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Article(id=1241687534820970935, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241687532522492319, articleNumber=null, orderNo=null, doi=10.3963/j.issn.1001-487X.2023.03.027, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1681747200000, receivedDateStr=2023-04-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773970977740, onlineDateStr=2026-03-20, pubDate=1693497600000, pubDateStr=2023-09-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773970977740, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773970977740, creator=13701087609, updateTime=1773970977740, updator=13701087609, issue=Issue{id=1241687532522492319, tenantId=1146029695717560320, journalId=1240670690148397066, year='2023', volume='40', issue='3', pageStart='1', pageEnd='242', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773970977192, creator=13701087609, updateTime=1773971036114, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241687779722187605, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241687532522492319, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241687779722187606, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241687532522492319, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=199, endPage=205, ext={EN=ArticleExt(id=1241687535081017792, articleId=1241687534820970935, tenantId=1146029695717560320, journalId=1240670690148397066, language=EN, title=Establishment and Application of Blasting Vibration Prediction System based on SSA-XGBoost, columnId=1240702076553065119, journalTitle=Blasting, columnName=BLASTING SAFETY, runingTitle=null, highlight=null, articleAbstract=

The peak particle velocity (PPV) of blasting vibration is an important index to measure the impact of blasting vibration on surrounding environment and structures. In order to improve the reliability of PPV prediction, a model based on extreme gradient boosting optimized by the sparrow search algorithm was proposed, and a corresponding blasting vibration prediction system was built using the App Designer of MATLAB. The maximum charge per delay, distance from blast center to measuring point, and elevation difference between measuring point and blast center were selected as the input parameters of 36 sets of training data and 5 sets of test data for the model to predict PPV. The results show that the proposed SSA-XGBoost model has a smaller average relative error compared with the GA-BPNN model and BPNN model, and it has a higher prediction accuracy and better stability proved by the Taylor graph.

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ZHANG Chun-yang (1981-), male, Ph. D, associate professor, mainly engaged in research on mining and rock mechanics, (E-mail)
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爆破振动质点速度峰值（Peak particle velocity，PPV）是衡量爆破振动对周围环境和结构物造成的影响程度的重要指标。为提高爆破振动质点速度峰值预测的可靠性，提出了一种基于麻雀搜索算法（Sparrow search algorithm，SSA）优化极端梯度提升树（Extreme gradient boosting，XGBoost）的PPV预测模型，通过Matlab软件中的App Designer模块构建了相应的爆破振动预测系统。通过36组训练数据和5组测试数据，选取了最大单段炸药量、爆心距和测点至爆破工作面的高程差作为模型输入参数，对PPV进行了预测。结果表明，提出的SSA-XGBoost模型相较于GA-BPNN模型和BPNN模型具有更小的平均相对误差，泰勒图进一步证明了SSA-XGBoost具有更高的预测精度和更好的稳定性。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
张春阳（1981-），男，博士、副教授，主要从事矿山开采及岩石力学方面的研究，（E-mail）。
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邹平（1983-），男，博士、高级工程师，主要从事矿山开采技术与安全方面的研究工作，（E-mail）。

ZOU Ping (1983-), male, Ph. D, senior engineer, mainly engaged in research on mining technology and safety, (E-mail) .

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邹平（1983-），男，博士、高级工程师，主要从事矿山开采技术与安全方面的研究工作，（E-mail）。

ZOU Ping (1983-), male, Ph. D, senior engineer, mainly engaged in research on mining technology and safety, (E-mail) .

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邹平（1983-），男，博士、高级工程师，主要从事矿山开采技术与安全方面的研究工作，（E-mail）。

ZOU Ping (1983-), male, Ph. D, senior engineer, mainly engaged in research on mining technology and safety, (E-mail) .

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Systems science & control engineering, 2020, 8(1): 22-34., articleTitle=A novel swarm intelligence optimization approach: sparrow search algorithm, refAbstract=null)], funds=[Fund(id=1241687547710066805, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, awardId=52174088, language=EN, fundingSource=National Natural Science Foundation of China(52174088), fundOrder=null, country=null), Fund(id=1241687547819118722, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, awardId=52174088, language=CN, fundingSource=国家自然科学基金项目(52174088), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241687539728306807, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, xref=1., ext=[AuthorCompanyExt(id=1241687539736695417, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, companyId=1241687539728306807, language=EN, country=null, 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city=null, postcode=null, companyName=null, departmentName=null, remark=3.武汉理工大学　资源与环境工程学院，武汉　430070)])], figs=[ArticleFig(id=1241687544069411738, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, language=EN, label=Fig. 1, caption=SSA-XGBoost model, figureFileSmall=RQz292HisxQ/v3nShEzeWA==, figureFileBig=M6e70ZuAMmXphR8bXW1UAQ==, tableContent=null), ArticleFig(id=1241687544191046569, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, language=CN, label=图1, caption=SSA-XGBoost模型, figureFileSmall=RQz292HisxQ/v3nShEzeWA==, figureFileBig=M6e70ZuAMmXphR8bXW1UAQ==, tableContent=null), ArticleFig(id=1241687546065900487, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, language=EN, label=Fig. 2, caption=Blasting vibration prediction system interface, figureFileSmall=jz/5Ur3OzwyEc993ti5Afw==, figureFileBig=6n2nKXM6jaSJBUDtXX9hWg==, tableContent=null), 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ArticleFig(id=1241687546900566067, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, language=EN, label=Table 1, caption=

Blasting vibration data

, figureFileSmall=null, figureFileBig=null, tableContent=
编号最大单段炸药量/kg爆心距/m高程差/m速度/（cm·s-1）
119.041.05.04.10
214.045.04.82.30
310.046.04.71.25
416.043.05.03.00
519.026.02.09.70
612.030.02.02.00
715.031.01.92.80
817.028.02.34.00
915.030.02.02.50
1028.825.03.09.20
1112.026.03.03.00
1225.028.02.86.00
1317.029.03.12.00
1428.016.00.03.00
1515.015.00.01.25
1612.038.07.02.50
1710.036.07.52.00
189.042.07.01.00
1911.063.00.01.70
2010.060.00.51.60
2110.070.00.01.50
2231.226.05.05.02
2316.229.74.81.25
2425.023.05.03.90
2531.025.05.05.00
2620.028.05.12.30
2731.046.03.04.10
2816.051.02.71.80
2924.043.03.03.70
3020.040.03.22.90
3130.026.05.015.70
3228.024.05.012.50
3329.028.54.914.00
3430.046.03.05.00
3528.044.03.04.50
3630.043.03.06.00
3729.045.03.04.50
3815.014.00.01.20
3928.028.55.113.00
4017.050.03.01.60
4110.040.06.92.30
), ArticleFig(id=1241687547013812287, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, language=CN, label=表1, caption=

爆破振动数据样本

, figureFileSmall=null, figureFileBig=null, tableContent=
编号最大单段炸药量/kg爆心距/m高程差/m速度/（cm·s-1）
119.041.05.04.10
214.045.04.82.30
310.046.04.71.25
416.043.05.03.00
519.026.02.09.70
612.030.02.02.00
715.031.01.92.80
817.028.02.34.00
915.030.02.02.50
1028.825.03.09.20
1112.026.03.03.00
1225.028.02.86.00
1317.029.03.12.00
1428.016.00.03.00
1515.015.00.01.25
1612.038.07.02.50
1710.036.07.52.00
189.042.07.01.00
1911.063.00.01.70
2010.060.00.51.60
2110.070.00.01.50
2231.226.05.05.02
2316.229.74.81.25
2425.023.05.03.90
2531.025.05.05.00
2620.028.05.12.30
2731.046.03.04.10
2816.051.02.71.80
2924.043.03.03.70
3020.040.03.22.90
3130.026.05.015.70
3228.024.05.012.50
3329.028.54.914.00
3430.046.03.05.00
3528.044.03.04.50
3630.043.03.06.00
3729.045.03.04.50
3815.014.00.01.20
3928.028.55.113.00
4017.050.03.01.60
4110.040.06.92.30
), ArticleFig(id=1241687547194167369, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, language=EN, label=Table 2, caption=

Prediction results and errors of training set

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编号实测速度/（cm·s-1）预测速度/（cm·s-1）相对误差/%
GA-BPNN模型SSA-XGBoost模型GA-BPNN模型SSA-XGBoost模型
14.103.894.105.010.00
22.302.322.260.951.74
31.251.281.262.500.80
43.003.023.000.700.00
59.709.419.722.980.21
62.002.172.056.332.50
72.802.402.8114.430.36
84.004.863.9921.460.25
92.503.132.4925.210.40
109.208.209.2110.860.11
113.002.583.0014.160.00
126.005.826.003.020.00
132.001.451.9927.460.50
143.002.873.024.210.67
151.251.461.2616.630.80
162.502.412.493.730.40
172.001.922.003.940.00
181.601.861.6216.261.25
191.701.591.716.710.59
201.000.991.090.969.00
211.501.581.495.320.67
225.024.845.023.540.00
231.251.481.2618.290.80
243.903.823.912.110.26
255.005.004.980.090.40
262.302.322.310.980.43
274.103.964.113.370.24
281.801.671.797.220.56
293.703.903.675.340.81
302.902.532.9012.810.00
3115.7015.4315.711.750.06
3212.5012.6612.491.270.08
3314.0015.5914.0111.380.07
345.003.624.9927.510.20
354.504.284.494.880.22
366.005.206.0113.310.17
), ArticleFig(id=1241687547336773718, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, language=CN, label=表2, caption=

训练数据预测结果及误差

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编号实测速度/（cm·s-1）预测速度/（cm·s-1）相对误差/%
GA-BPNN模型SSA-XGBoost模型GA-BPNN模型SSA-XGBoost模型
14.103.894.105.010.00
22.302.322.260.951.74
31.251.281.262.500.80
43.003.023.000.700.00
59.709.419.722.980.21
62.002.172.056.332.50
72.802.402.8114.430.36
84.004.863.9921.460.25
92.503.132.4925.210.40
109.208.209.2110.860.11
113.002.583.0014.160.00
126.005.826.003.020.00
132.001.451.9927.460.50
143.002.873.024.210.67
151.251.461.2616.630.80
162.502.412.493.730.40
172.001.922.003.940.00
181.601.861.6216.261.25
191.701.591.716.710.59
201.000.991.090.969.00
211.501.581.495.320.67
225.024.845.023.540.00
231.251.481.2618.290.80
243.903.823.912.110.26
255.005.004.980.090.40
262.302.322.310.980.43
274.103.964.113.370.24
281.801.671.797.220.56
293.703.903.675.340.81
302.902.532.9012.810.00
3115.7015.4315.711.750.06
3212.5012.6612.491.270.08
3314.0015.5914.0111.380.07
345.003.624.9927.510.20
354.504.284.494.880.22
366.005.206.0113.310.17
), ArticleFig(id=1241687547416465505, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241687534820970935, language=EN, label=Table 3, caption=

Prediction results and errors of testing set

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编号实测速度/（cm·s-1）GA-BPNN模型预测结果/（cm·s-1）相对误差/%BPNN模型预测结果/（cm·s-1）相对误差/%SSA-XGBoost模型预测结果/（cm·s-1）相对误差/%
14.53.9711.884.9710.434.571.56
21.21.181.681.4218.291.210.83
313.012.930.5614.219.2913.030.23
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基于SSA-XGBoost的爆破振动预测系统的构建与应用
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邹平 1 , 王亮 2 , 戴勇 1 , 张春阳 3
爆破 | 安全与管理 2023,40(3): 199-205
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爆破 | 安全与管理 2023, 40(3): 199-205
基于SSA-XGBoost的爆破振动预测系统的构建与应用
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邹平1 , 王亮2, 戴勇1, 张春阳3
作者信息
  • 1.紫金（长沙）工程技术有限公司，长沙　410000
  • 2.北方矿业有限责任公司，北京　100053
  • 3.武汉理工大学　资源与环境工程学院，武汉　430070

    • 邹平（1983-），男，博士、高级工程师，主要从事矿山开采技术与安全方面的研究工作，（E-mail）。

    ZOU Ping (1983-), male, Ph. D, senior engineer, mainly engaged in research on mining technology and safety, (E-mail) .

通讯作者:

张春阳（1981-），男，博士、副教授，主要从事矿山开采及岩石力学方面的研究，（E-mail）。
Establishment and Application of Blasting Vibration Prediction System based on SSA-XGBoost
Ping ZOU1 , Liang WANG2, Yong DAI1, Chun-yang ZHANG3
Affiliations
  • 1.Zijin (Changsha) Engineering Technology Co., Ltd., Changsha 410000, China
  • 2.NorinMinging Co., Ltd., Beijing 100053, China
  • 3.School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
出版时间: 2023-09-01 doi: 10.3963/j.issn.1001-487X.2023.03.027
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摘要
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爆破振动质点速度峰值（Peak particle velocity，PPV）是衡量爆破振动对周围环境和结构物造成的影响程度的重要指标。为提高爆破振动质点速度峰值预测的可靠性，提出了一种基于麻雀搜索算法（Sparrow search algorithm，SSA）优化极端梯度提升树（Extreme gradient boosting，XGBoost）的PPV预测模型，通过Matlab软件中的App Designer模块构建了相应的爆破振动预测系统。通过36组训练数据和5组测试数据，选取了最大单段炸药量、爆心距和测点至爆破工作面的高程差作为模型输入参数，对PPV进行了预测。结果表明，提出的SSA-XGBoost模型相较于GA-BPNN模型和BPNN模型具有更小的平均相对误差，泰勒图进一步证明了SSA-XGBoost具有更高的预测精度和更好的稳定性。

爆破振动  /  爆破振动速度峰值  /  麻雀搜索算法  /  极端梯度提升树  /  预测系统

The peak particle velocity (PPV) of blasting vibration is an important index to measure the impact of blasting vibration on surrounding environment and structures. In order to improve the reliability of PPV prediction, a model based on extreme gradient boosting optimized by the sparrow search algorithm was proposed, and a corresponding blasting vibration prediction system was built using the App Designer of MATLAB. The maximum charge per delay, distance from blast center to measuring point, and elevation difference between measuring point and blast center were selected as the input parameters of 36 sets of training data and 5 sets of test data for the model to predict PPV. The results show that the proposed SSA-XGBoost model has a smaller average relative error compared with the GA-BPNN model and BPNN model, and it has a higher prediction accuracy and better stability proved by the Taylor graph.

blasting vibration  /  peak particle velocity  /  sparrow search algorithm  /  extreme gradient boosting  /  prediction system
邹平, 王亮, 戴勇, 张春阳. 基于SSA-XGBoost的爆破振动预测系统的构建与应用. 爆破, 2023 , 40 (3) : 199 -205 . DOI: 10.3963/j.issn.1001-487X.2023.03.027
Ping ZOU, Liang WANG, Yong DAI, Chun-yang ZHANG. Establishment and Application of Blasting Vibration Prediction System based on SSA-XGBoost[J]. Blasting, 2023 , 40 (3) : 199 -205 . DOI: 10.3963/j.issn.1001-487X.2023.03.027
正文
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钻爆法始于20世纪初，其具有高效、灵活、成本低的优点，经过多年的实践和改进，已经成为现代矿山开采中广泛使用的方法之一[1]。然而，爆破过程产生能量的利用率较低，近70%到80%的爆炸能量会在传播中散失，这些散失的能量会产生爆破振动、噪音、飞石和爆破后冲等危害[2]。爆破振动作为爆破过程产生的危害之一，极大地影响边坡和围岩的稳定性，并对周围建筑和结构物造成损坏，如破裂、倒塌等。因此，爆炸作业前准确估算爆破振动效应能够优化爆破设计参数，从而降低爆破振动对周围岩体和建构筑物的危害。
爆破振动质点速度峰值（Peak particle velocity，PPV）是指当药包中心到某一建筑物的距离为标准距离时，爆破地震对建筑物及地基产生的质点振动速度最大值，常用来评估爆破振动对周围环境和结构物造成的影响程度。为了评价爆破振动效应水平，诸多学者提出了经验公式以预测PPV，常用的有萨道夫斯基公式[3]、美国矿务局公式[4]、印度标准公式[5]、通用公式[6]、印度中央矿业研究站公式等[7]。但是这些传统的经验公式仅仅考虑了爆心距和最大单段炸药量，并且定义的场地系数、衰减系数和拟合模型公式都是基于特定的岩体性质参数和地形地貌，因此这些经验模型预测结果在大多数情况下要么低估了爆破振动，要么高估了爆破振动。而爆破振动效应的大小直接影响周边建构筑物的稳定以及采掘开挖作业的安全高效运行。因此，有必要构建一种具有普适性、泛化能力强的预测模型。
工程问题中，研究变量与影响因素之间往往存在复杂的非线性关系，过去数十年间，诸多软科学技术被用来解决采矿、土木和岩土工程问题。许多学者已经成功将这些技术用于构建爆破振动预测模型。在国外，Faradonbeh等提出使用基因表达式编程对PPV进行预测[8]；Khandelwal等通过51组数据构建了分类与回归树模型预测PPV[9]，并与经验模型和多元线性回归模型进行了对比分析；Fisne等利用模糊推理系统对土耳其Akdaglar采石场获取的爆破振动数据进行了预测[10]；Armaghani等通过帝国主义竞争算法分别推导了幂方程式和二次方程式用于预测PPV[11]，结果表明：二次方程式能够获得更准确的预测结果。在国内，范勇等通过余弦振幅法得出了纵波波速和高程差是除了爆心距和最大单段炸药量以外影响爆破振动传播的重要影响因素[12]，选取这四个影响因素构建的PSO-BP神经网络模型，BP神经网络模型和萨道夫斯基公式对比分析表明，PSO-BP神经网络模型预测的结果更为可靠。郭钦鹏等建立了GA-BP神经网络模型对PPV进行预测[13]，结果表明：GA-BP神经网络模型相对于权值和阈值未优化的BP神经网络模型稳定性更高，精度更好。郑皓文等选取了孔径、孔斜、孔距、孔深、最大单段炸药量等12个影响爆破振动效应水平的爆破参数建立了ACOR-LSSVM模型[14]，最终对白鹤滩水电站爆破振动效应进行了预测。
上述成功的应用案例表明，软科学技术在解决PPV预测这类非线性问题上具有优势。鉴于此，本文尝试利用麻雀搜索算法（Sparrow Search Algorithm，SSA）对极端梯度提升树算法（Extreme Gradient Boosting，XGBoost）的超参数进行优化，在考虑爆心距、最大单段炸药量和高程差的情况下，构建SSA-XGBoost预测模型对PPV进行预测。
1　SSA-XGBoost预测模型基本原理
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1.1　XGBoost算法基本原理
XGBoost算法是一种集成学习算法，被广泛用于回归和分类问题中[15]。它是一种决策树算法，通过逐步训练多个弱分类器来构建一个强大的集成分类器。XGBoost算法的主要思想是优化损失函数，以找到最佳模型。
XGBoost算法的回归问题涉及以下公式和原理：
假设D1={（xi，yi）}是由n个样本和m个特征组成的数据集。附加函数z被集合树模型用来近似系统响应，如下
式中，F为包含Z棵树的函数空间，被定义为
式中：q表示了树的结构；T为叶子个数；ω表示叶子的权重。ωq（x）为叶子节点q的分数，f（x）为某一独立树，fz是与q、ω相联系并与独立树相关的函数。
为了优化集合树预测性能，定义XGBoost的目标函数为
L为显示预测误差的凸形损失函数，yi是真实值，k是误差最小化过程的迭代次数。
1.2　SSA算法基本原理
Xue等人建立了数学模型来理想化麻雀的觅食行为[16]，并进行了相关研究。麻雀搜索算法（Sparrow Search Algorithm，SSA）的数学模型建立如下：
将70%的麻雀分配为生产者。在每次迭代循环中，发现者的位置更新规则如下
式中：t为当前迭代次数；Xij表示第i只麻雀在第j维的位置；α是（0，1]内的随机数；tmax表示最大迭代次数；安全值ST为区间[0.5，1]中的随机数。预警值R2为[0，1]中的随机数；随机数Q服从高斯分布；L是一个1×d的矩阵。当R2≥ST时，这意味着生产者意识到危险，所有麻雀都到其他安全的地方。如果R2<ST，意味着生产者没有发现捕食者，发现者进行更广泛地搜索。
追随者用以下公式更新自己的位置
式中：Xb和Xw分别为发现者搜索的全局最优位置和全局最差位置。A表示大小为1×d的矩阵，其中每个元素随机设置为-1或1，且A+=AT·（AAT）-1。当i≤l/2时，加入者与找到最佳位置的生产者竞争食物。否则，适应度较低的加入者处于饥饿状态，需要飞往其他地方觅食。
当意识到捕食者的危险时，麻雀的位置改变规则如下
式中：Xbest为当前全局最优位置；K是[-1，1]中的随机数。步长控制参数β为服从正态分布，方差为1，均值为0的随机数。Fi、fg、fw分别为当前麻雀的适应度值、全局最佳适应度值和全局最差适应度值。ψ设为10-8，避免分母为零。
1.3　SSA-XGBoost模型
图1展示了麻雀搜索算法优化XGBoost算法的具体流程。为了提高模型的预测精度和计算效率，将数据集归一化至[-1，1]的范围内，并将训练集的均方误差（MSE）指定为适应度函数。利用麻雀搜索算法全局寻优能力，在设置的搜索空间内对XGBoost的超参数（树的个数、学习率、叶子节点最小样本数目）不断迭代优化，以构建最优SSA-XGBoost预测模型。其中，树的个数优化范围默认为[1，100]、学习率优化范围默认为[0.01，10]、叶子节点最小样本数目优化范围默认为[0.01，10]。
2　爆破振动预测系统的构建
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通过MATLAB中的App Designer模块对爆破振动预测模型进行可视化系统构建。App Designer提供了一个交互式的可视化界面，使得用户可以通过简单的鼠标操作来完成复杂的机器学习回归预测任务。用户可以通过拖放、调整参数等方式来探索数据和模型，并及时获得反馈。用户无需手动编写代码，只需根据需要进行简单的配置和参数调整即可，能够帮助初学用户或者无经验的现场工作人员提供极大的便利。不同模块的使用顺序分别如下。
2.1　用户设置模块
该模块用于选择优化算法和回归算法，并对元启发式优化算法中的迭代次数和种群数量进行设定，如图2所示。“优化算法选择”和“回归算法选择”三个下拉框用于选择不同的算法构建组合模型。“迭代次数”和“种群数量”分别用于设定不同迭代次数和种群数量。
2.2　输入参数设定模块
该模块可以设定输入参数用于后续模型的预测。在输入参数设定时，需要注意单位转换。
2.3　按键操作区模块
按键操作区模块是镐型截齿截割力及比能耗预测软件最核心的部分，该部分包括“读取训练数据”、“训练预测模型”、“测试结果预测”、“清空结果”和“退出程序”按键。
①读取训练数据
点击“读取训练数据”按钮后，选择需要的训练数据表格，此时运行说明区会出现“已成功读取训练数据！”。
②训练预测模型
在读取训练数据之前应确保用户设置模块和参数输入模块选择和填写完毕，否则会出现提醒弹框，点击“训练预测模型”后，直到运行说明区出现“已成功训练预测模型！”，才能进行后续操作。
③测试集预测
模型训练完毕后，点击“测试集预测”按钮，当用户遗漏设定测试集输入数据步骤时，会出现提醒弹框，以提醒用户进行操作。当测试集成功预测时，运行说明区出现“已成功训练预测模型！”，此时预测结果显示模块会出现PPV的预测值。
④清空结果和退出程序
完成模型训练和预测后，用户可以通过点击“清空结果”按钮清楚数据，再次循环上述流程进行新一轮测试集预测。在完成用户预测需求后，可以点击“退出程序”按钮退出程序。
3　爆破振动预测系统的应用
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3.1　训练SSA-XGBoost模型
收集了文献[13]中的41组数据，如表1所示。数据来源于华润小径湾爆破工程，采用TC-4850测振仪对现场设置的振动监测点进行PPV监测，根据《爆破安全规程》（GB6722—2014）同时测定质点振动相互垂直的三个分量，并且PPV指定为三个分量中的最大值。选取了和文献中相同的前36组数据作为训练数据，后5组作为测试数据，以爆心距、最大单段炸药量和高程差（测点与爆破作业点垂直高度差）为模型输入参数，以此与文献中提出的GA-BPNN模型、BPNN模型进行对比，验证SSA-XGBoost模型的准确性。
利用构建的爆破振动预测系统，读取训练数据，并将麻雀搜索算法的最大迭代次数和种群数量分别设置为500和50。模型训练完毕后，将需要预测的数据输入对应的数值框，使用SSA-XGBoost模型对PPV进行预测。SSA-XGBoost模型结果与GA-BPNN和BPNN模型训练数据预测结果如表2所示。
其中，GA-BPNN模型和SSA-XGBoost模型训练样本的预测相对误差最大值分别为25.21%和9%，最小值分别为0.09%和0，平均相对误差分别为8.52%和0.68%。由此可见，在训练样本的表现上，SSA-XGBoost模型虽然在个别PPV实测值较小的样本上相对误差较大，但是整体相对误差较小，并且最大相对误差、最小相对误差和平均相对误差均小于GA-BPNN模型，表现出更好的预测性能。
3.2　SSA-XGBoost模型预测结果分析
将五组测试数据分别输入对应的数值框，得出SSA-XGBoost模型预测结果，与GA-BPNN模型和BPNN模型结果比较如表3和图3所示。
由表3可知，GA-BPNN模型和BPNN模型测试数据的预测相对误差最大值分别为11.88%和29.81%，最小值分别为0.56%和3.15%，平均相对误差分别为4.33%和14.19%；而SSA-XGBoost模型的测试数据的预测相对误差最大值为4.35%，最小值为0.23%，平均相对误差为1.64%。为了更直观地比较不同模型预测结果误差，图4展示了GA-BPNN模型、BPNN模型和SSA-XGBoost模型对五组测试数据预测结果的相对误差。
可以看出，提出的SSA-XGBoost模型在测试数据预测表现上相较于GA-BPNN模型和BPNN模型更加稳定，并且具有较低的预测相对误差。为了更直观地展示模型的优越性，使用了泰勒图用于表示不同模型对于同一测试集的拟合程度，如图5所示。散点与参考点之间的直线距离越近，模型RMSE越小，即模型的精度最好。标准差越小说明数值波动较小，结果更稳定。相关系数越大，模型性能越好。图5中可以看出，SSA-XGBoost模型代表点相较于GA-BPNN模型和BPNN模型更靠近参考点，说明SSA-XGBoost模型相较于其他模型具有更优异的性能，可以作为预测PPV的首选模型。
4　结论
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（1）通过SSA对XGBoost模型的超参数进行优化，通过构建了SSA-XGBoost模型及相应的爆破振动预测系统，实现了SSA-XGBoost模型的可视化操作，便于用户使用，可操作性和实用性强。
（2）采用SSA-XGBoost模型，选取了最大单段炸药量、爆心距和高程差作为模型输入参数，预测结果显示，SSA-XGBoost模型的测试数据的预测相对误差最大值、最小值和平均相对误差相对GA-BP分别降低了7.534%、0.33%和2.69%，各模型的泰勒图也证明了SSA-XGBoost模型的泛化能力，说明该模型更适用于预测PPV。
基金
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  • 国家自然科学基金项目(52174088)
文献
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参考文献 引证文献
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李浩荡, 佘长超, 周永利, 等. 我国露天煤矿开采技术综述及展望[J]. 煤炭科学技术, 2019, 47(10): 24-35.
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ARMAGHANI D J, HASANIPANAH M, AMNIEH H B, et al. Feasibility of ICA in approximating ground vibration resulting from mine blasting[J]. Neural Computing and Applications, 2018, 29: 457-465.
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FAN Yong, PEI Yong, YANG Guang-dong, et al. Prediction of blasting vibration velocity peak based on an improved PSO-BP neural network[J]. Journal of Vibration and Shock, 2022, 41(16): 194-203, 302. in Chinese)
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郭钦鹏, 杨仕教, 朱忠华, 等. 运用GA-BP神经网络对爆破振动速度预测[J]. 爆破, 2020, 37(3): 148-152.
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郑皓文, 赵根, 胡英国, 等. 基于ACOR-LSSVM算法的爆破振动速度预测[J]. 爆破, 2018, 35(3): 154-158.
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2023年第40卷第3期
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文章信息
doi: 10.3963/j.issn.1001-487X.2023.03.027
  • 接收时间：2023-04-18
  • 首发时间：2026-03-20
  • 出版时间：2023-09-01
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  • 收稿日期：2023-04-18
基金
National Natural Science Foundation of China(52174088)
国家自然科学基金项目(52174088)
作者信息
    1.紫金（长沙）工程技术有限公司，长沙　410000
    2.北方矿业有限责任公司，北京　100053
    3.武汉理工大学　资源与环境工程学院，武汉　430070

通讯作者:

张春阳（1981-），男，博士、副教授，主要从事矿山开采及岩石力学方面的研究，（E-mail）。
参考文献
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2种不同金属材料的力学参数
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species		 占总种数比例
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total species (%)		 属
Genus		 种数
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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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