WO2021169038A1 - 一种深基坑爆破振速风险等级大数据评价方法 - Google Patents

一种深基坑爆破振速风险等级大数据评价方法 Download PDF

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WO2021169038A1
WO2021169038A1 PCT/CN2020/089026 CN2020089026W WO2021169038A1 WO 2021169038 A1 WO2021169038 A1 WO 2021169038A1 CN 2020089026 W CN2020089026 W CN 2020089026W WO 2021169038 A1 WO2021169038 A1 WO 2021169038A1
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blasting
risk
vibration velocity
foundation pit
factors
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袁长丰
陈秋汝
于广明
李亮
凌贤长
贺可强
路世豹
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青岛理工大学
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  • the invention belongs to the field of analysis of the impact of deep foundation pit construction on the surrounding environment, and specifically relates to a large data evaluation method for the risk level of deep foundation pit blasting vibration velocity.
  • blasting vibration velocity is mainly determined by comparing the requirements of the blasting specifications. In this way, the vibration velocity determined according to the specifications often has cases of nearby residents petitioning petitions and building damage during actual engineering blasting. The main reason is the blasting wave.
  • the technical problem to be solved by the present invention is that the requirements of the surrounding environment of blasting are different, and it cannot be determined only by referring to the requirements of blasting specifications.
  • the present invention proposes a large data evaluation method for the blasting vibration velocity risk level of foundation pits. Based on the quantification of the surrounding environment information, the risk evaluation of the blasting vibration velocity under various data is considered to finally determine the blasting required for the project. Vibration speed. This method avoids the method of determining the blasting vibration velocity that is only based on the blasting specifications and does not consider the surrounding environment, and the determined blasting vibration velocity is more scientific and reasonable.
  • the present invention is specifically implemented by the following technical solutions, a large data evaluation method for the vibration velocity risk level of deep foundation pit blasting.
  • step (1) is divided into the following steps:
  • blasting vibration velocity energy transmission is mainly related to 8 types of factors, namely: foundation pit wall rock mass grade, rock weathering degree, soil grade, water content of rock and soil, geological survey, single-stage maximum charge, and blasting effect , Distance from blasting center to measuring point.
  • 8 types of factors are represented by symbols, as shown in Table 1.
  • C i represents the influence attribute of the surrounding environment in the factor set C.
  • the determination of the blasting vibration velocity risk assessment set in step (2) is specifically as follows: According to Table 3, the risk assessment level is divided according to the impact of blasting vibration velocity on the surrounding environment. D represents the evaluation set.
  • the membership function and the membership degree are determined separately according to the unquantifiable factors and quantifiable factors.
  • the Karwowski membership function is used for non-quantifiable factors.
  • the Karwowski membership function is an empirical membership function commonly used in engineering and has accuracy.
  • the intermediate quadratic parabolic membership function is adopted, which is simple in form and small in calculation, which can reflect the changing process of the data to the greatest extent; the steeper the shape of the membership function, the higher the resolution, and the degree of discrimination of the calculated results Higher.
  • step (4) is specifically as follows:
  • W i reflects the importance of a certain attribute relative to the overall attribute in the entire evaluation system.
  • the corresponding weight of the influencing factor subset c i is:
  • ⁇ CD (c i ) p(D i
  • the membership matrix is R.
  • step (5) is specifically to obtain the maximum degree of membership according to the principle of maximum degree of membership. According to the maximum degree of membership, judge its scope in the evaluation set and determine its risk level.
  • the present invention considers the risk assessment of blasting vibration velocity under various data, and finally determines the blasting vibration velocity required by the project; avoiding the blasting specification only and without considering the surrounding environment
  • the method for determining the blasting vibration velocity is more scientific and reasonable.
  • Figure 1 is a technical flow chart of the implementation of the present invention.
  • the deep foundation pit of a subway station is 202 meters long and is an underground two-story island station.
  • the standard section has a width of 18.7m and a height of 14.605m.
  • the geology is relatively simple.
  • the buried depth of the vault is 17.8-38.9m, and the overlying rock is 14-36m.
  • Most of the deep foundation pits are located in the slightly weathered rock.
  • the stratum is the 182th layer of slightly weathered fine-grained granite (thickness 25-64m, flesh red, slightly developed-undeveloped joints and fissures, relatively complete hard-hard rock, basic quality grade III, rock saturated).
  • the station belongs to the denuded mound-eroded slope landform, and it is constructed by the underground excavation method.
  • the tunnel traverses mainly the micro-weathered fine-grained granite and the micro-weathered joint development zone of the fine-grained granite and the fine-grained granite (mass broken rock).
  • the groundwater in the site is mainly bedrock fissure water, and the amount of water is relatively small.
  • Select the influencing factor set and the risk evaluation set as shown in Table 1 and Table 2. Calculate information entropy, corresponding importance and normalized weight matrix.
  • a quadratic parabolic distribution is used.
  • the membership matrix is R,
  • the distance between the monitoring point and the blasting center is 50 meters. According to the principle of maximum membership degree, the maximum value is 0.6136. According to Table 5, the corresponding level is relatively safe.
  • the blasting vibration speed is within the range specified in the specification, and it will not cause large-scale damage to the surrounding environment, which is a safer state.
  • the maximum blasting vibration velocity monitored by Yongnian Road Station was 0.95cm/s. According to the monitoring report, the blasting did not cause large-scale damage to surrounding buildings, which was consistent with the assessment results.

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Abstract

本发明属于深基坑施工对周围环境影响分析领域，具体涉及一种深基坑爆破振速风险等级大数据评价方法。是在对周围环境信息进行量化的基础上，考虑多种数据下的爆破振速风险评价，最终确定工程所需的爆破振速。该方法避免了只根据爆破规范来进行的、不考虑周围环境情况的爆破振速确定方法，确定的爆破振速更具科学合理性。

Description

一种深基坑爆破振速风险等级大数据评价方法 技术领域：
本发明属于深基坑施工对周围环境影响分析领域，具体涉及一种深基坑爆破振速风险等级大数据评价方法。
背景技术：
随着国家基础设施建设的快速发展，深大基坑不断出现。在基坑施工过程中经常需要岩石爆破，爆破振动会对周围环境产生影响，特别是在人群拥挤的城市中，周围都是建筑物和居民，对爆破要求更高。合理评估爆破振速对周围环境影响是关系民生的工程实际问题，具有重要意义。目前，爆破振速的实施主要通过对照爆破规范要求来确定，这样按照规范确定的振速在实际工程爆破时，经常出现附近居民上访和建筑物损伤的案例，究其原因，主要是爆破波这种能量在不同地层组合的介质中的传播能耗不同，同时，爆破所处的不同周围环境要求也不同。因此，需要综合考虑各种影响因素，对规范要求的爆破振速进行周围环境风险等级评价后在最终爆破振速，这样才能够更科学开展施工。
发明内容：
本发明要解决的技术问题是爆破所处的周围环境要求不同，不能仅对照爆破规范要求来确定。
为解决上述问题，本发明提出基坑爆破振速风险等级大数据评价方法，在对周围环境信息进行量化的基础上，考虑多种数据下的爆破振速风险评价，最终确定工程所需的爆破振速。该方法避免了只根据爆破规范来进行的、不考虑周围环境情况的爆破振速确定方法，确定的爆破振速更具科学合理性。
为达到上述目的，本发明具体通过以下技术方案实现，一种深基坑爆破振速风险等级大数据评价方法，如图1所示，以地铁车站深基坑爆破为例，包括以下步骤：
(1)确定爆破工程周围环境影响因素，划分因素的风险等级，采用集合方法公约量化因素；
(2)确定风险评价集合，把对爆破振速对周围环境影响风险评价划分等级，为后期评价确定标准；
(3)根据公约量化因素确定隶属度函数以及隶属度；
(4)计算各影响因素信息熵，确定考虑信息熵的各影响因素权重，得到影响因素权重矩阵，然后建立周围环境风险评价初级矩阵，即隶属度矩阵，进一步得到爆破振速对周围环境 影响风险评价终极矩阵；
(5)搜索风险评价终极矩阵隶属度，根据步骤(2)的标准确定因素所在评价集中的等级，得出风险评价结果。
进一步的，步骤(1)分为以下步骤：
(1-1)选取爆破振速影响因素：
爆破振速能量传输的范围主要和8类因素相关，分别是：基坑壁岩体级别、岩体风化程度、土体等级、岩土体含水性、地质勘察、单段最大药量、爆破效果、爆破中心至测点距离。把这8类因素用符号表示，如表1所示。建立因素集C。
影响爆破振速的因素很多，不同的因素或者相同的因素中不同的等级都会对爆破振速产生不同的影响。这八个因素是从工程地质因素，水文地质因素和设计施工因素三个方面考虑的。其中基坑壁岩体级别、岩体风化程度、土体等级是从工程地质因素考虑；岩土体含水性是从水文地质因素考虑；地质勘察、单段最大药量、爆破效果、爆破中心至测点距离是从设计施工因素方面考虑。这八个因素是影响爆破振速的主要因素，研究他们对研究爆破振速来说更全面，更有意义。
表1 爆破振速影响因素对应符号
Figure PCTCN2020089026-appb-000001
C i表示因素集C中周围环境影响属性。
(1-2)对这8类因素进行等级划分。由于因素的等级划分按照因素特点，有的是定性划分，有的是定量划分，划分结果如表2所示。其中，基坑壁岩体级别是根据岩体的坚硬程度和完整性将稳定性相似的一些岩体划分为一类。
表2 8类因素等级划分
Figure PCTCN2020089026-appb-000002
Figure PCTCN2020089026-appb-000003
进一步的，步骤(2)中爆破振速风险评价集合确定具体为：根据表3按照爆破振速对周围环境影响给出了风险评价等级划分。D表示评价集合。
表3 爆破振速评价集合D等级划分
Figure PCTCN2020089026-appb-000004
进一步的，步骤(3)隶属度函数以及隶属度时根据不可量化因素和可量化因素分别确定。对于不可量化因素，采用Karwowski隶属函数，Karwowski隶属函数是工程上常用的经验隶属函数，具有准确性。对于可量化因素，采用中间形二次抛物形隶属度函数，其形式简单，计算量小，能最大程度的反映数据的变化过程；隶属函数的形状越陡，分辨率越高，计算结果区分度越高。
进一步的，步骤(4)中具体为：
(4-1)信息熵的确定：针对基坑爆破，进行6次集合方法公约量化因素，得到公约量化集c＝{c 1,c 2,c 4,c 5,c 6,c 8}，H(D|{c i})表示影响因素集中的因素c i相对于风险评价集合中的D的信息熵，用信息熵表示为：
Figure PCTCN2020089026-appb-000005
(4-2)权重计算：
W i反映在整个评价系统中某一个属性相对于总体属性的重要程度。相应的影响因素子集c i权重为：
Figure PCTCN2020089026-appb-000006
其中，σ CD(c i)＝p(D i|c i)。
(4-3)得到终极矩阵：
将上述权重整理成矩阵形式，得到归一化权重后的矩阵，用A表示。
采用二次抛物形分布，求得隶属度矩阵为R。
则得到终极矩阵为B，B＝A×R。
进一步的，步骤(5)具体为根据最大隶属度原则，得出隶属度最大值。根据最大隶属度判断其在评价集中的范围，确定其风险等级。
本发明的有益效果为：
本发明在对周围环境信息进行量化的基础上，考虑多种数据下的爆破振速风险评价，最终确定工程所需的爆破振速；避免了只根据爆破规范来进行的、不考虑周围环境情况的爆破振速确定方法，确定的爆破振速更具科学合理性。
附图说明
图1是本发明实施的技术流程图。
具体实施方式：
为使本发明实施例的目的、技术方案和优点更加清楚，下面对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例1：
某地铁车站深基坑全长202米，为地下两层岛式车站，标准段宽度18.7m，高度为14.605m。周围有政府服务中心，建筑公司，民房等重要建筑物，地质较为简单，拱顶埋深17.8-38.9m，覆岩14-36m，深基坑大部分位于微风化岩中，车站主体主要通过的地层为第182层微风化细粒花岗岩(厚度25-64m，肉红色，节理裂隙稍发育-不发育，属较完整的较硬-坚硬岩，岩体基本质量等级Ⅲ级，岩石饱和)。工程地质特征：车站属于剥蚀残丘-剥蚀斜坡地貌，采用暗挖法施工，隧道穿越地层主要为细粒花岗岩微风化和细粒花岗岩微风化节理发育带和细粒花 岗岩(块状破碎岩)，场区地下水主要为基岩裂隙水，水量较小。选取影响因素集和风险评价集，如表1和表2所示。计算信息熵，相应的重要度和归一化权重矩阵。
表4 归一化权重分配表
Figure PCTCN2020089026-appb-000007
H(D|C)＝0.1174。
将表格中归一化权重整理成矩阵形式。
A＝[0.1735,0.1643,0.1476,0.1348,0.1938,0.1860]。
采用的是二次抛物形分布。隶属度矩阵为R,
Figure PCTCN2020089026-appb-000008
终极矩阵为B,则
Figure PCTCN2020089026-appb-000009
则
B＝[0.2128,0.5770,0.6136,0.3994,0.0995]
表5 爆破振速等级评价隶属度取值范围
评价等级 安全 较安全 预警 较危险 危险
区域划分 [1,0.8] [0.6,0.8) [0.4,0.6) [0.2,0.4) [0,0.2)
该深基坑施工爆破振动数据进行监测时，监测点距爆破中心距离为50米。根据最大隶属度原则，得出最大值为0.6136，根据表5，对应等级为较安全。爆破振动速度在规范规定的范围内，不会对周围环境造成大规模的破坏，是较安全的状态。永年路站监测所得最大爆破 振速为0.95cm/s，根据监测报告，爆破没有对周围建筑物造成大规模的破坏，与评估结果吻合。

Claims (9)

  1. 一种深基坑爆破振速风险等级大数据评价方法，其特征在于包括以下步骤：
    (1)确定爆破工程周围环境影响因素，划分因素的风险等级，采用集合方法公约量化因素；
    (2)确定风险评价集合，把爆破振速对周围环境影响风险评价划分等级，为后期评价确定标准；
    (3)根据公约量化因素确定隶属度函数以及隶属度；
    (4)计算各影响因素信息熵，确定考虑信息熵的各影响因素权重，得到影响因素权重矩阵，然后建立周围环境风险评价初级矩阵，即隶属度矩阵，进一步得到爆破振速对周围环境影响风险评价终极矩阵；
    (5)搜索风险评价终极矩阵隶属度，根据步骤(2)的标准确定因素所在评价集中的等级，得出风险评价结果。
  2. 如权利要求1所述的一种深基坑爆破振速风险等级大数据评价方法，其特征在于：步骤(1)中选取爆破振速影响因素具体为建立因素集C，包括基坑壁岩体级别C 1、岩体风化程度C 2、土体等级C 3、岩土体含水性C 4、地质勘察C 5、单段最大药量C 6、爆破效果C 7、爆破中心至测点距离C 8。
  3. 如权利要求2所述的一种深基坑爆破振速风险等级大数据评价方法，其特征在于：步骤(1)中风险等级的划分，按照下表进行：
    Figure PCTCN2020089026-appb-100001
  4. 如权利要求1所述的一种深基坑爆破振速风险等级大数据评价方法，其特征在于：步骤(2)中爆破振速风险评价集合确定，按照下表进行：
    Figure PCTCN2020089026-appb-100002
  5. 如权利要求1所述的一种深基坑爆破振速风险等级大数据评价方法，其特征在于：步骤(3)隶属度函数以及隶属度时根据不可量化因素和可量化因素分别确定；对于不可量化因素，采用Karwowski隶属函数；对于可量化因素，采用中间形二次抛物形隶属度函数。
  6. 如权利要求1所述的一种深基坑爆破振速风险等级大数据评价方法，其特征在于：步骤(4)中信息熵的确定具体为：针对基坑爆破，进行6次集合方法公约量化因素，得到公约量化集c＝{c 1,c 2,c 4,c 5,c 6,c 8}，H(D|{c i})表示影响因素集中的因素c i相对于风险评价集合中的D的信息熵，用信息熵表示为：
    Figure PCTCN2020089026-appb-100003
  7. 如权利要求6所述的一种深基坑爆破振速风险等级大数据评价方法，其特征在于：步骤(4)中中权重计算具体为：W i反映在整个评价系统中某一个属性相对于总体属性的重要程度；相应的影响因素子集c i权重为：
    Figure PCTCN2020089026-appb-100004
    其中，σ CD(c i)＝p(D i|c i)。
  8. 如权利要求7所述的一种深基坑爆破振速风险等级大数据评价方法，其特征在于：步骤(4)中得到终极矩阵具体为：
    将权重整理成矩阵形式，得到归一化权重后的矩阵，用A表示；
    采用二次抛物形分布，求得隶属度矩阵为R；
    则得到终极矩阵为B，B＝A×R。
  9. 如权利要求1所述的一种深基坑爆破振速风险等级大数据评价方法，其特征在于：步骤(5)具体为为根据最大隶属度原则，得出隶属度最大值。根据最大隶属度判断其在评价集中的范围，确定其风险等级。
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