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In a gold mine, the uniaxial compressive strength of ultra-fine unclassified tailings backfill cannot satisfy the requirements of stope filling. To improve the backfill strength, steel fibers, polypropylene fibers and steel-polypropylene hybrid fibers were adopted as reinforcement materials for backfill, and the effects of fiber type, fiber dosage and curing age on the backfill strength were systematically analyzed. Based on the test data, a prediction model for backfill strength integrating Gaussian Process Regression (GPR) and Shapley Additive Explanations (SHAP) was proposed. The research results show that both steel fibers and polypropylene fibers can effectively enhance the backfill strength. Compared with the fiber-free blank control group, all fiber systems achieve the optimal strength enhancement at a fiber dosage of 0.3%. A typical nonlinear relationship of "promotion at low dosage and inhibition at high dosage" exists between fiber dosage and backfill strength. In addition, the established strength prediction model exhibits excellent prediction performance, and the average relative error of strength prediction is controlled within 10%. The SHAP analysis can accurately quantify the influence degree of fiber type, fiber dosage and curing age to backfill strength. The research results provide a quantitative basis for the mix proportion design of fiber-modified backfill and help promote the intelligent development of mine backfilling engineering.

, authors=Faping Zheng¹, Zonghong Zhou², Huiwen Mu², Jian Liu², Tingkai Hou², authorsList=Faping Zheng, Zonghong Zhou, Huiwen Mu, Jian Liu, Tingkai Hou, 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=1321895085436949276, articleId=1321895084837163802, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=某金矿超细全尾砂纤维充填体强度特征及预测研究, columnId=1226462294490657775, journalTitle=矿业研究与开发, columnName=采矿与矿山充填, runingTitle=null, highlight=null, articleAbstract=

某金矿存在超细全尾砂充填体单轴抗压强度无法满足采场充填要求的问题。为提高充填体强度，以钢纤维、聚丙烯纤维、钢纤维与聚丙烯混合纤维作为充填体强度增强材料进行试验，并系统分析充填体纤维种类和掺量及养护龄期对充填体强度的影响。基于试验数据，提出一种融合了高斯过程回归（GPR）与沙普利可加性解释法（SHAP）的充填体强度预测模型。研究结果表明：钢纤维和聚丙烯纤维均能有效增强充填体强度，相较无纤维空白对照组，纤维掺量为0.3%时，各纤维体系均表现出最佳的强度提升效果，且纤维掺量与强度之间呈现典型的“低掺促进、高掺抑制”非线性关系。此外，所建立的强度预测模型展现出优异的预测性能，强度预测平均误差控制在10%以内；SHAP分析能够精准量化纤维类型、掺量和养护龄期对充填体强度的影响程度大小。研究成果为纤维改性充填体的配比设计提供了量化依据，有利于推动矿山充填工程的智能化发展。

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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和岩石力学。E-mail:zfp121800@163.com

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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和岩石力学。E-mail:zfp121800@163.com

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