Article(id=1284878582048592776, tenantId=1146029695717560320, journalId=1283840460791746584, issueId=1284096873799594072, articleNumber=PA20260711_Np2Y7dyF, orderNo=null, doi=10.13637/j.issn.1009-6094.2025.0770, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1784268525850, onlineDateStr=2026-07-17, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784268525849, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784268525849, creator=admin, updateTime=1784268525849, updator=admin, issue=Issue{id=1284096873799594072, tenantId=1146029695717560320, journalId=1283840460791746584, year='2026', volume='26', 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exPlanations, SHAP)从全局和局部角度分别解释影响因素预测PPV的机制。结果表明:GA-VR模型在测试数据上R~2为0.948,预测性能优于6种对比模型及5类异质优化算法。通过SHAP分析可知影响因素中测点与爆破位置水平距离占主导因素,其次为最大段药量、测点与最小抵抗线夹角。提出的模型在保障高精度预测的同时提供了具有可解释的预测依据,可用于指导爆破参数的智能优化与安全控制。, authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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基于可解释GA-VR集成学习的爆破质点峰值振动速度预测
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杨溢 1 , 王淑贤 1 , 沈亚玺 2 , 石玉莲 3 , 丁秋月 1,4
安全与环境学报 | “新质生产力驱动的矿山安全与绿色转型技术”专题 2026,26(6):
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安全与环境学报 |“新质生产力驱动的矿山安全与绿色转型技术”专题 2026 , 26 (6) :
基于可解释GA-VR集成学习的爆破质点峰值振动速度预测
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杨溢1 , 王淑贤1 , 沈亚玺2 , 石玉莲3 , 丁秋月1,4
作者信息
  • 1.昆明理工大学公共安全与应急管理学院
  • 2.天津大学建筑工程学院
  • 3.昆明理工大学国土资源工程学院
  • 4.云南智科安全咨询有限公司
Affiliations
出版时间: 2026-06-25 doi: 10.13637/j.issn.1009-6094.2025.0770
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为提升爆破质点峰值振动速度(Peak Particle Velocity, PPV)的预测精度,提出了一种基于遗传算法(Genetic Algorithm, GA)优化的投票回归(Voting Regressor, VR)集成模型(GA-VR)。基于某露天矿实测数据,采用GA优化梯度提升回归、随机森林回归和决策树回归的超参数,并通过投票机制集成模型;同时利用夏普利加性解释(SHapley Additive exPlanations, SHAP)从全局和局部角度分别解释影响因素预测PPV的机制。结果表明:GA-VR模型在测试数据上R~2为0.948,预测性能优于6种对比模型及5类异质优化算法。通过SHAP分析可知影响因素中测点与爆破位置水平距离占主导因素,其次为最大段药量、测点与最小抵抗线夹角。提出的模型在保障高精度预测的同时提供了具有可解释的预测依据,可用于指导爆破参数的智能优化与安全控制。
安全工程  /  爆破振动预测  /  遗传算法  /  集成学习  /  可解释分析  /  特征工程
杨溢, 王淑贤, 沈亚玺, 石玉莲, 丁秋月. 基于可解释GA-VR集成学习的爆破质点峰值振动速度预测. 安全与环境学报, 2026 , 26 (6) . DOI: 10.13637/j.issn.1009-6094.2025.0770
  • 国家自然科学基金项目
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doi: 10.13637/j.issn.1009-6094.2025.0770
  • 首发时间:2026-07-17
  • 出版时间:2026-06-25
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国家自然科学基金项目
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    1.昆明理工大学公共安全与应急管理学院
    2.天津大学建筑工程学院
    3.昆明理工大学国土资源工程学院
    4.云南智科安全咨询有限公司
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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