Article(id=1311351226109886857, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2026.20251491, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756137600000, receivedDateStr=2025-08-26, revisedDate=1758384000000, revisedDateStr=2025-09-21, acceptedDate=1758729600000, acceptedDateStr=2025-09-25, onlineDate=1790580096032, onlineDateStr=2026-09-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580096032, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580096032, creator=13701087609, updateTime=1790580096032, updator=13701087609, issue=Issue{id=1311351152067830064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2026', volume='44', issue='1', pageStart='1', pageEnd='246', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790580078378, creator='13701087609', updateTime=1790588459421, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386304772927717, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386304772927718, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=138, endPage=142, ext={EN=ArticleExt(id=1311351226269270411, articleId=1311351226109886857, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=An OLHS-IAOO-KELM-Based Inversion Model for Permeability Coefficient of Tailings Dams and Its Application, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Tailings dams are formed through long-term accumulation of tailings, featuring complex stratification and non-uniform permeability coefficients. To obtain representative permeability coefficients that reflect their overall permeability characteristics, a novel inversion method was proposed. Optimal Latin Hypercube Sampling (OLHS) was used to obtain uniformly distributed samples of permeability coefficient combinations for tailings dams. These samples were then input into a finite element model for forward analysis to obtain corresponding samples of water heads at monitoring points. The two sets of samples were combined to form a dataset. A nonlinear mapping relationship from the permeability coefficients to the water heads at monitoring points was established using the Kernel Extreme Learning Machine (KELM). The hyperparameters of the KELM were optimized using an Improved Animated Oat Optimization (IAOO) algorithm, which integrated Latin Hypercube Sampling for population initialization, centroid opposition-based learning, and an adaptive optimal-seeking boundary mechanism. An inversion model for the permeability coefficient of tailings dams based on OLHS-IAOO-KELM was thus established and subsequently applied to a practical engineering case. The permeability coefficient values obtained through the inversion model were reasonable. The relative errors between the calculated water heads from forward seepage analysis and the measured water heads at the seven monitoring points did not exceed 2.08%, meeting the engineering accuracy requirements. Furthermore, the potential distribution of the seepage field in a typical cross-section of the tailings dam conformed to general patterns. Compared with other models, the inversion result of the proposed model exhibited the smallest errors. The proposed model demonstrates high accuracy and robustness, and possesses practical value for the inversion of permeability coefficients in tailings dams.

, authors=Zi-yi GUAN^a, Zhen-zhong SHEN^{a, b}, authorsList=Zi-yi GUAN, Zhen-zhong SHEN, authorCompany=null, correspAuthors=Zhen-zhong SHEN, 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=1311351226722255244, articleId=1311351226109886857, tenantId=1146029695717560320, journalId=1205116964453384197, language=CN, title=基于OLHS-IAOO-KELM的尾矿坝渗透系数反演模型及应用, columnId=1207271181661589640, journalTitle=水电能源科学, columnName=水利枢纽、水利建筑物, runingTitle=null, highlight=null, articleAbstract=

尾矿坝是由尾砂长期堆积而成的，分层复杂、渗透系数不均一，为获取能反映其整体渗透特性的代表性渗透系数，提出一种新的反演方法。采用最优拉丁超立方抽样（OLHS）获取均布的尾矿坝渗透系数组合样本，将其代入有限元模型进行正分析得到测点水头值样本，两者结合构成数据集，通过核极限学习机（KELM）建立从渗透系数到测点水头的非线性映射关系，利用融合拉丁超立方抽样初始化种群、重心反向学习和自适应趋优边界改进的不实野燕麦优化（IAOO）算法对KELM的超参数进行优化，建立了基于OLHS-IAOO-KELM的尾矿坝渗透系数反演模型，并将其应用于工程实例中。通过该模型反演得到的尾矿坝渗透系数值合理，7个测点经渗流正分析得到的计算水头和实测水头的相对误差不超过2.08%，满足工程精度要求，且尾矿坝典型断面的渗流场位势分布符合一般规律。与其他模型相比较，该模型的反演结果误差最小。该模型的准确性和鲁棒性高，在尾矿坝渗透系数反演中具有实用价值。

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管子懿（2000-），男，硕士研究生，研究方向为水利工程渗流特性分析控制，E-mail: guanzy_hhu@163.com

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管子懿（2000-），男，硕士研究生，研究方向为水利工程渗流特性分析控制，E-mail: guanzy_hhu@163.com

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