Article(id=1322560028976992304, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, 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=1745856000000, receivedDateStr=2025-04-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252482884, onlineDateStr=2026-10-29, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252482884, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252482884, creator=13701087609, updateTime=1793252482884, updator=13701087609, issue=Issue{id=1322559951067792299, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='4', pageStart='1', pageEnd='294', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252464310, creator='13701087609', updateTime=1793252763065, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322561204200001631, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322561204200001632, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=170, endPage=177, ext={EN=ArticleExt(id=1322560029203484721, articleId=1322560028976992304, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Comparative Study on Strength Prediction Models of Small Sample Grouting Materials, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The strength prediction of grouting materials is of great significance to improve the reliability of material design for grouting. Aiming at the problems that the traditional empirical models are difficult to accurately capture the relationship between material composition and strength, and the insufficient generalization ability of models caused by small samples, a machine learning prediction framework combining physical mechanism and data enhancement was proposed. The material performance prediction results of four machine learning methods, Random Forest Regression (RFR), Artificial Neural Network (ANN), Support Vector Regression (SVR) and Linear Regression (LR), were compared and analyzed. The results show that the RFR model has the best predictive performance, and its determination coefficient (R²) is increased to 0.934, which is 21.8%, 10.4% and 1.6% higher than that of LR, SVR and ANN models, respectively. The mean square error MAE is reduced to 2.59, which is 14.5%, 13.4% and 9.1% lower than that of LR, SVR and ANN models, respectively. In the analysis of feature importance, the contribution of graphite ratio to compressive strength is 37.6%. The data enhancement technique based on physical constraints can significantly improve the robustness of small sample modeling. The MAE of RFR and ANN models increases by 39.8% and 18.3% respectively compared to those without data enhancement. The prediction error of the RFR model remains stable below 10% under various water-solid ratio conditions. The research results can provide reference for the proportion of grouting reinforcement materials in mines.

, authors=Donghao ZHAI¹, Xiangpeng WANG^{1, 2}, Leijun YAO³, Libo FAN³, Xu WU¹, Qiuping KONG⁴, Yanbao LU⁴, authorsList=Donghao ZHAI, Xiangpeng WANG, Leijun YAO, Libo FAN, Xu WU, Qiuping KONG, Yanbao LU, authorCompany=null, correspAuthors=Xiangpeng WANG, 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=1322560029459337266, articleId=1322560028976992304, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=小样本注浆材料强度预测模型对比研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

注浆材料强度预测对提升注浆材料设计可靠性具有重要意义。针对传统经验模型难以准确捕捉材料成分与强度之间的关系，以及小样本导致的模型泛化能力不足的问题，提出了一种融合物理机理与数据增强的机器学习预测框架，并对随机森林回归（Random Forest Regression, RFR）、人工神经网络（Artificial Neural Network, ANN）、支持向量机回归（Support Vector Regression, SVR）和线性回归（Linear Regression, LR）4种机器学习方法的材料性能预测结果进行对比分析。结果表明：RFR模型的预测性能最优，其决定系数R²提升至0.934，较LR、SVR、ANN模型分别提高了21.8%、10.4%、1.6%，均方误差MAE降至2.59，较LR、SVR、ANN模型分别降低了14.5%、13.4%、9.1%；特征重要性分析中，石墨比例对抗压强度贡献度达37.6%；物理约束的数据增强技术可显著提升小样本建模的鲁棒性，RFR和ANN模型的MAE较未纳入数据增强时分别提升39.8%和18.3%；RFR模型在各水固比条件下的预测误差均稳定低于10%。研究结果可为矿山注浆加固材料配比提供参考。

, authors=翟东豪¹, 王向鹏^{1, 2}, 姚磊钧³, 樊立博³, 吴旭¹, 孔秋平⁴, 卢焱保⁴, authorsList=翟东豪, 王向鹏, 姚磊钧, 樊立博, 吴旭, 孔秋平, 卢焱保, authorCompany=null, correspAuthors=王向鹏, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=zffOJVisBg4xZzCNAaptsw==, magXml=IY2jueOAd80xuNkOGmgobw==, pdfUrl=null, pdf=XkQVvJ75At0k/Hndtu8uWA==, pdfFileSize=1763039, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=5UOrhJ01H6xeBRXYtEZwWQ==, mapNumber=null, fund=null)}, authors=[Author(id=1322560031422271552, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322560028976992304, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1102428556@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322560031493574722, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322560028976992304, authorId=1322560031422271552, language=EN, stringName=Donghao ZHAI, firstName=Donghao, middleName=null, lastName=ZHAI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.College of Computer and Network Security, Chengdu University of Technology, Chengdu, Sichuan 610059, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322560031590043715, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322560028976992304, authorId=1322560031422271552, language=CN, stringName=翟东豪, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．成都理工大学 计算机与网络安全学院，四川 成都　610059, bio={"content":"

翟东豪（2002—），男，河南洛阳人，硕士研究生，主要研究方向为人工智能与地学大数据。E-mail:1102428556@qq.com

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翟东豪（2002—），男，河南洛阳人，硕士研究生，主要研究方向为人工智能与地学大数据。E-mail:1102428556@qq.com

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