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The distribution characteristics of blasting pile is an important index indicator to evaluate blasting effect. In view of the inadequacy of the current direct and indirect methods of measuring fragment size of the blast pile, a spatial distribution measurement method for adaptive stratification of the blast pile is proposed. It uses the GA-LSSVM model to predict the shape parameters α and β of the Weibull function and sets multiple prediction points to predict the three-dimensional blasting pile morphology. By converting and fusing the parameters of the Kuz-Ram fragment prediction model, a distance prediction model of blast pile stratification is established and applied to the Weibull-GA-LSSVM model to achieve an automatic stratification of the blast pile. Through field application, the stratification design is continuously optimized for the best stratification position to realize the adaptive stratification. The results show that: (1) the Weibull-GA-LSSVM model can accurately predict the morphology of the blast pile with a good stability that the average relative error of the prediction results of the maximum forward distance of the blast pile is only 5.6% and the relative error of the prediction results of the looseness coefficient is mostly around 9%. (2) The Kuz-Ram-based blast pile stratification model can reasonably output the layer distance and number before blast, which ensures the shoveling efficiency after blast. (3) The optimal layer distance formula is derived to achieve the adaptive stratification of the blast pile, and the measurement accuracy of the fragment size distribution of the blast pile is significantly improved, which is closer to the overall fragment size distribution.

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爆堆块度分布特征是评价爆破效果的重要指标,针对目前爆堆块度直接与间接法测量方法的不足,提出了一种爆堆自适应分层的块度空间分布测量方法,该方法使用GA-LSSVM模型来预测Weilbull函数的形状参数αβ,并设置多个预测点,生成三维爆堆形态预测曲线。同时对Kuz-Ram块度预测模型参数进行转换和融合,建立了爆堆分层距离预测模型,并将其导入Weilbull-GA-LSSVM爆堆形态预测模型,实现爆堆自动分层。通过现场应用,不断优化分层设计,探究爆堆最佳分层位置,达到爆堆自适应分层的效果。以广东省大排矿山为工程依托开展现场试验,结果表明:(1)Weibull-GA-LSSVM模型能够准确预测爆堆形态,其中爆堆最大前移距离预测结果的平均相对误差仅为5.6%,松散系数预测结果的相对误差多数在9%左右,表现出良好的稳定性。(2)爆堆自动分层模型能够在爆前合理地输出分层距离以及分层层数,保证了爆后现场爆堆的铲装效率。(3)推导出爆堆最佳分层位置距离公式,实现了爆堆自适应分层,爆堆块度分布测量精度明显提高,更接近于爆堆整体块度分布。

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徐振洋(1982-),男,博士、教授,从事爆炸力学与工程爆破方面的研究,(E-mail)

XU Zhen-yang (1982-), male, professor, mainly engaged in research on mining engineering and blasting theory and technology, (E-mail) .

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徐振洋(1982-),男,博士、教授,从事爆炸力学与工程爆破方面的研究,(E-mail)

XU Zhen-yang (1982-), male, professor, mainly engaged in research on mining engineering and blasting theory and technology, (E-mail) .

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徐振洋(1982-),男,博士、教授,从事爆炸力学与工程爆破方面的研究,(E-mail)

XU Zhen-yang (1982-), male, professor, mainly engaged in research on mining engineering and blasting theory and technology, (E-mail) .

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figureFileSmall=aj8OGcEN9R6xKS4MOcFqeA==, figureFileBig=q1+wIGjY2zuek5I3dBk1aA==, tableContent=null), ArticleFig(id=1241769356766548320, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Fig. 12, caption=Adaptive stratified image acquisition of the 300 platform burst pile in the east mining area, figureFileSmall=F4VuDSQiLrNgzHkCT3i21A==, figureFileBig=/mc+JWb7PN0IEHpUWAABvg==, tableContent=null), ArticleFig(id=1241769356888183144, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=图12, caption=东采区300平台爆堆自适应分层图像采集, figureFileSmall=F4VuDSQiLrNgzHkCT3i21A==, figureFileBig=/mc+JWb7PN0IEHpUWAABvg==, tableContent=null), ArticleFig(id=1241769356997235051, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Fig. 13, caption=Fragment size distribution statistics of blast pile E at the+300 platform in east mining area, figureFileSmall=40Fg+pkfSBkvyf2Pua6Eow==, figureFileBig=YvbyaNNJ3FWkJXUou89dgQ==, tableContent=null), ArticleFig(id=1241769357123064174, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=图13, caption=东采区300平台E爆堆块度分布统计, figureFileSmall=40Fg+pkfSBkvyf2Pua6Eow==, figureFileBig=YvbyaNNJ3FWkJXUou89dgQ==, tableContent=null), ArticleFig(id=1241769357232116084, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Fig. 14, caption=Comparison of the fragment size distribution of different blast piles in the east mining area, figureFileSmall=NnAUObW7kG/ST3G6zBLTfw==, figureFileBig=Clfc0+YSGFqqhKWMMtlJBg==, tableContent=null), ArticleFig(id=1241769357345362297, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=图14, caption=东采区不同爆堆块度分布对比, figureFileSmall=NnAUObW7kG/ST3G6zBLTfw==, figureFileBig=Clfc0+YSGFqqhKWMMtlJBg==, tableContent=null), ArticleFig(id=1241769357433442681, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 1, caption=

Input layer parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
编号炸药单耗/(kg·m-3台阶高度/m抵抗线/m孔距/m排距/m自由面坡脚/°
10.5016.54.27.54.579
20.4415.14.27.54.576
30.4415.14.27.54.580
40.4415.24.27.54.577
50.4115.34.27.54.582
60.4115.34.27.54.581
70.4115.34.27.54.576
80.4816.04.27.54.577
90.4515.34.27.54.578
100.4415.64.27.54.580
), ArticleFig(id=1241769357550883199, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表1, caption=

输入层参数

, figureFileSmall=null, figureFileBig=null, tableContent=
编号炸药单耗/(kg·m-3台阶高度/m抵抗线/m孔距/m排距/m自由面坡脚/°
10.5016.54.27.54.579
20.4415.14.27.54.576
30.4415.14.27.54.580
40.4415.24.27.54.577
50.4115.34.27.54.582
60.4115.34.27.54.581
70.4115.34.27.54.576
80.4816.04.27.54.577
90.4515.34.27.54.578
100.4415.64.27.54.580
), ArticleFig(id=1241769357643157890, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 2, caption=

Output layer parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
编号松散系数 α β
11.1171.10741.1059
21.2141.02651.2613
31.1621.07291.3125
41.1121.04901.2084
51.0731.05931.3040
61.1501.06301.2553
71.1031.07331.0479
81.1331.09731.3239
91.0531.19031.2337
101.1521.04281.1260
), ArticleFig(id=1241769357718655364, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表2, caption=

输出层参数

, figureFileSmall=null, figureFileBig=null, tableContent=
编号松散系数 α β
11.1171.10741.1059
21.2141.02651.2613
31.1621.07291.3125
41.1121.04901.2084
51.0731.05931.3040
61.1501.06301.2553
71.1031.07331.0479
81.1331.09731.3239
91.0531.19031.2337
101.1521.04281.1260
), ArticleFig(id=1241769357802541450, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 3, caption=

GA-LSSVM prediction results

, figureFileSmall=null, figureFileBig=null, tableContent=
编号松散系数 α β
真实预测相对误差/%真实预测相对误差/%真实预测相对误差/%
11.1121.2389.131.04901.12096.851.20841.25954.23
21.1031.2358.141.07331.22697.151.04791.09674.64
31.2141.3208.751.02651.10177.331.26131.31394.17
41.1501.2559.101.06301.14116.141.25531.31865.04
51.0731.1779.971.05931.13316.971.30401.37105.14
), ArticleFig(id=1241769357890621836, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表3, caption=

GA-LSSVM预测结果

, figureFileSmall=null, figureFileBig=null, tableContent=
编号松散系数 α β
真实预测相对误差/%真实预测相对误差/%真实预测相对误差/%
11.1121.2389.131.04901.12096.851.20841.25954.23
21.1031.2358.141.07331.22697.151.04791.09674.64
31.2141.3208.751.02651.10177.331.26131.31394.17
41.1501.2559.101.06301.14116.141.25531.31865.04
51.0731.1779.971.05931.13316.971.30401.37105.14
), ArticleFig(id=1241769357995479438, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 4, caption=

Maximum forward distance prediction results

, figureFileSmall=null, figureFileBig=null, tableContent=
编号爆堆实际最大前移距离/m爆堆预测最大前移距离/m相对误差/%
140.442.34.7
239.641.44.5
338.540.75.7
441.344.78.2
539.741.64.9
), ArticleFig(id=1241769358100337042, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表4, caption=

最大前移距离预测结果

, figureFileSmall=null, figureFileBig=null, tableContent=
编号爆堆实际最大前移距离/m爆堆预测最大前移距离/m相对误差/%
140.442.34.7
239.641.44.5
338.540.75.7
441.344.78.2
539.741.64.9
), ArticleFig(id=1241769358209388950, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 5, caption=

Input layer parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
台阶高度H/m孔径D/mm平均单耗q/(kg·m-3孔距S/m排距J/m超深长度LB/m堵塞长度LC/m系数A
151400.407.44.51.537
最小抵抗线W/m坡脚θ分层控制系数K单孔装药量Q/kg漏斗容积T/m3相对强度E钻孔精度标准差e/m 
4766145.441000.20 
), ArticleFig(id=1241769358305857944, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表5, caption=

输入层参数

, figureFileSmall=null, figureFileBig=null, tableContent=
台阶高度H/m孔径D/mm平均单耗q/(kg·m-3孔距S/m排距J/m超深长度LB/m堵塞长度LC/m系数A
151400.407.44.51.537
最小抵抗线W/m坡脚θ分层控制系数K单孔装药量Q/kg漏斗容积T/m3相对强度E钻孔精度标准差e/m 
4766145.441000.20 
), ArticleFig(id=1241769358419104158, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 6, caption=

Output layer parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
爆堆最大前移距离/m x50/cm x80/cm n爆堆分层距离/m爆堆分层层数
37.7632531.6913.33
), ArticleFig(id=1241769358486213023, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表6, caption=

输出层参数

, figureFileSmall=null, figureFileBig=null, tableContent=
爆堆最大前移距离/m x50/cm x80/cm n爆堆分层距离/m爆堆分层层数
37.7632531.6913.33
), ArticleFig(id=1241769358586876323, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 7, caption=

Layered image acquisition

, figureFileSmall=null, figureFileBig=null, tableContent=
编号分层图像采集层数距离/m
爆堆A313.30
爆堆B49.57
爆堆C58.65
爆堆D66.67
), ArticleFig(id=1241769358674956711, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表7, caption=

分层图像采集

, figureFileSmall=null, figureFileBig=null, tableContent=
编号分层图像采集层数距离/m
爆堆A313.30
爆堆B49.57
爆堆C58.65
爆堆D66.67
), ArticleFig(id=1241769358763037097, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 8, caption=

Optimal layer distance and maximum forward distance

, figureFileSmall=null, figureFileBig=null, tableContent=
编号最佳分层距离/m爆堆最大前移距离/m
116.1440.27
215.9239.83
319.9247.44
419.5645.12
515.1737.47
614.3536.71
720.1348.26
819.1144.31
920.6147.20
1015.4038.79
), ArticleFig(id=1241769358859506091, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表8, caption=

最佳分层距离与最大前移距离

, figureFileSmall=null, figureFileBig=null, tableContent=
编号最佳分层距离/m爆堆最大前移距离/m
116.1440.27
215.9239.83
319.9247.44
419.5645.12
515.1737.47
614.3536.71
720.1348.26
819.1144.31
920.6147.20
1015.4038.79
), ArticleFig(id=1241769358918226351, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 9, caption=

Input layer parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
台阶高度H/m平均单耗q/(kg·m-3排距J/m孔距S/m最小抵抗线W/m坡脚θ
15.60.484.57.44.279
), ArticleFig(id=1241769358993723826, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表9, caption=

输入层参数

, figureFileSmall=null, figureFileBig=null, tableContent=
台阶高度H/m平均单耗q/(kg·m-3排距J/m孔距S/m最小抵抗线W/m坡脚θ
15.60.484.57.44.279
), ArticleFig(id=1241769359106970037, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=EN, label=Table 10, caption=

Output layer parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
爆堆最大前移距离/m爆堆最佳分层位置/m
44.218.59
), ArticleFig(id=1241769359207633337, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241769332183732462, language=CN, label=表10, caption=

输出层参数

, figureFileSmall=null, figureFileBig=null, tableContent=
爆堆最大前移距离/m爆堆最佳分层位置/m
44.218.59
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爆堆自适应分层的块度空间分布测量方法研究
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徐振洋 1, 2 , 郇宝乾 1 , 李萍丰 3 , 王雪松 4 , 周成平 3
爆破 | 理论与技术探索 2024,41(1): 27-36
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爆破 | 理论与技术探索 2024, 41(1): 27-36
爆堆自适应分层的块度空间分布测量方法研究
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徐振洋1, 2 , 郇宝乾1, 李萍丰3, 王雪松4, 周成平3
作者信息
  • 1.辽宁科技大学 矿业工程学院,鞍山 114051
  • 2.辽宁省金属矿产资源绿色开采工程研究中心,鞍山 114051
  • 3.宏大爆破工程集团有限责任公司,广州 510623
  • 4.沈阳工业大学 建筑与土木学院,沈阳 110870
  • 徐振洋(1982-),男,博士、教授,从事爆炸力学与工程爆破方面的研究,(E-mail)

    XU Zhen-yang (1982-), male, professor, mainly engaged in research on mining engineering and blasting theory and technology, (E-mail) .

Study on Measurement Method of Fragment Spatial Distribution by Adaptive Stratification of Blasting Pile
Zhen-yang XU1, 2 , Bao-qian HUAN1, Ping-feng LI3, Xue-song WANG4, Cheng-ping ZHOU3
Affiliations
  • 1.School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China
  • 2.Liaoning Engineering Technology Research Center for Efficient Mining and Utilization of Metal Mineral Resources, Anshan 114051, China
  • 3.Hongda Demolition Engineering Group Co., Guangzhou 510623, China
  • 4.School of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang 110870, China
出版时间: 2024-03-01 doi: 10.3963/j.issn.1001-487X.2024.01.005
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爆堆块度分布特征是评价爆破效果的重要指标,针对目前爆堆块度直接与间接法测量方法的不足,提出了一种爆堆自适应分层的块度空间分布测量方法,该方法使用GA-LSSVM模型来预测Weilbull函数的形状参数αβ,并设置多个预测点,生成三维爆堆形态预测曲线。同时对Kuz-Ram块度预测模型参数进行转换和融合,建立了爆堆分层距离预测模型,并将其导入Weilbull-GA-LSSVM爆堆形态预测模型,实现爆堆自动分层。通过现场应用,不断优化分层设计,探究爆堆最佳分层位置,达到爆堆自适应分层的效果。以广东省大排矿山为工程依托开展现场试验,结果表明:(1)Weibull-GA-LSSVM模型能够准确预测爆堆形态,其中爆堆最大前移距离预测结果的平均相对误差仅为5.6%,松散系数预测结果的相对误差多数在9%左右,表现出良好的稳定性。(2)爆堆自动分层模型能够在爆前合理地输出分层距离以及分层层数,保证了爆后现场爆堆的铲装效率。(3)推导出爆堆最佳分层位置距离公式,实现了爆堆自适应分层,爆堆块度分布测量精度明显提高,更接近于爆堆整体块度分布。

爆堆  /  自适应分层  /  块度分布  /  分层距离  /  爆破效果

The distribution characteristics of blasting pile is an important index indicator to evaluate blasting effect. In view of the inadequacy of the current direct and indirect methods of measuring fragment size of the blast pile, a spatial distribution measurement method for adaptive stratification of the blast pile is proposed. It uses the GA-LSSVM model to predict the shape parameters α and β of the Weibull function and sets multiple prediction points to predict the three-dimensional blasting pile morphology. By converting and fusing the parameters of the Kuz-Ram fragment prediction model, a distance prediction model of blast pile stratification is established and applied to the Weibull-GA-LSSVM model to achieve an automatic stratification of the blast pile. Through field application, the stratification design is continuously optimized for the best stratification position to realize the adaptive stratification. The results show that: (1) the Weibull-GA-LSSVM model can accurately predict the morphology of the blast pile with a good stability that the average relative error of the prediction results of the maximum forward distance of the blast pile is only 5.6% and the relative error of the prediction results of the looseness coefficient is mostly around 9%. (2) The Kuz-Ram-based blast pile stratification model can reasonably output the layer distance and number before blast, which ensures the shoveling efficiency after blast. (3) The optimal layer distance formula is derived to achieve the adaptive stratification of the blast pile, and the measurement accuracy of the fragment size distribution of the blast pile is significantly improved, which is closer to the overall fragment size distribution.

blast pile  /  adaptive stratification  /  fragment size distribution  /  layer distance  /  blasting effect
徐振洋, 郇宝乾, 李萍丰, 王雪松, 周成平. 爆堆自适应分层的块度空间分布测量方法研究. 爆破, 2024 , 41 (1) : 27 -36 . DOI: 10.3963/j.issn.1001-487X.2024.01.005
Zhen-yang XU, Bao-qian HUAN, Ping-feng LI, Xue-song WANG, Cheng-ping ZHOU. Study on Measurement Method of Fragment Spatial Distribution by Adaptive Stratification of Blasting Pile[J]. Blasting, 2024 , 41 (1) : 27 -36 . DOI: 10.3963/j.issn.1001-487X.2024.01.005
爆堆块度分布是评价爆破效果的重要指标[1]。目前,爆堆块度分布测量方法主要分为直接法和间接法[2]。直接法由于其工作量较大且耗时耗力,在工程实际应用中被逐渐淘汰。间接法分为相关数据测量法[3]、经验分布函数法[4]、数值模拟法[5]、摄影图像处理法[6]。相关数据测量法是通过后期工序指标推测块度分布情况,推算结果并不可靠。经验分布函数法是通过现有理论与试验建立爆堆块度分布信息与相关影响因素的函数关系[7]。研究发现,R-R分布比重越大,越近似于爆堆实际分布规律[8]。数值模拟法是利用计算机建立爆破物理过程的瞬态数学模型[9]。黄梦龙等通过拟合地质影响系数Kα,成功模拟了爆破后爆堆块度分布情况[10]。冯春等模拟了露天矿爆堆形成的全过程[11],证明了数值模拟的可行性。摄影图像处理法分为二维图像和三维图像[12]。姬付全等利用ACE+CLAHE算法对隧道爆破块度图像进行处理[13],能够在光照不均的情况下识别隧道爆堆块度。江松等在深度学习算法中融入点渲染多分支的方法[14],解决了模型上采样时丢失语义信息的问题,提升了爆堆块体的分割精度。刘强等利用3DPCFM统计方法预测了三维点云爆堆的表面块度分布[15]。谢博等利用区域生长的方法实现了点云岩块轮廓自动识别[16]。不难看出,以上对爆堆块度的研究分析多为爆堆表观层,难以准确表征爆堆整体的块度分布情况。
因此,提出了爆堆自适应分层的块度空间测量方法,该方法使用GA-LSSVM模型来预测Weilbull函数的形状参数αβ,并设置多个预测点,进而预测三维爆堆形态。通过将Kuz-Ram块度预测模型参数进行转换和融合,建立了爆堆分层距离预测模型,并将其应用于Weilbull-GA-LSSVM爆堆形态预测模型,实现爆堆自动分层。通过现场应用,不断优化分层设计,探究爆堆最佳分层位置,达到爆堆自适应分层的效果。并以广东省肇庆市大排矿为工程依托,开展现场试验,验证了爆堆自适应分层的块度空间测量方法准确性。
大排矿山矿区位于广东省肇庆市封开县长岗镇、罗董镇,矿山岩性主要为石灰石与花岗岩,范围海拔标高最低为100 m,标高最高为大排山山顶366.2 m,设计最低开采标高为+30 m,台阶高度为15 m,计划每年生产骨料2000万t,机制砂1300万t,合计每年生产成品总量为3300万t。图1为大排矿山施工总平面图,矿山开采分为东西两个采区,设计东西采区采用独立的运输矿道,采用公路开拓—汽车运输的开拓方式,运矿设备为载重不小于70 t的电动矿车,达产期计划需配备73台载重70 t的矿车。根据矿物料运输距离及岩性,矿山配套的挖装设备为小松PC500、PC850。
首先采用Weibull-GA-LSSVM模型预测爆堆形态,获取爆堆最大前移距离,再基于Kuz-Ram分层距离预测模型,计算爆堆分层距离,两者相结合,输出爆堆分层层数,实现爆堆自动分层,如图2所示。
Weibull函数是一种连续性分布模型,由于Weibull分布曲线近似于爆堆剖面曲线[17],因此被广泛运用于爆堆形态预测。
爆破试验表明,爆破后岩体质量等于爆破前岩体质量
式中:ρrρra分别为爆破前、后的岩石密度;hx)是爆堆高度,m;A0是台阶剖面面积,m2Lm是最远抛掷距离,m。
将式(1)无量纲化
式中:ξ为岩石松散系数。
HX)为Weibull分布的概率密度函数
式中:αβ为控制曲线形状的参数,β>1。若αβ选择合理,HX)在Lm处变化很小。
因此,为了更加准确地预测爆堆形态[17],必须有效预测Weibull函数中的两个参数αβ,进而修正Weibull函数形态,使其更加接近于爆堆真实形态。
LSSVM算法是一种全新的回归计算方法[18,19],通过引入GA优化算法,我们可以有效地改善LSSVM模型的收敛速度,并且通过调整惩罚参数c和核函数g,可以显著提升模型的学习和泛化能力,能够更准确地预测Weibull函数中的两个参数αβ图3为优化过程。
采用Matlab的2021(a)仿真平台建立GA-LSSVM模型,共选取30组肇庆大排矿现场爆破监测数据为数据集,按2∶1的比例分为训练集和测试集。其中,爆堆松散系数以及Weibull函数中的形状参数α、β作为模型的输出层参数,而炸药单耗、台阶高度、抵抗线、孔距、排距、自由面坡脚作为输入层参数[20]。限于篇幅,数据见表1表2
表3所示为GA-LSSVM模型的预测结果,α值的相对误差低于8%,β值的相对误差变化不大,在4%~6%之间,而松散系数的相对误差则稳定在10%以下,多数在9%左右,预测结果达到了预期的要求。
现将预测输出的αβ值导入Weibull分布函数,设置多个预测点,生成三维爆堆形态预测曲线,输出爆堆最大前移距离,如图4所示,从而实现爆堆形态可视化。
图5所示为无人机测量现场爆堆最大前移距离的过程[21]表4所示为爆堆最大前移距离预测结果,平均相对误差仅为5.6%,这是因为在爆破过程中,破碎岩石之间的碰撞会产生阻力,从而使得爆堆预测距离要超出爆堆实际测量距离[6]
为了保证分层距离预测的合理性,需要综合考虑现场爆堆块体的大小以及场铲装设备型号参数。
多年来,Kuz-Ram爆破块度预测模型一直被广泛地运用于矿业中[22],模型表达式如下
式中:x50为平均粒径,cm;x0特定筛下累积率为63.2%的块体粒径值,cm;x为特征粒径,cm;m为孔距与抵抗线之比;q为炸药单位耗药量,kg/m3Q为单孔药量,kg;W为最小抵抗线,m;D为炮孔直径,mm;e为钻孔精度标准差,m;H为台阶高度,m;E为炸药的相对重量强度,梯恩梯为115,一般工业炸药取100;L0开挖线以上的装药长度,m;A为岩石系数,与岩体裂隙、结构面发育程度有关;R为筛下累计率;n为不均匀系数。
由文献[23]可知x50=0.6931/n,利用Kuz-Ram模型的筛下累计率公式推导x80,推导过程如下
结合现场实际生产情况,选取挖机斗容T作为模型参数,最终得出分层距离预测公式如下
式中:M表示分层距离,m;k为分层距离控制系数,k>1且为正整数,k越大爆堆分层的层数越少;x80为特征粒径,即为特定筛下累积率为80%的块体粒径,m;T为不同型号挖机漏斗的斗容,m3N为爆堆最大前移距离,m。
结合Weibull-GA-LSSVM爆堆形态预测模型和分层距离预测模型,建立爆堆自动分层模型。爆前输入模型所需爆破设计参数,预测生成三维爆堆形态,如图6所示,输出爆堆最大前移距离、爆堆分层距离以及爆堆分层层数等参数。同时,在爆堆自动分层模型中增加了手动自定义调节功能,在等分的基础上调整层与层之间的距离,也可以调整分层剖面的倾斜角度,以便于工作人员根据实际情况进行调整。
为了验证爆堆自动分层方法的可行性,首先对大排矿东采区炮区A进行爆堆自动分层处理,表56所示分别为模型输入层及输出层参数,不难看出,爆堆自动分层模型能够合理地输出分层距离与分层层数。然后,又连续对东采取的BCD三个炮区进行爆堆的自动分层处理,最终四次爆破产生的爆堆经自动分层后采集的图像如表7所示,结果表明爆堆自动分层模型具有良好的稳定性。
通过MATLAB开发了基于深度学习U-Net分割模型的爆堆粒度分布检测系统[24,25],如图7所示。此外,调用相关命令,可以生成或读取“.xls”文件,能够对爆堆级配效果进行全面、准确的评估。
图8展示了四次爆破后爆堆块度空间分布的统计结果,其中每一层的块度分布以小写字母标识,而整体的块度分布则以大写字母标识,不同颜色的区域代表着不同的爆堆块体最长径范围。经过分析,我们发现,在四次爆破后,爆堆的大块率显著下降。这是因为前期的爆破活动对岩体造成了严重的损伤[26]。对于单一爆堆而言,表面的大块数量最多,根部次之,中部的大块数量最少。
图8中可以看出,a2的块度分布更接近Ab3更接近Bc4更接近Cd5更接近D,这表明爆堆的中部靠左区域,块体分布情况接近于爆堆整体的块度分布情况。为了更好地探究爆堆分层的最佳位置,我们随机对10个爆堆进行分层块度分析,结果表明,中部靠左区域的块体分布情况确实接近于爆堆整体的块度分布,如图9所示。
表8提供了十个爆堆中最接近爆堆整体块度分布的分层中点位置及对应的爆堆最大前移距离。
使用MATLAB进行拟合,图10展示了最终的拟合结果,从而得出最佳的分层位置公式,如下
式中:y代表最佳分层距离,m;x表示爆堆最大前移距离,m;从拟合结果来看,各离散点的曲线误差不超过0.5 m,表明效果良好,可以忽略不计,能够满足现场爆堆挖机铲装的实际应用要求。
现将爆堆最佳分层距离公式导入爆堆自动分层模型,建立爆堆自适应分层模型。利用大排矿东采区300平台E炮区对爆堆自适应分层的块度空间测量方法进行现场验证,爆前输入表9所示模型参数,图11所示为生成的爆堆自适应分层三维模型,预测结果如表10所示。
按照表9模型的预测结果,对东采区300平台E爆堆的最佳分层位置进行划定,然后使用挖机进行挖掘,如图12所示,最后采集爆堆最佳分层位置的剖面图对爆堆块度分布情况进行测量。
图13所示为东采区300平台爆堆块度分布情况,与爆堆表面块度分布情况相比,最佳分层位置的块度分布级配曲线更接近于爆堆整体块度分布级配曲线,表面块度分布曲线处于低位,主要是因为爆堆表面的大块居多,中小粒径的块体个数占比较少[11]
为了进一步检验爆堆自适应分层测量方法的精确度,我们连续对东采区的FGHI四个爆堆进行了块度分布统计,具体结果见图14。从这个结果中,我们可以明显看出,爆堆自适应分层的块度空间分布测量方法,能够更精确地统计爆堆的块度分布状况。
(1)建立爆堆自动分层模型,爆堆最大前移距离预测结果的平均相对误差仅为5.6%,能够在爆前合理地输出爆堆分层距离及爆堆分层层数,表现出良好的稳定性,实现了对爆堆块度的分层量化分析,为爆堆自适应分层奠定基础。
(2)推导出爆堆最佳分层位置距离公式,建立了爆堆自适应分层模型,并通过现场试验进行了验证,爆堆自适应分层的块度空间分布测量方法的测量精度更高,更接近于爆堆整体的块度空间分布情况,对爆破效果的评价具有一定的现场应用价值。
  • 国家自然科学基金资助项目(51974187)
  • 辽宁省教育厅重点项目(LJKZ0282)
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2024年第41卷第1期
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doi: 10.3963/j.issn.1001-487X.2024.01.005
  • 接收时间:2023-08-24
  • 首发时间:2026-03-20
  • 出版时间:2024-03-01
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  • 收稿日期:2023-08-24
基金
National Natural Science Foundation of China(51974187)
国家自然科学基金资助项目(51974187)
Key Projects of Liaoning Provincial Department of Education(LJKZ0282)
辽宁省教育厅重点项目(LJKZ0282)
作者信息
    1.辽宁科技大学 矿业工程学院,鞍山 114051
    2.辽宁省金属矿产资源绿色开采工程研究中心,鞍山 114051
    3.宏大爆破工程集团有限责任公司,广州 510623
    4.沈阳工业大学 建筑与土木学院,沈阳 110870
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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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