Article(id=1322559939428602270, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559901990240737, 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=1743350400000, receivedDateStr=2025-03-31, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252461534, onlineDateStr=2026-10-29, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252461534, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252461534, creator=13701087609, updateTime=1793252461534, updator=13701087609, issue=Issue{id=1322559901990240737, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='3', pageStart='1', pageEnd='330', issueExtLink='null', onlineDate='null', pubDate='1774368000000', pubDateStr='2026-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252452609, creator='13701087609', updateTime=1793252731500, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322561071810991099, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559901990240737, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322561071810991100, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559901990240737, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=133, endPage=141, ext={EN=ArticleExt(id=1322559939613151647, articleId=1322559939428602270, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Strength Damage Prediction Model of Freeze-Thaw Sandstone Based on Machine Learning, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problems of significant strength damage of sandstone in cold regions under freeze-thaw action and the complex research process of traditional test methods, a machine learning-based prediction model for the strength damage of freeze-thawed sandstone was proposed. By means of literature research, 117 sets of experimental data on strength damage of sandstone after freeze-thaw cycels were selected as the data set. The longitudinal wave velocity, dry density, saturated density, number of cycles and freeze-thaw period were selected as the input characteristics of the model, and the strength loss rate was used as the output characteristics. Six machine learning models such as decision tree, random forest, support vector machine, XGBoost, extreme random tree and CatBoost were used to predict the strength damage of sandstone after freeze-thaw cycles, and R², MAE, MSE and RMSE were used to evaluate the performance of the model. The results show that the CatBoost model has the best performance in all evaluation indicators. Its R² on the test set exceeds 0.93, and the MAE value, MSE value and RMSE value are 0.028, 0.002 and 0.039, respectively, showing strong generalization ability and good prediction effect. Further, the Bayesian optimization algorithm was used to optimize the hyperparameters of the CatBoost model, and the number of iterations, the maximum depth of the tree and L2 regularization were determined as the key parameters affecting the prediction performance of the model. Eigenvalue weight analysis shows that the order of importance of each variable to the model contribution is : number of cycles>freeze-thaw period>dry density>saturated density>longitudinal wave velocity. The research results can provide effective prediction tools and method references for rock mechanics engineering practice in cold regions.

, authors=Kaihui ZHANG^{1, 2}, Yihai ZHANG^{1, 2}, Xutong JIANG^{1, 2}, authorsList=Kaihui ZHANG, Yihai ZHANG, Xutong JIANG, authorCompany=null, correspAuthors=Yihai ZHANG, 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=1322559941467034016, articleId=1322559939428602270, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于机器学习的冻融砂岩强度损伤预测模型研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

针对寒区砂岩在冻融作用下强度损伤显著且传统试验方法研究过程复杂的问题，提出了一种基于机器学习的冻融砂岩强度损伤预测模型。通过文献调研收集了117组冻融循环后砂岩强度损伤的试验数据作为数据集，选取纵波波速、干密度、饱和密度、循环次数、冻融周期作为模型的输入特征，强度损失率作为输出特征。采用决策树、随机森林、支持向量机、XGBoost、极端随机树、CatBoost等6种机器学习模型对冻融循环后砂岩强度损伤进行预测，并使用R²、MAE、MSE、RMSE指标评估模型性能。结果表明，CatBoost模型的各项评估指标均表现最优，其在测试集上的R²超过0.93，MAE值、MSE值、RMSE值分别为0.028、0.002、0.039，展现出较强的泛化能力和良好的预测效果。进一步通过贝叶斯优化算法对CatBoost模型进行超参数调优，确定迭代次数、树最大深度和L2正则化为影响模型预测性能的关键参数。特征值权重分析显示，各变量对模型贡献的重要性排序为：循环次数>冻融周期>干密度>饱和密度>纵波波速。研究成果可为寒区岩石力学工程实践提供有效的预测工具与方法参考。

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张凯惠（1997—），男，甘肃天水人，硕士，助理工程师，主要从事岩石力学与边坡稳定性研究工作。E-mail: calhounzhang_97@163.com

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张凯惠（1997—），男，甘肃天水人，硕士，助理工程师，主要从事岩石力学与边坡稳定性研究工作。E-mail: calhounzhang_97@163.com

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