Article(id=1321893383275135269, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, 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=1752076800000, receivedDateStr=2025-07-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093542164, onlineDateStr=2026-10-27, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093542164, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093542164, creator=13701087609, updateTime=1793093542164, updator=13701087609, issue=Issue{id=1321893331622286028, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='7', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093529849, creator='13701087609', updateTime=1793094587578, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897768357048353, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897768357048354, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=108, endPage=115, ext={EN=ArticleExt(id=1321893383426130214, articleId=1321893383275135269, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Prediction of Open-Pit Slope Stability Based on the WOA-CNN-LSTM Model, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

With the expansion of open-pit mining scale, slope stability problems have become increasingly prominent. Great significance is attached to realizing safe and efficient open-pit mining by accurately predicting slope stability and taking preventive measures in advance. A whale optimization algorithm-convolutional neural network-long short-term memory (WOA-CNN-LSTM) prediction model for slope stability was constructed based on factor analysis. Six factors, including cohesion, internal friction angle, slope angle, slope height, pore pressure ratio and unit weight, were selected as the discrimination indicators of the model. Factor analysis was adopted to eliminate the correlation among the six indicators, reduce data dimensions and extract common factors. The extracted common factors were taken as the inputs of the WOA-CNN-LSTM model, and the model was applied to slope engineering cases to verify its reliability. The research results show that the CNN-LSTM network can be optimized by iteratively updating and searching for optimal network parameters using the WOA algorithm. The problems of the CNN-LSTM model, including falling into local optimal solutions and thegradient descent affectingthe prediction accuracy of slope stability, can be effectively overcome. The prediction accuracy and generalization ability of the model are thus improved, and a new idea for slope stability prediction is provided.

, authors=Faping Zheng1, Zonghong Zhou2, Huiwen Mu2, Jian Liu2, authorsList=Faping Zheng, Zonghong Zhou, Huiwen Mu, Jian Liu, authorCompany=null, correspAuthors=Zonghong Zhou, 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=1321893385384870183, articleId=1321893383275135269, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于WOA-CNN-LSTM模型的露天矿边坡稳定性预测, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

随着露天矿开采规模扩张,边坡稳定性问题日益突出。精准预测边坡稳定性并提前防范,对实现露天矿安全高效开采具有重大意义。基于因子分析构建了鲸鱼优化算法-卷积神经网络-长短期记忆网络(WOA-CNN-LSTM)边坡稳定性预测模型。选取黏聚力、内摩擦角、边坡角、边坡高度、孔隙压力比和容重6个因素作为模型的判别指标,利用因子分析法消除6个指标间的相关性,降低数据维数,提取公因子。将提取的公因子作为WOA-CNN-LSTM模型的输入,并将该模型应用于边坡工程实例中,验证模型的可靠性。结果表明,利用WOA算法迭代更新寻找最优网络参数原理,对CNN-LSTM网络进行优化,可有效克服CNN-LSTM模型易陷入局部最优解、梯度下降影响边坡稳定性预测精度的问题,提高了模型的预测精度和泛化能力,同时为边坡稳定性预测提供了一种新思路。

, authors=郑发萍1, 周宗红2, 慕慧文2, 刘剑2, authorsList=郑发萍, 周宗红, 慕慧文, 刘剑, authorCompany=null, correspAuthors=周宗红, authorNote=

郑发萍(2000—),女,四川会理人,硕士研究生,研究方向为矿山安全。E-mail:

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周宗红(1967—),男,安徽宿州人,教授,博士生导师,主要从事采矿与岩石力学教学与研究。E-mail:
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郑发萍(2000—),女,四川会理人,硕士研究生,研究方向为矿山安全。E-mail:

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基于WOA-CNN-LSTM模型的露天矿边坡稳定性预测
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郑发萍 1 , 周宗红 2 , 慕慧文 2 , 刘剑 2
矿业研究与开发 | 矿山安全与环保 2026,46(7): 108-115
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矿业研究与开发 |矿山安全与环保 2026 , 46 (7) : 108 -115
基于WOA-CNN-LSTM模型的露天矿边坡稳定性预测
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郑发萍1 , 周宗红2 , 慕慧文2, 刘剑2
作者信息
  • 1.昆明理工大学 公共安全与应急管理学院,云南 昆明 654093
  • 2.昆明理工大学 国土资源工程学院,云南 昆明 654093
通讯作者:
周宗红(1967—),男,安徽宿州人,教授,博士生导师,主要从事采矿与岩石力学教学与研究。E-mail:
作者简介:

郑发萍(2000—),女,四川会理人,硕士研究生,研究方向为矿山安全。E-mail:

Prediction of Open-Pit Slope Stability Based on the WOA-CNN-LSTM Model
Faping Zheng1 , Zonghong Zhou2 , Huiwen Mu2, Jian Liu2
Affiliations
  • 1.Faculty of Public Safety and Emergency Management, Kunming University of Science and Technology, Kunming, Yunnan 654093, China
  • 2.Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, Yunnan 654093, China
出版时间: 2026-07-25
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随着露天矿开采规模扩张,边坡稳定性问题日益突出。精准预测边坡稳定性并提前防范,对实现露天矿安全高效开采具有重大意义。基于因子分析构建了鲸鱼优化算法-卷积神经网络-长短期记忆网络(WOA-CNN-LSTM)边坡稳定性预测模型。选取黏聚力、内摩擦角、边坡角、边坡高度、孔隙压力比和容重6个因素作为模型的判别指标,利用因子分析法消除6个指标间的相关性,降低数据维数,提取公因子。将提取的公因子作为WOA-CNN-LSTM模型的输入,并将该模型应用于边坡工程实例中,验证模型的可靠性。结果表明,利用WOA算法迭代更新寻找最优网络参数原理,对CNN-LSTM网络进行优化,可有效克服CNN-LSTM模型易陷入局部最优解、梯度下降影响边坡稳定性预测精度的问题,提高了模型的预测精度和泛化能力,同时为边坡稳定性预测提供了一种新思路。

露天矿边坡稳定性  /  因子分析  /  WOA-CNN-LSTM模型  /  预测精度  /  矿山安全

With the expansion of open-pit mining scale, slope stability problems have become increasingly prominent. Great significance is attached to realizing safe and efficient open-pit mining by accurately predicting slope stability and taking preventive measures in advance. A whale optimization algorithm-convolutional neural network-long short-term memory (WOA-CNN-LSTM) prediction model for slope stability was constructed based on factor analysis. Six factors, including cohesion, internal friction angle, slope angle, slope height, pore pressure ratio and unit weight, were selected as the discrimination indicators of the model. Factor analysis was adopted to eliminate the correlation among the six indicators, reduce data dimensions and extract common factors. The extracted common factors were taken as the inputs of the WOA-CNN-LSTM model, and the model was applied to slope engineering cases to verify its reliability. The research results show that the CNN-LSTM network can be optimized by iteratively updating and searching for optimal network parameters using the WOA algorithm. The problems of the CNN-LSTM model, including falling into local optimal solutions and thegradient descent affectingthe prediction accuracy of slope stability, can be effectively overcome. The prediction accuracy and generalization ability of the model are thus improved, and a new idea for slope stability prediction is provided.

Open-pit slope stability  /  Factor analysis  /  WOA-CNN-LSTM model  /  Prediction accuracy  /  Mine safety
郑发萍, 周宗红, 慕慧文, 刘剑. 基于WOA-CNN-LSTM模型的露天矿边坡稳定性预测. 矿业研究与开发, 2026 , 46 (7) : 108 -115 .
Faping Zheng, Zonghong Zhou, Huiwen Mu, Jian Liu. Prediction of Open-Pit Slope Stability Based on the WOA-CNN-LSTM Model[J]. Mining Research and Development, 2026 , 46 (7) : 108 -115 .
  • 国家自然科学基金项目(52264019)
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  • 接收时间:2025-07-10
  • 首发时间:2026-10-27
  • 出版时间:2026-07-25
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  • 收稿日期:2025-07-10
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国家自然科学基金项目(52264019)
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    1.昆明理工大学 公共安全与应急管理学院,云南 昆明 654093
    2.昆明理工大学 国土资源工程学院,云南 昆明 654093

通讯作者:

周宗红(1967—),男,安徽宿州人,教授,博士生导师,主要从事采矿与岩石力学教学与研究。E-mail:
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2种不同金属材料的力学参数
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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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