Article(id=1241089938737918294, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241089933696364783, articleNumber=null, orderNo=null, doi=10.3963/j.issn.1001-487X.2023.02.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1674748800000, receivedDateStr=2023-01-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773828499731, onlineDateStr=2026-03-18, pubDate=1685548800000, pubDateStr=2023-06-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773828499731, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773828499731, creator=13701087609, updateTime=1773828499731, updator=13701087609, issue=Issue{id=1241089933696364783, tenantId=1146029695717560320, journalId=1240670690148397066, year='2023', volume='40', issue='2', pageStart='1', pageEnd='229', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773828498529, creator=13701087609, updateTime=1773828588505, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241090311141782020, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241089933696364783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241090311141782021, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241089933696364783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=80, endPage=87, ext={EN=ArticleExt(id=1241089938997965157, articleId=1241089938737918294, tenantId=1146029695717560320, journalId=1240670690148397066, language=EN, title=Study on Blasting Characteristics of Rock Mass and Prediction of Rock Fragment Size by Bench Blasting in Tunnel Excavation, columnId=1240702072862069231, journalTitle=Blasting, columnName=BLASTING IN ORE AND ROCK, runingTitle=null, highlight=null, articleAbstract=

The conclusions including: the Kuznetsov and kansake models are still applicable to the prediction of unit explosive consumption in bench blasting tunnel engineering, and the Kuznetsov model considers the influence of rock mass characteristics, which is more practical than the kansake model. In addition, the Kuznetsov model can predict the average blasting fragment as the basis of the distribution model of blasting fragment. Compared with the lower bench, the upper bench has higher single blasting consumption, higher content of fine particles and smaller fragments. The Kuz-Ram model has a good prediction effect for the small blasting fragments that below the average value. For the large blasting fragments that above the average value, the KCO model has a better prediction effect, and it has more accurate for the prediction of the largest blasting fragment. This paper analyzes the applicable conditions and scope of the prediction model for rock fragment, which provides a basis for the unit explosive consumption and fragment distribution of tunnel blasting. The methods for predicting the specific consumption of explosives and the fragmentation of rock mass during blasting originated from open-pit blasting. However, the stress state of the rock mass in stepped tunnels is different from that in open-pit mines, so it is unknown whether the above prediction methods are applicable to stepped tunnel blasting. Based on statistical data from the bench blasting in the Tianjiangli tunnel, several commonly used calculation methods in open-pit mines were used to predict the specific consumption of explosives and the distribution of fragments, and the predicted results were compared with the actual measurements. The results show that the Kuznetsov model and the Kansake model are still applicable to predicting the specific consumption of explosives in stepped tunnel blasting, and the Kuznetsov model takes into account the influence of rock mass characteristics, making it more practical than the Kansake model. In addition, the Kuznetsov model can predict the average value of the blasting fragmentation and serve as a basis for the fragmentation distribution model. The specific consumption of explosives is higher on the upper bench, resulting in a higher content of fine particles and smaller overall rock mass after blasting, while the specific consumption is lower on the lower step, resulting in a lower content of fine particles and a larger overall rock mass after blasting. The Kuz-Ram model is better in predicting the blasting fragmentation of small rock masses with a block size below the average value, while the KCO model is better for predicting the blasting fragmentation of large rock masses with a block size above the average value, and the KCO model can accurately predict the maximum blasting block. This article analyzes the applicable conditions and scope of the prediction models and provides a basis for the specific consumption and fragmentation distribution in tunnel blasting.

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爆破炸药单耗及岩体爆破碎片块度预测方法均起源于露天矿爆破,台阶法隧道上下台阶岩体应力状态与露天矿大不相同,以上预测方法是否适用于台阶法隧道爆破尚未可知。根据天江里隧道台阶法爆破统计数据,采用几种露天矿常用计算方法预测了上下台阶炸药单耗及碎片分布,并与实测结果进行了对比,得出如下结论:Kuznetsov模型和Kansake模型仍然适用于台阶法隧道工程炸药单耗的预测,且Kuznetsov模型考虑了岩体特性的影响,比Kansake模型更加实用;此外,Kuznetsov模型可以预测爆破碎片平均值,作为爆破碎片分布模型的基础。上台阶爆破单耗高,岩体爆破后细颗粒含量高、整体块体小;下台阶爆破单耗低,岩体爆破后细颗粒含量低、整体块度大。对于块度在平均值以下的小块体爆破碎片,采用Kuz-Ram模型预测效果较好;对于块度在平均值以上的大块体爆破碎片,采用KCO模型预测效果较好,且KCO模型对最大爆破块体的预测较准确。分析了预测模型的适用条件及范围,为隧道爆破炸药单耗和块度分布提供了依据。

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董武俊(1973-),男,高级工程师,主要从事隧道工程的工作与研究,(E-mail)

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董武俊(1973-),男,高级工程师,主要从事隧道工程的工作与研究,(E-mail)

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董武俊(1973-),男,高级工程师,主要从事隧道工程的工作与研究,(E-mail)

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label=Table 1, caption=

Blasting parameters of upper bench of tunnel

, figureFileSmall=null, figureFileBig=null, tableContent=
炮孔类别段别炮孔数量/个炮孔深度/m装药集中度/(kg·m-1装药量/kg
单孔总量
开槽孔142.20.7001.546.16
扩槽孔542.00.7001.405.60
辅助孔762.00.6001.207.20
辅助孔862.00.6001.207.20
崩落孔9172.00.5001.0017.00
轮廓孔10252.00.3000.6015.00
底孔11132.00.6751.3517.55
合计 75   75.71
), ArticleFig(id=1241089954890182850, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241089938737918294, language=CN, label=表1, caption=

隧道上台阶爆破参数

, figureFileSmall=null, figureFileBig=null, tableContent=
炮孔类别段别炮孔数量/个炮孔深度/m装药集中度/(kg·m-1装药量/kg
单孔总量
开槽孔142.20.7001.546.16
扩槽孔542.00.7001.405.60
辅助孔762.00.6001.207.20
辅助孔862.00.6001.207.20
崩落孔9172.00.5001.0017.00
轮廓孔10252.00.3000.6015.00
底孔11132.00.6751.3517.55
合计 75   75.71
), ArticleFig(id=1241089954999234759, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241089938737918294, language=EN, label=Table 2, caption=

Blasting parameters of lower bench of tunnel

, figureFileSmall=null, figureFileBig=null, tableContent=
炮孔类别段别炮孔数量/个炮孔深度/m装药集中度/(kg·m-1装药量/kg
单孔总量
第一排1320.5001.203.60
第二排2520.5501.105.50
第三排3720.5501.107.70
第四排4920.5501.109.90
第五排5820.5501.108.80
第六排6620.5001.006.00
第七排7620.5001.006.00
崩落孔91220.5001.0012.00
轮廓孔10820.3000.604.80
底孔111620.6751.3521.60
合计 75   75.71
), ArticleFig(id=1241089955099898062, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241089938737918294, language=CN, label=表2, caption=

隧道下台阶爆破参数

, figureFileSmall=null, figureFileBig=null, tableContent=
炮孔类别段别炮孔数量/个炮孔深度/m装药集中度/(kg·m-1装药量/kg
单孔总量
第一排1320.5001.203.60
第二排2520.5501.105.50
第三排3720.5501.107.70
第四排4920.5501.109.90
第五排5820.5501.108.80
第六排6620.5001.006.00
第七排7620.5001.006.00
崩落孔91220.5001.0012.00
轮廓孔10820.3000.604.80
底孔111620.6751.3521.60
合计 75   75.71
), ArticleFig(id=1241089955204755670, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241089938737918294, language=EN, label=Table 3, caption=

Predicted and measured explosive specific consumption

, figureFileSmall=null, figureFileBig=null, tableContent=
计算模型上台阶下台阶
炸药总量/kg
75.71
爆破体积/m3
71.82
最小抵抗线/m
0.8
孔距/m
0.7
排距/m
0.8
X50/cm
16
炸药总量/kg
85.9
爆破体积/m3
114.56
最小抵抗线/m
0.9
孔距/m
0.8
排距/m
1
X50/cm
21
Kuznetsov模型1.180.87
Li模型2.081.56
Kansake模型1.020.71
实测值1.050.75
), ArticleFig(id=1241089955318001885, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241089938737918294, language=CN, label=表3, caption=

预测与实测的炸药单耗

, figureFileSmall=null, figureFileBig=null, tableContent=
计算模型上台阶下台阶
炸药总量/kg
75.71
爆破体积/m3
71.82
最小抵抗线/m
0.8
孔距/m
0.7
排距/m
0.8
X50/cm
16
炸药总量/kg
85.9
爆破体积/m3
114.56
最小抵抗线/m
0.9
孔距/m
0.8
排距/m
1
X50/cm
21
Kuznetsov模型1.180.87
Li模型2.081.56
Kansake模型1.020.71
实测值1.050.75
), ArticleFig(id=1241089955557077218, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241089938737918294, language=EN, label=Table 4, caption=

Calculation parameters and results of uniformity index

, figureFileSmall=null, figureFileBig=null, tableContent=
爆破位置计算参数及结果
B/DS/BW/BABSLB-LC/LL/HnX50Xmaxb
上台阶0.0190.020.860.580.831.050.160.72.15
下台阶0.0210.020.890.530.810.950.210.81.76
), ArticleFig(id=1241089955619991785, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241089938737918294, language=CN, label=表4, caption=

Kuz-Ram和KCO模型参数

, figureFileSmall=null, figureFileBig=null, tableContent=
爆破位置计算参数及结果
B/DS/BW/BABSLB-LC/LL/HnX50Xmaxb
上台阶0.0190.020.860.580.831.050.160.72.15
下台阶0.0210.020.890.530.810.950.210.81.76
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台阶法隧道岩体爆破特性研究及岩石碎片块度预测
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董武俊
爆破 | 矿岩爆破 2023,40(2): 80-87
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爆破 | 矿岩爆破 2023, 40(2): 80-87
台阶法隧道岩体爆破特性研究及岩石碎片块度预测
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董武俊
作者信息
  • 中铁十一局集团有限公司,武汉 430064
  • 董武俊(1973-),男,高级工程师,主要从事隧道工程的工作与研究,(E-mail)

Study on Blasting Characteristics of Rock Mass and Prediction of Rock Fragment Size by Bench Blasting in Tunnel Excavation
Wu-jun DONG
Affiliations
  • China Railway 11th Bureau Group Co, Ltd, Wuhan 430064, China
出版时间: 2023-06-01 doi: 10.3963/j.issn.1001-487X.2023.02.012
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爆破炸药单耗及岩体爆破碎片块度预测方法均起源于露天矿爆破,台阶法隧道上下台阶岩体应力状态与露天矿大不相同,以上预测方法是否适用于台阶法隧道爆破尚未可知。根据天江里隧道台阶法爆破统计数据,采用几种露天矿常用计算方法预测了上下台阶炸药单耗及碎片分布,并与实测结果进行了对比,得出如下结论:Kuznetsov模型和Kansake模型仍然适用于台阶法隧道工程炸药单耗的预测,且Kuznetsov模型考虑了岩体特性的影响,比Kansake模型更加实用;此外,Kuznetsov模型可以预测爆破碎片平均值,作为爆破碎片分布模型的基础。上台阶爆破单耗高,岩体爆破后细颗粒含量高、整体块体小;下台阶爆破单耗低,岩体爆破后细颗粒含量低、整体块度大。对于块度在平均值以下的小块体爆破碎片,采用Kuz-Ram模型预测效果较好;对于块度在平均值以上的大块体爆破碎片,采用KCO模型预测效果较好,且KCO模型对最大爆破块体的预测较准确。分析了预测模型的适用条件及范围,为隧道爆破炸药单耗和块度分布提供了依据。

爆破  /  炸药单耗  /  碎片分布  /  模型  /  块度

The conclusions including: the Kuznetsov and kansake models are still applicable to the prediction of unit explosive consumption in bench blasting tunnel engineering, and the Kuznetsov model considers the influence of rock mass characteristics, which is more practical than the kansake model. In addition, the Kuznetsov model can predict the average blasting fragment as the basis of the distribution model of blasting fragment. Compared with the lower bench, the upper bench has higher single blasting consumption, higher content of fine particles and smaller fragments. The Kuz-Ram model has a good prediction effect for the small blasting fragments that below the average value. For the large blasting fragments that above the average value, the KCO model has a better prediction effect, and it has more accurate for the prediction of the largest blasting fragment. This paper analyzes the applicable conditions and scope of the prediction model for rock fragment, which provides a basis for the unit explosive consumption and fragment distribution of tunnel blasting. The methods for predicting the specific consumption of explosives and the fragmentation of rock mass during blasting originated from open-pit blasting. However, the stress state of the rock mass in stepped tunnels is different from that in open-pit mines, so it is unknown whether the above prediction methods are applicable to stepped tunnel blasting. Based on statistical data from the bench blasting in the Tianjiangli tunnel, several commonly used calculation methods in open-pit mines were used to predict the specific consumption of explosives and the distribution of fragments, and the predicted results were compared with the actual measurements. The results show that the Kuznetsov model and the Kansake model are still applicable to predicting the specific consumption of explosives in stepped tunnel blasting, and the Kuznetsov model takes into account the influence of rock mass characteristics, making it more practical than the Kansake model. In addition, the Kuznetsov model can predict the average value of the blasting fragmentation and serve as a basis for the fragmentation distribution model. The specific consumption of explosives is higher on the upper bench, resulting in a higher content of fine particles and smaller overall rock mass after blasting, while the specific consumption is lower on the lower step, resulting in a lower content of fine particles and a larger overall rock mass after blasting. The Kuz-Ram model is better in predicting the blasting fragmentation of small rock masses with a block size below the average value, while the KCO model is better for predicting the blasting fragmentation of large rock masses with a block size above the average value, and the KCO model can accurately predict the maximum blasting block. This article analyzes the applicable conditions and scope of the prediction models and provides a basis for the specific consumption and fragmentation distribution in tunnel blasting.

blasting  /  specific consumption of explosives  /  fragment distribution  /  model  /  block size
董武俊. 台阶法隧道岩体爆破特性研究及岩石碎片块度预测. 爆破, 2023 , 40 (2) : 80 -87 . DOI: 10.3963/j.issn.1001-487X.2023.02.012
Wu-jun DONG. Study on Blasting Characteristics of Rock Mass and Prediction of Rock Fragment Size by Bench Blasting in Tunnel Excavation[J]. Blasting, 2023 , 40 (2) : 80 -87 . DOI: 10.3963/j.issn.1001-487X.2023.02.012
钻孔和爆破是地下工程最有效的掘进方法,用于破碎原位岩体,以便经济高效地装载、运输和破碎岩体至所需尺寸[1,2]。当挖掘过程中获取的爆破碎片不符合要求时,它将迫使重型机械进行二次破碎,导致整个装载和运输周期都会受到影响,造成较高的成本和较低的生产率。影响岩石破碎的因素主要分为两类:一是可控因素,如炸药单耗、炸药种类;二是非可控因素,如地质条件[3-5]。基于这些因素,学者们开发了多种方法评估爆破产生的岩石碎片,其中最常用的是Kuz-Ram模型[6,7]。Kuz-Ram模型是许多露天矿岩石碎裂研究的基础[8-12]。通常,学者们将预测岩石破碎的新技术和模拟结果与Kuz-Ram模型进行比较[5,13-17]。目前,已知的爆破碎片分布模型都是根据露天爆破收集的数据开发的,露天爆破是对具有两个或三个自由面的大型台阶进行爆破。然而,在隧道中进行的爆破作业比台阶爆破复杂得多,爆破效果也存在很大的差异[18,19]。对于全断面隧道爆破,爆破前只存在一个自由面;对于台阶法隧道爆破,上台阶爆破前只存在一个自由面,下台阶爆破前只存在两个自由面。此外,对于隧道掌子面,爆破孔布设方式因地制宜,并没有露天爆破那么规则与统一,开槽孔、周边孔和底孔的排距和最小抵抗线可能完全不同[20-22]。因此,露天爆破炸药单耗预测模型、岩体爆破碎片分布模型在隧道爆破中的适用性尚未可知,有必要进行研究与分析。
在本研究中,首先尝试采用3种露天矿计算模型预测隧道上下台阶炸药单耗,以判定该模型是否可以用于地下环境。其次在该模型的基础上,采用两种岩体爆破碎片分布模型对隧道爆破碎片进行了预测,通过将预测结果与实测结果进行比较,提出了预测模型的适用条件及范围,为隧道爆破块度分布提供依据。
Agyei总结对比了露天矿常用的爆破炸药单耗预测模型[23],认为Kuznetsov模型、Li模型和Kansake模型具有简单实用的特点,它们考虑了钻孔几何、爆破参数及岩体特性,作为本次隧道爆破炸药单耗的预测模型。
Kuznetsov提出了爆破岩体平均破碎尺寸与所需炸药量之间的关系[24],作为岩石类型的函数,表示如下
式中:X50为爆破岩堆平均块度,cm;A为岩体系数,中硬岩A=7;有裂隙的硬岩A=10,无裂隙硬岩A=13;V为每个孔的岩石破碎体积,m3Qe为每个孔的TNT当量炸药质量,kg。
Cunningham对该方程进行了修正[25],表示如下
式中:q为炸药单耗,kg/m3Q单孔装药量,kg;E为相对重量爆破能量,铵油炸药E=100,TNT炸药E=115。
对于隧道爆破,岩体系数A可以通过现场试验获取;单孔装药量Q可取隧道掌子面爆破孔装药量平均值;平均碎片尺寸X50通过实测获取。因此,炸药单耗q计算表达式如下
Kuznetsov模型是为露天爆炸而开发的,在地下工程爆破情况下,预期结果可能会有所不同。Kuznetsov模型并未涵盖爆破的所有方面,例如没有考虑孔间爆破延时对岩石破碎有影响。
Li等人根据数值模拟和实验数据分析[26],提出了一个关于炸药单耗和平均碎片尺寸(X50)的经验公式,如式(4)所示。
式中:d50为岩体爆破碎片平均尺寸q为爆破单位体积岩体所消耗的炸药量,kg/m3a为孔距,m;b为排距,m;d为爆破孔直径,m。
Li模型以钻孔几何参数为主,兼顾了炸药单耗,没有考虑岩体特性,计算公式比较简单。
炸药单耗也可以按磅/立方码计算[27],如公式(6)所示
式中:PC为单孔装药长度,英尺;pe为炸药密度,g/cm3d为爆破孔直径,英寸;B为最小抵抗线长度,英尺;S为孔距,英尺;H为孔深或台阶高度,英尺。
该模型对爆破断面、钻孔几何参数及炸药参数考虑比较全面,但是忽略了岩体特性,无法预测爆破后的岩体碎片尺寸。
对于台阶法隧道爆破,由于上台阶和下台阶的自由面个数与受力状态的不同,爆破岩石碎片分布也会存在差异,采用两种常用的爆破岩石碎片分布模型进行研究。
Rammler使用以下方程式定义爆破岩堆块度大小及其分布[28],据此划分爆破块体的颗粒累计曲线。
式中:x为筛网尺寸,Rx为通过x尺寸筛网的岩体碎屑累积含量,%;n为均匀性指数,均匀性指数的典型范围为0.8~2.2,0.8表示岩堆不均匀,2.2表示石堆的大部分碎片接近平均碎片大小。
XC为特征尺寸,定义为通过63.2%的颗粒对应的尺寸,cm,可通过以下公式进行计算
通过利用Kuznetsov模型,将X=X50Rx=0.5代入上式,从平均粒径计算出特征粒径
进而,可以得到爆破碎片累积分布函数
接下来,只要知道n值,就可以使用Rosin-Rammler方程绘制爆破岩体碎片累计曲线。
Cunningham通过现场试验开发了该表达[29],它将钻爆孔的所有几何参数关联起来,如下所示
式中:B为最小抵抗线;S为孔距;D为钻孔直径,m;W为钻孔偏差;L为总装药长度;H为台阶高度。
Ouchterlony指出[30],当爆破孔被引爆时,岩石破裂发生在两个不同的应力区:压缩应力区和拉伸应力区,Kuz-Ram模型不足以识别两个应力区对应的细颗粒和粗颗粒组分,提出了一种新方法,称其为KCO模型。在该模型中,平均碎片大小X50和均匀性指数n由Cunningham提出的方程计算[25]。Rosin Ramler定义的粒度分布的函数现在被Swebrec取代。
与Rosin-Rammler方程一样,Swebrec模型使用x50的50%作为中心参数,但增加了xmax的最大极限值,第三个参数b是定义曲线波动的计算参数[27],称为三参数碎片尺寸分布模型。
式中:Pxx尺寸筛网的岩体碎片的百分比;xmax可取最小抵抗线与孔距中较小值。
新的Swebrec模型有三个大参数,可以很好地拟合不同类型的碎片数据,对于两到三个数量级的各种碎片大小,相关系数至少为0.997或更好(R2>0.995)。
天江里隧道为一座左、右线分离的双向四车道高速公路隧道,隧道主洞设计高9.25 m,宽12.25 m。隧址区位于低中山区,出露地层岩性主要为第三系泥质砂岩、砂岩、砾岩及第四系(Q)松散堆积层。其中Ⅲ级围岩为主、Ⅳ围岩为辅。
(1)爆破孔布置及开挖方式
隧道主动断面面积较大,采用分台阶方式进行爆破与开挖,其中上台阶断面面积35.91 m2,下台阶断面面积57.28 m2,见图1。采用凿岩机水平钻孔,待上台阶爆破完成并清除爆破岩堆以后,再进行下台阶爆破。
(2)爆破器材与装药结构
有水地段采用爆炸性能、抗水性能、安全性能较好及环境污染小的乳化炸药,规格为ϕ 32 mm×200 mm,乳化炸药间隔或不耦合装药,其余均用2#岩石硝铵炸药,具体爆破参数见表1
图2所示,轮廓孔采用分段装药结构,其他孔采用连续装药结构。事先由炮工将药卷间隔串联在导爆索上,并用胶带绑扎在一根有一定强度的竹片上,装药时炮工将绑有药卷的竹片放入每个孔内。表2为隧道下台阶爆破参数。
在本案例研究中,从隧道口弃石场获取了爆破岩体碎片的高清照片,使用Split-desktop工具对爆破碎片进行测量,测量碎片块度(图34)并生成颗粒级配曲线(图56)。采用的参照物为黑色钢管,长度为15 cm。蓝色区域或线条为岩石边界。图78是采用手动的方式对识别有误的边界进行删除,同时对遗漏的边界进行补充。
根据爆破设计获取上下台阶的爆破几何参数,根据爆破日志统计数据获取爆破炸药总量和炸药单耗,根据爆破岩堆图像识别获取爆破碎片平均块度X50。分别采用Kuznetsov模型、Li模型和Kansake模型计算上下台阶爆破单耗,将计算结果与统计结果汇总于表3。由表3可知,Li模型对炸药单耗的预测结果与统计结果大相径庭,严重高估了隧道爆破的炸药单耗。Kansake模型对炸药单耗预测结果与统计结果非常接近,但由于忽略了岩体特性的影响,其预测效果在是否适用于其他岩体尚未可知。对于本次隧道爆破,Kuznetsov模型在上台阶炸药单耗预测结果比统计值高12%,下台阶炸药单耗预测结果比统计值高16%,基本是适用于隧道爆破炸药单耗预测。
总之,Kuznetsov模型在炸药单耗预测中更好。其一是Kuznetsov模型综合考虑了岩体特性、爆破块度和炸药性质的影响,特别是岩体特性,已经被证明是影响炸药单耗的最大因素,而Kansake模型忽略了特体特性的影响;其二是Kuznetsov模型在行业内应用较广泛,其实用性得到了一致的认可。
上下台阶爆破块度累积分布曲线如图9所示,可知红色曲线比黑色曲线更平滑,表明下台阶爆破块度分布相比于上台阶连续性更强,这是由于下台阶断面形状更加规整,类似于一个矩形,爆破孔布置更加规范所致。两条曲线在横坐标为5 cm和9 cm处相交,5 cm以下的爆破碎片占比,上台阶比下台阶高很多;5~9 cm之间的爆破碎片占比,上台阶与下台阶相近。10 cm以上,黑色曲线又回到红色曲线上方,表明同一占比情况下,下台阶爆破碎片的尺寸,均高于上台阶,下台阶最大碎片尺寸约为75.2 cm,也高于上台阶的63.5 cm。对比二者的炸药单耗可知,上台阶炸药单耗为1.05 kg/m3,下台阶为0.75 kg/m3。总而言之,上台阶爆破单耗高,岩体爆破后细颗粒含量高、整体块体小;下台阶爆破单耗低,岩体爆破后细颗粒含量低、整体块度大。
Kuz-Ram块度模型考虑了钻孔几何、装药结构及爆破碎片平均块度等参数对爆破块度分布的影响,KCO模型在此基础上增加了一个最大块度参数Xmax以控制爆破最大碎片的产生,按公式(8)~(13)计算隧道上下台阶模型参数,汇总于表4
计算得到模型参数以后,根据Kuz-Ram和KCO模型分别绘制上下台阶爆破碎片曲线,将模型预测曲线与实测曲线绘制于图10图11中。由图可知,三条曲线在纵坐标为50的网格线上交汇,对应的平均碎片尺寸X50为0.16和0.21 m。当碎片块度小于X50时,实测曲线大体上均于预测曲线的下方,表明两个预测模型均高估了小块体的含量,但Kuz-Ram模型预测结果与实测结果更接近;相反,当碎片块度大于X50时,实测曲线均于预测曲线的上方,表明两个预测模型均低估了大块体的含量,但KCO模型预测结果与实测结果更接近;对于最大块度Xmax,上下台阶实测结果分别为63.5 cm和75.2 cm,Kuz-Ram模型预测结果为97 cm和155 cm,比实测结果大得多。而KCO模型由于引入了Xmax参数,预测结果分别为70 cm和80 cm,与实测结果吻合度较高。
(1)尽管3个爆破炸药单耗模型均从露天采矿开发的,但Kuznetsov模型和Kansake模型仍然适用于隧道工程炸药单耗预测。且Kuznetsov模型考虑了岩体特性的影响,比Kansake模型更加实用;此外,Kuznetsov模型可以预测爆破碎片平均值,作为爆破碎片分布模型的基础。
(2)通过对比上下台阶岩体爆破效果,可知上台阶爆破单耗高,岩体爆破后细颗粒含量高、整体块体小;下台阶爆破单耗低,岩体爆破后细颗粒含量低、整体块度大。
(3)通过对比2种预测方法,可知对于块度在平均值以下的小块体爆破碎片,采用Kuz-Ram模型预测效果较好;对于块度在平均值以上的大块体爆破碎片,采用KCO模型预测效果较好,且KCO模型对最大爆破块体的预测较准确。
  • 高速铁路山岭隧道智能化建造技术研究(K2018G014)
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2023年第40卷第2期
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doi: 10.3963/j.issn.1001-487X.2023.02.012
  • 接收时间:2023-01-27
  • 首发时间:2026-03-18
  • 出版时间:2023-06-01
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  • 收稿日期:2023-01-27
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高速铁路山岭隧道智能化建造技术研究(K2018G014)
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    中铁十一局集团有限公司,武汉 430064
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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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