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Article(id=1322907565465674651, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322907425740825534, 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=1721923200000, receivedDateStr=2024-07-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793335342041, onlineDateStr=2026-10-30, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793335342041, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793335342041, creator=13701087609, updateTime=1793335342041, updator=13701087609, issue=Issue{id=1322907425740825534, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='6', pageStart='1', pageEnd='300', issueExtLink='null', onlineDate='null', pubDate='1750780800000', pubDateStr='2025-06-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793335308726, creator='13701087609', updateTime=1793335308726, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=138, endPage=145, ext={EN=ArticleExt(id=1322907565897687964, articleId=1322907565465674651, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Intelligent Evaluation and Optimization Design System of On-Demand Support of Stope Rock Roadways Based on XGBoost and Its Engineering Applications, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to avoid the phenomenon that the good rock is over-supported and the poor rock is under-supported, based on the XGBoost algorithm combined with the roadway safety factor, an intelligent evaluation and optimization design system of on-demand support in Longqiao Iron Mine was developed. 7 on-demand anchor mesh support indexes and 5 on-demand plain spray support indexes affecting the stability of roadway in Longqiao Iron Mine were used as the main input values. 60 sets of practical roadway data were selected as examples to carry out the on-demand optimization support design and effectiveness evaluation. The results show that at the level of −370.0 m, the surrounding rock grade is better, the safety factor is larger, and the support measures are not needed. At the level of −382.5 m and −395.0 m, the surrounding rock grade is slightly worse, the safety factor is slightly lower, and the plain spray support form is required. At the level of −407.5 m and −420.0 m, the surrounding rock grade is above the third grade, and the surrounding rock properties are poor, so it is necessary to adopt the form of anchor mesh support. The intelligent evaluation and optimization design system of on-demand support provides real-time rock mass quality evaluation and support scheme optimization for Longqiao Iron Mine, which has effectively improved the safety of roadway and significantly saved the support cost.

, authors=Jingshang LIN, authorsList=Jingshang LIN, authorCompany=null, correspAuthors=null, 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=1322907573103502277, articleId=1322907565465674651, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于XGBoost的采场岩巷按需支护智评优化设计系统及工程应用, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

为避免出现好岩过强支护而差岩欠强支护现象，基于XGBoost算法结合巷道安全系数，开发了龙桥铁矿按需支护智评优化设计系统。采用影响龙桥铁矿巷道稳定性的7项按需锚网支护指标和5项按需素喷支护指标作为主要输入值，以60个巷道实际数据为例，进行按需优化支护设计和效果评价。结果表明：在−370.0 m水平试算处围岩等级较好，安全系数较大，可不采取支护措施；在−382.5 m水平和−395.0 m水平试算处围岩等级略差，安全系数略低，需采用素喷支护形式；在−407.5 m水平和−420.0 m水平试算处围岩等级均为3级以上，围岩性质较差，均需要采取锚网支护形式。按需支护智评优化设计系统为龙桥铁矿提供了实时的岩体质量评估和支护方案优化，有效提高了巷道的安全性，显著节省了支护成本。

, authors=林景尚, authorsList=林景尚, authorCompany=null, correspAuthors=null, authorNote=

林景尚（1987—），男，福建泉州人，工程师，主要从事矿山采掘和通风方面的研究工作。E-mail:

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林景尚（1987—），男，福建泉州人，工程师，主要从事矿山采掘和通风方面的研究工作。E-mail:

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林景尚（1987—），男，福建泉州人，工程师，主要从事矿山采掘和通风方面的研究工作。E-mail:

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articleId=1322907565465674651, language=EN, label=Fig.2, caption=Intelligent evaluation and optimization design system of on-demand support in Longqiao Iron Mine, figureFileSmall=YbEAYh2s0szT6GEdlIUQ3g==, figureFileBig=aux+LtaGU+K3YtekI/n3hQ==, tableContent=null), ArticleFig(id=1322907575473284059, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=CN, label=图2, caption=龙桥铁矿按需支护智评优化设计系统, figureFileSmall=YbEAYh2s0szT6GEdlIUQ3g==, figureFileBig=aux+LtaGU+K3YtekI/n3hQ==, tableContent=null), ArticleFig(id=1322907575540392924, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=EN, label=Fig.3, caption=Setting configuration page, figureFileSmall=YrqJQULXwP3tRXTXbx3sFA==, figureFileBig=hP/brNO5rA/rHyTxJ/vxxQ==, tableContent=null), ArticleFig(id=1322907577209725917, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=CN, label=图3, caption=设置配置页面, 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−382.5 m level, figureFileSmall=x09sQ60GpIJc0K9nfzO9tQ==, figureFileBig=1zKa5R3BZXUcE6q+R/GmMg==, tableContent=null), ArticleFig(id=1322907578375742437, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=CN, label=图7, caption=−382.5 m水平素喷支护设计, figureFileSmall=x09sQ60GpIJc0K9nfzO9tQ==, figureFileBig=1zKa5R3BZXUcE6q+R/GmMg==, tableContent=null), ArticleFig(id=1322907578480600038, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=EN, label=Fig.8, caption=Design of anchor mesh support at −407.5 m level, figureFileSmall=9C4Xg2TsDh9Jz5bMZ6lszQ==, figureFileBig=rJ5CFqYJKwh6iqhbJrzifQ==, tableContent=null), ArticleFig(id=1322907578593846247, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=CN, label=图8, caption=−407.5 m水平锚网支护设计, figureFileSmall=9C4Xg2TsDh9Jz5bMZ6lszQ==, figureFileBig=rJ5CFqYJKwh6iqhbJrzifQ==, tableContent=null), ArticleFig(id=1322907578858087400, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=EN, label=Table 1, caption=

Datas of on-demand anchor mesh support sample library

, figureFileSmall=null, figureFileBig=null, tableContent=
序号A1A2/mA3/mA4/mA5/mA6/cmA7
13.15003.82.22158.989
23.25003.82.22155.873
33.35003.82.22153.927
43.45003.82.22152.695
53.45003.82.22152.695
⋯⋯
594.95003.81.22151.025
605.05003.81.22151.003
), ArticleFig(id=1322907578941973481, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=CN, label=表1, caption=

按需锚网支护样本库数据

, figureFileSmall=null, figureFileBig=null, tableContent=
序号A1A2/mA3/mA4/mA5/mA6/cmA7
13.15003.82.22158.989
23.25003.82.22155.873
33.35003.82.22153.927
43.45003.82.22152.695
53.45003.82.22152.695
⋯⋯
594.95003.81.22151.025
605.05003.81.22151.003
), ArticleFig(id=1322907579197826026, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=EN, label=Table 2, caption=

Datas of on-demand plain spray support sample library

, figureFileSmall=null, figureFileBig=null, tableContent=
序号B1B2/mB3/mB4B5/mmB6
11.005003.832.505.810 0
21.505003.832.502.844 0
31.805003.832.502.069 2
42.005003.832.501.724 5
51.255003.832.503.918 0
⋯⋯
593.505003.832.5805.365 9
604.005003.832.5805.279 4
), ArticleFig(id=1322907579273323499, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907565465674651, language=CN, label=表2, caption=

按需素喷支护样本库数据

, figureFileSmall=null, figureFileBig=null, tableContent=
序号B1B2/mB3/mB4B5/mmB6
11.005003.832.505.810 0
21.505003.832.502.844 0
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基于XGBoost的采场岩巷按需支护智评优化设计系统及工程应用
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林景尚
矿业研究与开发 | 矿山安全与环保 2025,45(6): 138-145
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矿业研究与开发 |矿山安全与环保 2025 , 45 (6) : 138 -145
基于XGBoost的采场岩巷按需支护智评优化设计系统及工程应用
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林景尚（1987—），男，福建泉州人，工程师，主要从事矿山采掘和通风方面的研究工作。E-mail:

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林景尚（1987—），男，福建泉州人，工程师，主要从事矿山采掘和通风方面的研究工作。E-mail:

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林景尚
作者信息
  • 安徽庐江龙桥矿业股份有限公司，安徽　庐江县　231551
作者简介:

林景尚（1987—），男，福建泉州人，工程师，主要从事矿山采掘和通风方面的研究工作。E-mail:

Intelligent Evaluation and Optimization Design System of On-Demand Support of Stope Rock Roadways Based on XGBoost and Its Engineering Applications
Jingshang LIN
Affiliations
  • Anhui Lujiang Longqiao Mining Co., Ltd., Lujiang County, Anhui 231551, China
出版时间: 2025-06-25
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摘要
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为避免出现好岩过强支护而差岩欠强支护现象，基于XGBoost算法结合巷道安全系数，开发了龙桥铁矿按需支护智评优化设计系统。采用影响龙桥铁矿巷道稳定性的7项按需锚网支护指标和5项按需素喷支护指标作为主要输入值，以60个巷道实际数据为例，进行按需优化支护设计和效果评价。结果表明：在−370.0 m水平试算处围岩等级较好，安全系数较大，可不采取支护措施；在−382.5 m水平和−395.0 m水平试算处围岩等级略差，安全系数略低，需采用素喷支护形式；在−407.5 m水平和−420.0 m水平试算处围岩等级均为3级以上，围岩性质较差，均需要采取锚网支护形式。按需支护智评优化设计系统为龙桥铁矿提供了实时的岩体质量评估和支护方案优化，有效提高了巷道的安全性，显著节省了支护成本。

XGBoost模型  /  安全系数  /  岩巷  /  效果评价  /  按需支护

In order to avoid the phenomenon that the good rock is over-supported and the poor rock is under-supported, based on the XGBoost algorithm combined with the roadway safety factor, an intelligent evaluation and optimization design system of on-demand support in Longqiao Iron Mine was developed. 7 on-demand anchor mesh support indexes and 5 on-demand plain spray support indexes affecting the stability of roadway in Longqiao Iron Mine were used as the main input values. 60 sets of practical roadway data were selected as examples to carry out the on-demand optimization support design and effectiveness evaluation. The results show that at the level of −370.0 m, the surrounding rock grade is better, the safety factor is larger, and the support measures are not needed. At the level of −382.5 m and −395.0 m, the surrounding rock grade is slightly worse, the safety factor is slightly lower, and the plain spray support form is required. At the level of −407.5 m and −420.0 m, the surrounding rock grade is above the third grade, and the surrounding rock properties are poor, so it is necessary to adopt the form of anchor mesh support. The intelligent evaluation and optimization design system of on-demand support provides real-time rock mass quality evaluation and support scheme optimization for Longqiao Iron Mine, which has effectively improved the safety of roadway and significantly saved the support cost.

XGBoost model  /  Safety factor  /  Rock roadway  /  Effectiveness evaluation  /  On-demand support
林景尚. 基于XGBoost的采场岩巷按需支护智评优化设计系统及工程应用. 矿业研究与开发, 2025 , 45 (6) : 138 -145 .
Jingshang LIN. Intelligent Evaluation and Optimization Design System of On-Demand Support of Stope Rock Roadways Based on XGBoost and Its Engineering Applications[J]. Mining Research and Development, 2025 , 45 (6) : 138 -145 .
正文
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0　引言
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矿岩采动会引起岩体的变形和塌陷，进一步加剧岩体的破坏程度。随着时间的推移，岩体的整体强度不断下降，岩体内部的应力集中现象逐渐加剧，使得岩体更加脆弱和不稳定，从而造成岩石裂隙的发育。矿岩采动是一个岩体逐渐发生变形和破坏的动态过程。由于高强度排采和频繁扰动，岩体中的断裂构造逐渐破碎，岩体质量等级不断降低，造成矿山开采工程的岩体性质恶化[1-5]。岩体裂隙的过度发育对工程安全构成了巨大的潜在风险，特别是对于矿房帮的大型露天开采活动，极易导致采场片帮、顶板坍塌，甚至频发地压灾害[6-9]。因此，加强巷道和硐室周边岩体的支护，消除片帮、冒顶等安全隐患，对于矿山安全生产意义重大。
黄成林等[10]对复杂工程地质条件下的梧桐举铁矿进行了详细的工程地质勘察，根据岩体地质力学分级方法将矿区围岩等级划分为四级，提出了相应的支护方案，并将其应用于矿区不同施工段，取得了较好支护的效果。孟庆彬等[11]针对和睦山铁矿特定的工程地质条件，对巷道围岩等级进行划分，利用FLAC3D数值模拟软件揭示了不同支护形式对围岩位移影响的时间规律，提出了不同围岩类型的合理支护形式和参数。张毅等[12]通过锚杆轴力的测试试验，提出在浅层断裂岩体中，应根据岩体工程质量采用长短结合的锚杆支护方式，实现了围岩的稳定性控制，取得了良好的支护效果。张长锁[13]应用松动圈支护理论，优化了破碎岩体支护设计的参数，推荐采用锚网喷支护，并通过工程实践证明，锚网喷适用于破碎岩体，支护效果得到了较大的改善。秦绍龙等[14]综合考虑了现场工程地质调查结果与矿区岩体质量等级等多方面因素，总结了巷道失稳的破坏类型，提出了“树脂锚杆+金属网+喷射混凝土+长锚索”联合支护方式，有效维持了围岩稳定性。
矿山巷道支护对于维持岩巷稳定性具有积极作用。目前，巷道支护设计一般参照公路隧道和铁路隧道支护规范，或基于设计者的相关工程经验进行矿山巷道支护设计。因矿山巷道服务年限短等特点，常出现巷道支护设计过于保守，造成支护成本过高，导致许多矿山难以承担，由此又造成本应支护而未支护或欠支护现象普遍存在。因此，本研究基于XGBoost模型和巷道支护安全系数，开发了采场岩巷按需支护智评优化设计系统。在巷道支护效果评价和优化设计的基础上，实现按需支护，避免出现好岩过强支护或差岩欠强支护现象，对于矿山科学管理、优化设计、降低安全风险和生产成本具有重要意义。
1　巷道支护安全系数与XGBoost模型
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1.1　巷道支护安全系数
巷道开挖后支护主要承受弯矩和轴力，因而支护主要呈弯曲抗压状态，为保证支护材料稳定性，内部应力需控制在极限强度以内[15]。在数值模拟中直接调用支护内部应力，取开挖截面水平方向为坐标面横轴，垂直方向为坐标面纵轴，壳型单元应力张量如图1所示。为保证支护结构稳定性，通过控制支护内部应力保证支护结构整体安全性的方法[16-17]，计算最小安全系数，为工程施工提供依据，以便提前对支护薄弱处进行处理。
通过内部应力与极限强度的比值计算开挖截面各点的安全系数，其计算公式为：
式中：Fs为安全系数；[σ]为内部应力；σ为极限强度。
1.2　XGBoost模型
XGBoost模型是一种集成学习算法，其基于梯度提升原理，通过依次训练一系列弱分类器（通常是决策树），每个分类器的训练都是在前一个分类器的残差上进行，最终将这些弱分类器组合成一个性能更加强大的集成分类器[18-20]。XGBoost算法通过在损失函数中加入正则项，与普通梯度提升法相比，在模型训练过程中正则化项的引入，可用于控制模型的复杂度，从而防止算法训练时过拟合。此外，XGBoost模型对损失函数进行了二阶泰勒展开，不仅能够更准确地估计目标函数的形状，还能更有效地指导模型参数的更新，提高了模型在训练过程中的收敛速度和泛化能力，进一步提升了预测的精度和效率。
XGBoost算法的目标函数为：
式中：o为损失函数；n为训练样本总数；l(yi，)为样本的训练误差，yi和ŷi分别为样本i的实际值和预测值；K为树的总数量；Ω(fk)为正则化项，用于控制叶子节点个数，防止过拟合。
当迭代至第t轮时，XGBoost算法的目标函数为：
式中：为t−1轮迭代中的预测值；ft(xi)为第t轮迭代中树模型的预测值；c为前t−1次迭代的常量之和。
对公式(3)进行二阶泰勒展开，则目标函数为：
式中：gi为样本i的一阶梯度；hi为样本i的二阶梯度。
对目标函数ot的权重ωj求偏导，则叶子节点的最优权重和函数的最优目标函数o为：
式中：Ij为属于第j个叶子节点的样本集合；γ为正侧化参数；T为树的叶子节点数。
XGBoost算法在选择最优分裂点时，采用了特征并行的方法，选择增益最大的特征值作为最佳分裂点。在选择子树分裂点时，增益函数G为：
式中：IL为左子树分裂点总集合；IR为右子树分裂点总集合；I为父节点样本集合；α为加入新叶子节点引入的复杂度代价。
XGBoost算法有3个指标可对特征进行重要性排序，分别为：某个特征在所有树中被用作分割样本的特征次数w，其若被使用的次数越多，说明该特征越重要；特征平均增益值G，表示特征对模型预测的贡献，平均增益值越高意味着该特征对模型的贡献越大；特征平均覆盖率C，表示在训练模型时，对该特征进行的平均覆盖次数，覆盖次数越多代表该特征在训练中越重要，其计算公式为：
式中：X为分类到每个叶子节点的集合；Gx为每个节点的增益；Cx为每个节点上的样本数目。
本文对这3个指标进行综合分析，以便更全面地评估每个特征对XGBoost模型的重要性，从而为特征选择和模型优化提供支持。
2　按需支护智评优化设计系统
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一般矿山采场巷道的服务年限较短，常出现巷道过支护以及应支护而未支护或欠支护的普遍现象。因此，本文以安徽省庐江县龙桥铁矿为例，采用XGBoost算法，结合巷道安全系数，借助Python软件，开发了按需支护智评优化设计系统。该系统可用于巷道快速优化支护设计与支护效果评价。
2.1　评价指标建立
巷道支护设计及效果评价受到诸多因素的影响，结合龙桥铁矿的生产实际情况，对岩体等级、二次应力大小及方向、岩石移动变形程度、开采扰动岩体响应特征、围岩破碎程度、开采扰动程度、岩体结构面条件、岩石坚硬程度、地下水条件、支护方式、产状有利性、充填物等40多项指标进行了筛选，确定采场岩巷支护设计和效果评价的多源指标。按需支护锚网支护指标为：岩体等级(A1)、埋深(A2)、巷道跨度(A3)、锚杆间距(A4)、锚杆长度(A5)、兜网间距(A6)和安全系数(A7)，具体样本数据见表1。素喷支护指标为：岩体等级(B1)、埋深(B2)、巷道跨度(B3)、水泥型号(B4)、支护厚度(B5)和安全系数(B6)，具体样本数据见表2。
2.2　系统功能介绍
在Python软件的基础上，基于表1和表2两个数据集，将A1~A6作为特征值，A7作为标签值，B1~B5作为特征值，B6作为标签值，采用XGBoost算法进行训练以预测未知参数的安全系数，并开发了龙桥铁矿按需支护智评优化设计系统，其初始页面如图2所示。其中，按需支护岩体等级模块有设计配置、素喷支护、锚网支护3个选项，如图2红色虚框所示（颜色标识见电子版）。
在按需支护设计之前，需要进行断面试算，输入三心拱断面的巷宽、巷高、拱高以及断面底板中心点的坐标，调用XGBoost模型进行断面的安全系数试算，其试算结果及支护方案建议和CAD图纸明细栏配置如图3所示。
在断面试算的基础上，素喷支护功能区中，输入素喷支护的水泥型号、喷层厚度、水泥与砂的比例等参数，依据断面试算的三心拱断面参数，系统自动设计出素喷支护设计效果图，并相应计算出巷道断面面积，依据距离幂次反比法计算该处的岩体等级与等级可信度，根据原有素喷支护数据库，通过XGBoost模型计算该处巷道的安全系数，依据安全系数判据给出相应的支护方案评价，如图4所示。
在锚网支护页面，输入锚网支护的锚杆间距、锚杆排距、锚杆长度、锚杆重量、兜网长度、兜网宽度、兜网架厚、兜网间距、兜网底距等9个参数，系统自动设计出锚网支护设计的效果图，依据距离幂次反比法计算该处的岩体等级与等级可信度，根据原有锚网支护数据库，通过XGBoos模型计算该处巷道的安全系数，依据安全系数判据给出相应的支护方案评价，如图5所示。
2.3　应用实例分析
基于按需支护智评优化设计系统，对龙桥铁矿现场−370.0 m、−382.5 m、−395.0 m、−407.5 m、−420.0 m等5个水平进行了支护效果评价和优化设计，在断面参数输入位置处，输入三心拱断面巷道的几何尺寸，其巷宽为4 m、墙高为2.5 m、拱高为1.3 m，系统会自动计算出该处的围岩等级和安全系数，并给出优化后的支护方案，如图6所示。
由图6可知，在−370.0 m水平试算处围岩等级较好，安全系数较大，可不采取支护措施；在−382.5 m水平和−395.0 m水平试算处围岩等级略差，安全系数略低，需采用巷道素喷支护形式；在−407.5 m水平和−420.0 m水平试算处围岩等级均为3级以上，围岩性质较差，均需要采取锚网支护形式。
通过按需支护智评优化设计系统，对−382.5 m水平和−407.5 m水平的方案进行支护设计与可行性分析，分别如图7和图8所示。对于−382.5 m水平来说，设置水泥型号为P⋅O 32.5、喷层厚度为20 mm、水泥∶砂为1∶3，采用XGBoost模型计算该处巷道的安全系数为3.094，满足巷道安全性要求，方案可行。对于−407.5 m水平来说，设计锚杆间距为1.0 m、锚杆排距为1.1 m、锚杆长度设计为2 m、锚杆重量为7.8 kg/m、兜网长度为3.3 m、兜网宽度为1.5 m、兜网架厚为120 mm、兜网间距为150 mm、兜网底距为1.2 m，采用XGBoost模型计算该处巷道的安全系数为2.57，满足巷道安全性要求，方案可行。
通过按需支护智评优化设计系统实现了对不同水平的安全性评价及快速支护设计，可见该系统是一个科学、高效、可靠的巷道支护设计工具，通过综合运用大数据+人工智能技术，提供实时的岩体质量评估和支护方案优化，有效提高了巷道的安全性，显著节省了支护成本。
3　结论
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基于XGBoost模型和巷道支护安全系数，借助Python软件，开发了龙桥铁矿按需支护智评优化设计系统，该系统用于巷道快速优化支护设计与效果评价。得出的主要结论如下。
（1）采用影响龙桥铁矿巷道稳定性的7项按需锚网支护指标和5项按需素喷支护指标作为主要输入值，以60个巷道实际数据为例，进行按需优化支护设计和效果评价。
（2）基于该系统对现场−370.0 m、−382.5 m、−395.0 m、−407.5 m、−420.0 m等5个水平进行支护评价和优化设计。在−370.0 m水平试算处围岩等级较好，安全系数较大，可不采取支护措施；在−382.5 m水平和−395.0 m水平试算处围岩等级略差，安全系数也略低，需要采用素喷支护形式；在−407.5 m水平和−420.0 m水平试算处围岩等级均为3级以上，围岩性质较差，需要采取锚网支护形式。
（3）该系统不仅评价了采场巷道围岩等级，还对巷道结构、岩体稳定性等关键因素进行综合评估，以确定最佳支护设计方案。与传统支护设计方法相比，该系统能够更精准地评估巷道的安全性和稳定性，快速优化支护设计，有效提高了采场巷道围岩的安全性，显著节省了支护成本。
基金
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  • 深部煤矿采动响应与灾害防控国家重点实验室开放基金重点项目(SKLMRDPC22KF02)
文献
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2025年第45卷第6期
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  • 接收时间：2024-07-26
  • 首发时间：2026-10-30
  • 出版时间：2025-06-25
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  • 收稿日期：2024-07-26
基金
深部煤矿采动响应与灾害防控国家重点实验室开放基金重点项目(SKLMRDPC22KF02)
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    安徽庐江龙桥矿业股份有限公司，安徽　庐江县　231551
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