Article(id=1322559992780140888, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, 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=1744128000000, receivedDateStr=2025-04-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252474255, onlineDateStr=2026-10-29, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252474255, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252474255, creator=13701087609, updateTime=1793252474255, updator=13701087609, issue=Issue{id=1322559951067792299, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='4', pageStart='1', pageEnd='294', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252464310, creator='13701087609', updateTime=1793252763065, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322561204200001631, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322561204200001632, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=60, endPage=68, ext={EN=ArticleExt(id=1322559992968884569, articleId=1322559992780140888, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Prediction of Compressive Strength of Unclassified Tailings Backfill Based on Shrinkage Characteristics, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To mitigate the impact of variations in cementitious material dosage and slurry mass concentration in backfill batching system on the strength prediction of backfill in the stope, the shrinkage characteristics of unclassified-tailings backfill slurry, which were strongly correlated with two factors, were selected as the research subject. Static shrinkage tests and uniaxial compression tests were conducted with different cement-tailing ratios (1∶4, 1∶8, 1∶20) and mass concentrations (70%, 72%, 74%, 76%). The random forest algorithm was selected to calculate the feature importance scores of different factors, and a compressive strength prediction model was constructed based on the shrinkage ratio. The model was validated by combining in situ measured data from an upward horizontal slice mining stope in a lead-zinc mine. The results are concluded as follows. Firstly, when the cement-tailing ratio is constant, the shrinkage ratio of the backfill slurry decreases gradually with the increase of mass concentration. When the mass concentration is constant, the slurry shrinkage ratio also exhibits a decreasing trend with the increase of cement-tailing ratio. Backfill slurry with low mass concentration and a low cement-tailing ratio is more prone to bleeding and segregation, which leads to the deterioration of backfill quality. Secondly, by using a random forest model to calculate the feature importance scores of each factor, it is identified that the cement-tailing ratio has the greatest impact on the compressive strength of the backfill material (93%), and the shrinkage ratio (5.6%) has a greater impact on the compressive strength than the mass concentration (1.4%). Thirdly, the engineering application results show that compared with the prediction method based on mass concentration, the prediction results of the uniaxial compressive strength of the backfill based on shrinkage ratio have smaller errors in most cases and have a better engineering applicability.

, authors=Xinyu YUAN1, Ruixiang ZHAO1, Wangping DI1, Hu DONG1, Yiming WANG2, Yiren WANG2, Anju YU2, authorsList=Xinyu YUAN, Ruixiang ZHAO, Wangping DI, Hu DONG, Yiming WANG, Yiren WANG, Anju YU, authorCompany=null, correspAuthors=Yiren WANG, 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=1322559993228931418, articleId=1322559992780140888, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于沉缩特性的全尾砂充填体抗压强度预测, columnId=1226462294490657775, journalTitle=矿业研究与开发, columnName=采矿与矿山充填, runingTitle=null, highlight=null, articleAbstract=

为降低充填配料系统中胶凝材料添加量和质量浓度的波动对采场充填体强度预判造成的影响,选择与两因素具有强关联的全尾砂充填料浆沉缩特性作为研究对象。开展不同灰砂比(1∶4、1∶8、1∶20)和质量浓度(70%、72%、74%、76%)的胶结充填料浆静态沉缩试验与固结充填单轴压缩试验,采用随机森林算法计算不同因素的特征重要性得分,基于沉缩率构建抗压强度预测模型,并结合某铅锌矿上向水平分层采场的原位实测数据进行验证。结果显示:当灰砂比相同时,随着质量浓度的增大,充填料浆沉缩率逐渐减小;当质量浓度相同时,随着灰砂比的增大,料浆沉缩率同样呈下降的趋势;低质量浓度、低灰砂比的充填料浆更容易产生泌水离析现象,造成充填体质量劣化。通过随机森林模型计算各因素的特征重要性得分,识别出灰砂比对充填体抗压强度的影响程度最大(93%),沉缩率(5.6%)对抗压强度的影响程度大于质量浓度(1.4%)。工程应用结果表明,相较于基于质量浓度的预测方法,基于沉缩率的充填体单轴抗压强度预测的结果在多数工况下误差更小,具有较好的工程适用性。

, authors=原新宇1, 赵瑞祥1, 第旺平1, 董浒1, 王贻明2, 王奕仁2, 俞安举2, authorsList=原新宇, 赵瑞祥, 第旺平, 董浒, 王贻明, 王奕仁, 俞安举, authorCompany=null, correspAuthors=王奕仁, authorNote=

原新宇(1993—),男,内蒙古赤峰人,工程师,主要从事金属矿地下开采方面的研究工作。E-mail:

, correspAuthorsNote=
王奕仁(1996—),男,河南焦作人,博士研究生,主要从事金属矿高效开采与膏体充填方面的研究工作。E-mail:
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原新宇(1993—),男,内蒙古赤峰人,工程师,主要从事金属矿地下开采方面的研究工作。E-mail:

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原新宇(1993—),男,内蒙古赤峰人,工程师,主要从事金属矿地下开采方面的研究工作。E-mail:

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基于沉缩特性的全尾砂充填体抗压强度预测
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原新宇 1 , 赵瑞祥 1 , 第旺平 1 , 董浒 1 , 王贻明 2 , 王奕仁 2 , 俞安举 2
矿业研究与开发 | 采矿与矿山充填 2026,46(4): 60-68
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矿业研究与开发 |采矿与矿山充填 2026 , 46 (4) : 60 -68
基于沉缩特性的全尾砂充填体抗压强度预测
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原新宇(1993—),男,内蒙古赤峰人,工程师,主要从事金属矿地下开采方面的研究工作。E-mail:

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原新宇1 , 赵瑞祥1, 第旺平1, 董浒1, 王贻明2, 王奕仁2 , 俞安举2
作者信息
  • 1.赤峰中色白音诺尔矿业有限公司,内蒙古 赤峰市 025473
  • 2.北京科技大学 资源与安全工程学院,北京市 100083
通讯作者:
王奕仁(1996—),男,河南焦作人,博士研究生,主要从事金属矿高效开采与膏体充填方面的研究工作。E-mail:
作者简介:

原新宇(1993—),男,内蒙古赤峰人,工程师,主要从事金属矿地下开采方面的研究工作。E-mail:

Prediction of Compressive Strength of Unclassified Tailings Backfill Based on Shrinkage Characteristics
Xinyu YUAN1 , Ruixiang ZHAO1, Wangping DI1, Hu DONG1, Yiming WANG2, Yiren WANG2 , Anju YU2
Affiliations
  • 1.Chifeng NFC Baiyinnuoer Mining Co., Ltd., Chifeng, Inner Mongolia 025473, China
  • 2.School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China
出版时间: 2026-04-25
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为降低充填配料系统中胶凝材料添加量和质量浓度的波动对采场充填体强度预判造成的影响,选择与两因素具有强关联的全尾砂充填料浆沉缩特性作为研究对象。开展不同灰砂比(1∶4、1∶8、1∶20)和质量浓度(70%、72%、74%、76%)的胶结充填料浆静态沉缩试验与固结充填单轴压缩试验,采用随机森林算法计算不同因素的特征重要性得分,基于沉缩率构建抗压强度预测模型,并结合某铅锌矿上向水平分层采场的原位实测数据进行验证。结果显示:当灰砂比相同时,随着质量浓度的增大,充填料浆沉缩率逐渐减小;当质量浓度相同时,随着灰砂比的增大,料浆沉缩率同样呈下降的趋势;低质量浓度、低灰砂比的充填料浆更容易产生泌水离析现象,造成充填体质量劣化。通过随机森林模型计算各因素的特征重要性得分,识别出灰砂比对充填体抗压强度的影响程度最大(93%),沉缩率(5.6%)对抗压强度的影响程度大于质量浓度(1.4%)。工程应用结果表明,相较于基于质量浓度的预测方法,基于沉缩率的充填体单轴抗压强度预测的结果在多数工况下误差更小,具有较好的工程适用性。

充填料浆  /  沉缩率  /  灰砂比  /  质量浓度  /  随机森林  /  抗压强度预测

To mitigate the impact of variations in cementitious material dosage and slurry mass concentration in backfill batching system on the strength prediction of backfill in the stope, the shrinkage characteristics of unclassified-tailings backfill slurry, which were strongly correlated with two factors, were selected as the research subject. Static shrinkage tests and uniaxial compression tests were conducted with different cement-tailing ratios (1∶4, 1∶8, 1∶20) and mass concentrations (70%, 72%, 74%, 76%). The random forest algorithm was selected to calculate the feature importance scores of different factors, and a compressive strength prediction model was constructed based on the shrinkage ratio. The model was validated by combining in situ measured data from an upward horizontal slice mining stope in a lead-zinc mine. The results are concluded as follows. Firstly, when the cement-tailing ratio is constant, the shrinkage ratio of the backfill slurry decreases gradually with the increase of mass concentration. When the mass concentration is constant, the slurry shrinkage ratio also exhibits a decreasing trend with the increase of cement-tailing ratio. Backfill slurry with low mass concentration and a low cement-tailing ratio is more prone to bleeding and segregation, which leads to the deterioration of backfill quality. Secondly, by using a random forest model to calculate the feature importance scores of each factor, it is identified that the cement-tailing ratio has the greatest impact on the compressive strength of the backfill material (93%), and the shrinkage ratio (5.6%) has a greater impact on the compressive strength than the mass concentration (1.4%). Thirdly, the engineering application results show that compared with the prediction method based on mass concentration, the prediction results of the uniaxial compressive strength of the backfill based on shrinkage ratio have smaller errors in most cases and have a better engineering applicability.

Backfill slurry  /  Shrinkage ratio  /  Cement-tailing ratio  /  Mass concentration  /  Random forest  /  Prediction of compressive strength
原新宇, 赵瑞祥, 第旺平, 董浒, 王贻明, 王奕仁, 俞安举. 基于沉缩特性的全尾砂充填体抗压强度预测. 矿业研究与开发, 2026 , 46 (4) : 60 -68 .
Xinyu YUAN, Ruixiang ZHAO, Wangping DI, Hu DONG, Yiming WANG, Yiren WANG, Anju YU. Prediction of Compressive Strength of Unclassified Tailings Backfill Based on Shrinkage Characteristics[J]. Mining Research and Development, 2026 , 46 (4) : 60 -68 .
  • 国家自然科学基金项目(52374111)
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2026年第46卷第4期
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  • 接收时间:2025-04-09
  • 首发时间:2026-10-29
  • 出版时间:2026-04-25
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  • 收稿日期:2025-04-09
基金
国家自然科学基金项目(52374111)
作者信息
    1.赤峰中色白音诺尔矿业有限公司,内蒙古 赤峰市 025473
    2.北京科技大学 资源与安全工程学院,北京市 100083

通讯作者:

王奕仁(1996—),男,河南焦作人,博士研究生,主要从事金属矿高效开采与膏体充填方面的研究工作。E-mail:
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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