Article(id=1321893404439597135, 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=1751990400000, receivedDateStr=2025-07-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093547210, onlineDateStr=2026-10-27, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093547210, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093547210, creator=13701087609, updateTime=1793093547210, 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=265, endPage=275, ext={EN=ArticleExt(id=1321893404624146512, articleId=1321893404439597135, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=FRCTB Compressive Strength Prediction Based on HOA-R-Stacking Algorithm, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the insufficient prediction accuracy of the compressive strength of fiber reinforced cemented tailings backfill (FRCTB), an improved Stacking ensemble learning model based on a weighted residual mechanism was proposed, utilizing 364 sets of experimental data collected from the literature. The model constructed an ensemble framework with six heterogeneous base learners and four meta-learners, and employed the Hippopotamus Optimization Algorithm (HOA) for global optimization of the base learner parameters. The prediction results of the base learners were obtained through 5-fold cross-validation, and adaptive weights were assigned to each base learner according to their prediction errors. Residual features were extracted as inputs for the meta-learner. Six single models were set as benchmarks for horizontal prediction accuracy comparison. Finally, interpretability analysis was introduced to explore the key influencing factors. The results show that among the single models, the light gradient boosting machine achieves the best comprehensive performance, with a coefficient of determination of 0.887 0. The HOA-R-Stacking model with Support Vector Regression (SVR) as the meta-learner performs the best, achieving a coefficient of determination of 0.947 0, and its mean absolute error and root mean square error are reduced by 34.53% and 33.94%, respectively, compared with the best single model. The interpretability analysis indicates that fiber content is one of the key factors affecting the compressive strength of FRCTB. The research results provide an effective technical means for predicting the compressive strength of FRCTB, which helps to improve backfill stability and reduce the cost of mine filling.

, authors=Liu He¹, Haiping Yuan¹, Yixian Wang¹, Chuanqi Zhu², Hengzhe Li¹, authorsList=Liu He, Haiping Yuan, Yixian Wang, Chuanqi Zhu, Hengzhe Li, authorCompany=null, correspAuthors=Haiping Yuan, 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=1321893405110685777, articleId=1321893404439597135, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于HOA-R-Stacking算法的FRCTB抗压强度预测, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

为解决纤维增强尾砂胶结充填体（FRCTB）抗压强度预测精度不足的问题，基于文献收集的364组试验数据，提出一种基于加权残差机制的改进Stacking集成学习模型。模型采用6种异质基学习器与4种元学习器构建集成框架，使用河马优化算法（HOA）对基学习器参数进行全局寻优；通过5折交叉验证获取基学习器预测结果，并依据预测误差为各基学习器分配自适应权重；提取残差特征作为元学习器输入，设置6种单一模型作为对照开展横向预测精度对比；最后引入可解释性分析探究关键影响因素。结果表明：单一模型中，轻量梯度提升机综合性能最优（决定系数为0.887 0）；以支持向量回归（SVR）为元学习器的HOA-R-Stacking模型的性能最佳（决定系数为0.947 0），其平均绝对误差和均方根误差较最优单一模型分别降低34.53%和33.94%。可解释性分析表明：纤维含量是影响FRCTB抗压强度的关键因素之一。研究成果为FRCTB抗压强度预测提供了有效技术手段，有助于提升充填体稳定性、降低矿山充填成本。

, authors=何流¹, 袁海平¹, 汪亦显¹, 朱传奇², 李恒喆¹, authorsList=何流, 袁海平, 汪亦显, 朱传奇, 李恒喆, authorCompany=null, correspAuthors=袁海平, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=qYF2WuW+BtUUtxye7xQm2g==, magXml=RRxn/bKKQ682VWsAaV9F0Q==, pdfUrl=null, pdf=xajGbDyc4PvE9QIKFoDG/g==, pdfFileSize=2917598, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Dfpogo0L/iZ6pJLVjUBfbg==, mapNumber=null, fund=null)}, authors=[Author(id=1321893409229492313, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893404439597135, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=2023171912@mail.hfut.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893409304989787, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893404439597135, authorId=1321893409229492313, language=EN, stringName=Liu He, firstName=Liu, middleName=null, lastName=He, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.College of Civil Engineering, Hefei University of Technology, Hefei, Anhui 230009, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893409372098652, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893404439597135, authorId=1321893409229492313, language=CN, stringName=何流, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．合肥工业大学 土木与水利工程学院，安徽 合肥　230009, bio={"content":"

何流（1999—），男，贵州毕节人，硕士研究生，主要从事非煤矿山安全开采的研究。E-mail:2023171912@mail.hfut.edu.cn

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何流（1999—），男，贵州毕节人，硕士研究生，主要从事非煤矿山安全开采的研究。E-mail:2023171912@mail.hfut.edu.cn

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