Article(id=1321893278908272869, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, 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=1751558400000, receivedDateStr=2025-07-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093517281, onlineDateStr=2026-10-27, pubDate=1781712000000, pubDateStr=2026-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093517281, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093517281, creator=13701087609, updateTime=1793093517281, updator=13701087609, issue=Issue{id=1321893272490987695, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='6', pageStart='1', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='1781712000000', pubDateStr='2026-06-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093515751, creator='13701087609', updateTime=1793094575775, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897718625194101, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897718625194102, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=32, endPage=41, ext={EN=ArticleExt(id=1321893279092822246, articleId=1321893278908272869, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Optimization of Stope Structure Parameters Based on DBO-MLP-SVM Algorithm Combined with Numerical Simulation, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problems that the traditional engineering analogy method is easily affected by subjective evaluation, and that the simple numerical simulation method involves complex modeling and heavy workload in the optimization of stope structure parameters, an artificial intelligence stope parameter optimization method based on DBO-MLP-SVM is proposed. The Dung Beetle Optimizer (DBO) was used to optimize the performance of Multilayer Perceptron (MLP) and the Support Vector Machine (SVM). The two were fused to overcome the defect that MLP is easy to fall into a local optimum, and the relevant mine stope parameters and stope stability classification data were collected to train the proposed model. Taking a limestone mine in southwest China as an example, the stability classification under different pillar widths was obtained by substituting the model, and the stope displacement, pillar deformation and surface settlement were analyzed and verified by FLAC3D numerical simulation. The results show that the DBO-MLP-SVM model predicts that the stope is stable when the pillar width is greater than 8 m, and the optimal stope structure parameter is pillar width×spacing = 8 m×8 m, which is consistent with the numerical simulation results. Compared with the traditional engineering analogy method, the production capacity of the stope after intelligent collaborative optimization increased by 51.80%, and the loss rate and dilution rate changed little, which effectively balances production efficiency and safety. This method provides an efficient and accurate technical approach for the optimization of stope structure parameters, which can serve as a reference for the optimization of mine stope parameters.

, authors=Kun Li¹, Gang Wang², Li Zhao³, Dengfeng Su¹, Yingpeng Hu¹, Baohui Tan¹, Rui Wang¹, xinxin Hou¹, authorsList=Kun Li, Gang Wang, Li Zhao, Dengfeng Su, Yingpeng Hu, Baohui Tan, Rui Wang, xinxin Hou, authorCompany=null, correspAuthors=Li Zhao, 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=1321893279617110247, articleId=1321893278908272869, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于DBO-MLP-SVM算法联合数值模拟的采场结构参数优化研究, columnId=1226462294490657775, journalTitle=矿业研究与开发, columnName=采矿与矿山充填, runingTitle=null, highlight=null, articleAbstract=

针对采场结构参数优化中传统工程类比方法易受主观评价影响、单纯数值模拟方法建模复杂且工作量大等难题，提出基于DBO-MLP-SVM的人工智能采场参数优选方法。采用蜣螂算法（DBO）优化多层感知机（MLP）与支持向量机（SVM）的性能，将二者融合以克服MLP易陷入局部最优的缺陷，并收集相关矿山采场参数与采场稳定性分级数据对所提模型进行训练，以西南某石灰石矿山为例，代入模型获得不同矿柱宽度下的稳定性分级，并通过FLAC3D数值模拟对采场位移、矿柱变形及地表沉降进行分析验证。结果表明：DBO-MLP-SVM模型预测矿柱宽度大于8 m时采场稳定，最优采场结构参数为矿柱宽度×间距=8 m×8 m，数值模拟结果与其一致；与传统工程类比法相比，智能协同优化后采场生产能力增幅达51.80%，损失率、贫化率变化较小，有效兼顾了生产效能与安全保障。该方法为采场结构参数优化提供了高效、准确的技术途径，可为矿山采场参数优化提供借鉴。

, authors=李坤¹, 王刚², 赵黎³, 粟登峰¹, 胡颖鹏¹, 谭宝会¹, 王瑞¹, 侯鑫鑫¹, authorsList=李坤, 王刚, 赵黎, 粟登峰, 胡颖鹏, 谭宝会, 王瑞, 侯鑫鑫, authorCompany=null, correspAuthors=赵黎, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=wOp7iIVOXZ8TC+fHzN/9HQ==, magXml=NcBu0jOzPXkDTB+Q/siFKg==, pdfUrl=null, pdf=/t2UTeA+eelvBOJHQIz3Dg==, pdfFileSize=2674256, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=C25LmvXdsPAfCw66ds6rig==, mapNumber=null, fund=null)}, authors=[Author(id=1321893281684902130, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893278908272869, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=15264940610@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893281764593908, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893278908272869, authorId=1321893281684902130, language=EN, stringName=Kun Li, firstName=Kun, middleName=null, lastName=Li, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Environment and Resources, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893281848479989, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893278908272869, authorId=1321893281684902130, language=CN, stringName=李坤, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．西南科技大学 环境与资源学院，四川 绵阳市　621010, bio={"content":"

李坤（2000—），男，山东临沂人，硕士研究生，主要从事矿业工程相关研究工作。E-mail:15264940610@163.com

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李坤（2000—），男，山东临沂人，硕士研究生，主要从事矿业工程相关研究工作。E-mail:15264940610@163.com

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