因度Ni大于0时，表明第i个因素对其他因素影响明显，是引发潜在风险的推动因素，将该指标因素列为原因因素；当原因度Ni小于0时，表明第i个因素受其他因素影响显著，是风险发生的直接因素，将该指标因素列为结果因素。
（4）绘制因果关系图。
利用笛卡尔坐标系，绘制中心度-原因度因果关系图。
2.2　构建ISM模型
ISM模型可以根据指标因素的作用关系，将指标因素层次分解，最终以可视化方式呈现指标因素间的内在逻辑关系，将复杂性、关联性、结构不清的系统分析直观展现，可以为探析充填管道磨损的直接致因、过渡致因和根本致因提供思路，具体步骤如下[14]。
（1）计算整体影响矩阵G和可达性矩阵K。
综合影响矩阵T仅能判断不同指标因素之间的影响程度，忽略了指标因素本身对系统的影响。因此，ISM模型引入了整体影响矩阵G的概念。整体影响矩阵G能够全面体现充填管道磨损风险影响因素之间和因素自身的整体影响关系，由综合影响矩阵T与单位矩阵E之和进行确定。具体计算公式为：
根据整体影响矩阵G可以分析指标因素之间影响关系的复杂程度，如果将所有指标因素影响关系全部展现，层次阶级非常庞杂，难以发现重要影响路径和剖析关键影响关系。为此，引入阈值λ，将整体影响矩阵G中影响关系较弱的关联信息进行剔除，简化系统结构，通过计算可达矩阵K构建多级递阶结构模型。
通过查阅相关文献，阈值λ一般是由专家结合自身工作经验进行取值，λ∈[0,1]，这种取值方式具有主观任意性[15]。为保证计算结果的客观性，基于统计分布法，计算综合影响矩阵T的指标因素平均值α与标准差β之和确定本文阈值[16-17]。
（2）基于可达矩阵K划分层次结构。
根据可达矩阵K，定义可达集R(Xi)、先行集Q(Xi)和共同集C(Xi)，其中，可达集R(Xi)为可达矩阵K中第Xi个指标因素对应行kij值等于1的相应指标因素集合，先行集Q(Xi)为可达矩阵K中第Xi个指标因素对应列kij等于1的相应指标因素集合，共同集C(Xi)是可达集R(Xi)和先行集Q(Xi)的共同指标的集合。采用结果优先抽取法对各指标因素划分层级，即共同集C(Xi)与可达集R(Xi)中相同的指标因素划分到同一层级，并将该指标因素确定为多层递阶结构的第一层，然后将第一层所有指标因素从可达矩阵K中剔除后，对剩余指标因素循环操作步骤，直到所有指标因素全部依次确定相应层级。
（3）绘制递阶结构图。
结合影响因素层级划分结果，绘制多级递阶结构模型图。
3　实证研究与结果分析
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结合充填管道磨损风险影响因素体系，基于 DEMATEL-ISM法，开展指标因素作用关系深入分析与研究。已有文献和研究中并未对DEMATEL-ISM 集成分析法专家数量进行具体规定，张鹏等[18]组织了 8位专家研究了城市燃气管道风险因素影响关系；苑东亮等[14]邀请了 5位专家对采空区稳定性致因因素进行识别。参考以往学者应用成功案例，一般专家选取4~10位。因此，本文邀请了 3 名充填采矿领域的专家学者和 2 名现场管理人员，组建5 人专家小组，通过专家访谈打分形式，由专家结合自身经验，按照0~4的影响关系判别原则，确定指标因素相互影响程度。然后，对各位专家分数汇总均值化处理，最终确定12个指标因素的相互影响关系，构建直接影响矩阵A，并对矩阵A进行规范化处理。
3.1　DEMATEL法指标属性的确定
根据式(1)、式(2)，运行MATLAB R2022a软件，计算综合影响矩阵T，结果见表2。根据式(3)、式(4)，计算指标因素影响度、被影响度、中心度和原因度。根据中心度数值大小，进行指标因素重要性排序，根据原因度正负值，划分原因因素和结果因素，确定指标因素属性，见表3。根据表3，在笛卡尔坐标系中绘制指标因素中心度-原因度因果关系，如图2所示。
3.2　ISM模型构建递阶结构
应用 ISM模型进一步分析研究各指标因素在充填管道磨损风险指标体系中的层次结构。根据式(5), 计算整体影响矩阵G, 具体计算结果见表4。按照统计分布方法，计算影响关系阈值，其中，α=0.4651,β=0.0641, 则阈值λ=0.5292, 根据式(6)，计算可达矩阵K。然后按照 ISM 模型指标层次划分原理，结合可达矩阵K, 经过多次重复操作，最终确定充填管道磨损风险 12 个指标因素的层次结构。指标因素层级划分结果见表5，指标因素递阶结构见图3。
3.3　结果分析
（1）原因度分析。原因度是划分指标因素属性的重要依据。当指标原因度为正数时，该指标因素为原因因素，表明在充填管道磨损评价指标体系中，该指标因素易对其他指标因素产生影响作用，而该指标因素不易受到其他指标因素影响，对于此类指标因素，矿山企业要重点防控和持续优化；当指标原因度为负数时，该指标因素归类为结果因素，表明该指标因素更易受到其他指标因素影响，在充填过程中，矿山企业应加强对该类指标因素的重点监测。在整个指标体系中，指标因素原因度绝对值越大，引发深井充填管道磨损失效概率越大。由表3可知，在12个指标因素中，原因因素有7个，结果因素有5个。
针对原因因素，按照原因度数值大小降序排列依次为：充填倍线X5、充填料浆腐蚀性X12、料浆质量浓度X1、管道内径X8、充填骨料形状X11、料浆密度X2、粗颗粒占比X9，其中充填倍线X5、充填料浆腐蚀性X12、料浆质量浓度X1和管道内径X8为突出原因因素，是深井充填管道磨损失效的主要因子。矿山企业要合理设计充填钻孔长度和水平管道长度，合理控制充填倍线；要通过正交试验或大量现场试验实现充填配比的优化，科学确定料浆质量浓度和控制料浆酸碱度；通过大型数值模拟软件合理设计管径。
针对结果因素，按照原因度数值绝对值大小降序排列依次为：偏斜率X3、管道铺设的不平整度X7、管道绝对粗糙度X4、料浆流速与临界流速之比X10、加权平均粒径X6, 其中排序前 3的结果因素主要与管道属性和充填施工工艺相关。在矿山充填实践中，要注意控制钻孔偏斜率、注重管道安装质量和选用高品质充填管道，以提升充填管道系统稳定性。
（2）中心度分析。中心度主要体现了各指标因素在充填管道磨损指标体系中的重要性，中心度越大，表明该指标因素影响程度越显著。由表3及图2可知，充填管道磨损影响因素中心度排序排名前五的影响因素依次为：管道内径X8、充填倍线X5、料浆流速与临界流速之比X10、管道铺设的不平整度X7、管道绝对粗糙度X4。这5个指标因素与其他因素之间的相互作用关系密切，是充填管道磨损失效的关键风险因素，需要重点控制和优化设计。综合来看，充填倍线X5中心度数值和原因度数值均较大，表明该指标是充填管道磨损最突出的风险指标因素，占据核心地位。
（3）层次结构分析。由图3可知，充填管道磨损风险指标因素体系可以分为3阶4层。其中，直接致因层包括偏斜率X3、加权平均粒径X6、管道铺设的不平整度X7、充填骨料形状X11，这些因素易受其他因素影响，是导致充填管道磨损失效现象的最直接因素。中间过渡致因层包括料浆密度X2、管道绝对粗糙度X4、粗颗粒占比X9、料浆质量浓度X1、料浆流速与临界流速之比X10、充填料浆腐蚀性X12，上述这些指标因素是导致管道失效的间接因素。通过观察影响路径箭头发现，过渡致因层因素主要通过管道铺设的不平整度X7间接对管道磨损发生作用。深井充填管道系统存在管路布置复杂、弯管多、管径变幅较大等特点，这些均易引发管道磨损问题，在管道安装质量不高与管道自身腐蚀双重作用下，将进一步加剧管道磨损程度，从而出现跑浆、漏浆和钻孔堵塞等情况，影响井下正常回采作业和充填采空区，造成经济损失甚至人员伤亡等安全事故。最后，根本致因层包括了管道内径X8和充填倍线X5，即两个指标因素是造成管道磨损的根本性因素，对其他指标因素产生深刻影响，是管道磨损风险防范最重要因素。因此，在设计充填管道时，需要反复测算与论证，才能从源头上降低管道磨损风险，实现管道长久稳定。
通过多级递阶结构图既可以了解单指标因素对充填管道磨损的影响程度，也可以探究多指标因素交互作用对管道磨损的影响情况。由图3可以发现，管道内径X8与充填倍线X5、料浆质量浓度X1与料浆流速与临界流速之比X10之间存在双向箭头，这表明管道内径与充填倍线、料浆质量浓度与料浆流速的交互作用长期对管道磨损具有相对突出的影响作用。
另外，对比发现充填倍线X5和管道内径X8两个指标因素均属于底层根本致因层，同时也是正值原因度数值排在前二的指标因素；偏斜率X3、管道铺设的不平整度X7两个指标因素均为表层致因层因素，同时也是结果因素中排序前二的指标因素，而其他因素在层次结构中的位置与DEMATEL 模型所得出的结论呼应。这体现了 DEMATEL 模型与 ISM模型在充填管道磨损风险因素关联性分析和关键因素识别方面具有高度一致性，表明DEMATEL-ISM 在充填管道磨损风险影响因素分析方面具有适应性。
4　结论
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（1）采用文献分析、规范标准法和专家讨论法相结合的方式，从浆体特性、管道本质属性、管路现场安装质量和充填技术4个方面，识别归纳了12个充填管道磨损风险影响因素，构建了较为完善的指标体系。
（2）考虑到充填管道磨损风险影响因素较多、具有较强关联性的特点，应用DEMATEL方法，通过计算指标中心度和原因度，确定指标因素属性特性，识别关键指标因素。结果显示导致管道磨损失效问题包括了7 个原因因素和 5个结果因素，其中管道内径、充填倍线、料浆流速与临界流速之比、管道铺设不平整度、管道绝对粗糙度为关键因素，需要矿山企业重点关注。
（3）运用 DEMATEL-ISM集成分析法，对充填管道磨损风险影响因素进行了层级划分，得到了3阶 4层的递阶结构模型，其中，管道内径、充填倍线为根本致因，管道绝对粗糙度、粗颗粒占比、料浆质量浓度、料浆流速与临界流速之比、充填料浆腐蚀性等是过渡致因，钻孔偏斜率、加权平均粒径、管道铺设不平整度、充填骨料形状等是直接致因。根据结构模型，可以清楚了解风险因素之间的相互作用关系与影响路径，可以为后续制定针对性措施、降低充填管道磨损失效现象提供理论参考。
基金
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  • 国家自然科学基金项目(52204141)
文献
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2025年第45卷第2期
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  • 接收时间：2024-04-01
  • 首发时间：2026-10-29
  • 出版时间：2025-02-25
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  • 收稿日期：2024-04-01
基金
国家自然科学基金项目(52204141)
作者信息
    1.招金有色矿业有限公司，山东　烟台市　265400
    2.山东科技大学 能源与矿业工程学院，山东　青岛市　266590
    3.辽宁工程技术大学 矿业学院，辽宁　阜新市　123000

通讯作者:

杜兆文（1991—），男，山东临沂人，博士，主要从事充填开采、采煤方法研究。E-mail:
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