CN111339486A - A big data evaluation method for deep foundation pit blasting vibration velocity risk level - Google Patents

Abstract

The invention belongs to the field of analysis of the impact of deep foundation pit construction on the surrounding environment, and particularly relates to a big data evaluation method for a deep foundation pit blasting vibration velocity risk level. On the basis of quantifying the surrounding environmental information, considering the blasting vibration velocity risk assessment under various data, the blasting vibration velocity required for the project is finally determined. This method avoids the method of determining the blasting vibration velocity, which is carried out only according to the blasting specification and does not consider the surrounding environment, and the determined blasting vibration velocity is more scientific and reasonable.

Description

A big data evaluation method for deep foundation pit blasting vibration velocity risk level

Technical field:

The invention belongs to the field of analysis of the impact of deep foundation pit construction on the surrounding environment, and particularly relates to a big data evaluation method for a deep foundation pit blasting vibration velocity risk level.

Background technique:

With the rapid development of national infrastructure construction, deep and large foundation pits continue to appear. Rock blasting is often required in the process of foundation pit construction, and blasting vibration will affect the surrounding environment, especially in crowded cities, surrounded by buildings and residents, and higher requirements for blasting. Reasonable assessment of the impact of blasting vibration speed on the surrounding environment is a practical engineering problem related to people's livelihood, and is of great significance. At present, the implementation of blasting vibration velocity is mainly determined by comparing with the requirements of blasting regulations. In this way, when the vibration velocity determined according to the specifications is used in actual engineering blasting, there are often cases of petitions by nearby residents and damage to buildings. The reason is mainly the blasting wave. The propagation energy consumption of various kinds of energy in the medium of different formation combinations is different, and at the same time, the requirements of different surrounding environments where the blasting is located are also different. Therefore, it is necessary to comprehensively consider various influencing factors, and evaluate the surrounding environmental risk level for the blasting vibration velocity required by the specification, and then determine the final blasting vibration velocity, so that the construction can be carried out more scientifically.

Invention content:

The technical problem to be solved by the present invention is that the requirements of the surrounding environment where the blasting is located are different, which cannot be determined only by reference to the requirements of the blasting specification.

In order to solve the above problems, the present invention proposes a big data evaluation method for the vibration velocity risk level of foundation pit blasting. On the basis of quantifying the surrounding environmental information, considering the blasting vibration velocity risk assessment under various data, the blasting required for the project is finally determined. Vibration speed. This method avoids the method of determining the blasting vibration velocity, which is carried out only according to the blasting specification and does not consider the surrounding environment, and the determined blasting vibration velocity is more scientific and reasonable.

In order to achieve the above object, the present invention is specifically realized by the following technical solutions. A method for evaluating the risk level of vibration velocity in deep foundation pit blasting with big data, as shown in Figure 1, takes the deep foundation pit blasting of a subway station as an example, including the following steps:

(1) Determine the environmental impact factors around the blasting project, divide the risk levels of the factors, and use the collective method to quantify the factors;

(2) Determine the risk assessment set, classify the risk assessment of the impact of blasting vibration speed on the surrounding environment, and determine the standard for the later assessment;

(3) Determine the membership function and membership degree according to the quantification factors of the convention;

(4) Calculate the information entropy of each influencing factor, determine the weight of each influencing factor considering the information entropy, obtain the weight matrix of the influencing factors, and then establish the primary matrix of surrounding environmental risk assessment, that is, the degree of membership matrix, and further obtain the impact of blasting vibration velocity on the surrounding environment. Evaluate the final matrix;

(5) Search the membership degree of the ultimate risk evaluation matrix, determine the level of the evaluation set where the factor is located according to the standard of step (2), and obtain the risk evaluation result.

Further, step (1) is divided into the following steps:

(1-1) Select the influencing factors of blasting vibration velocity:

The range of blasting vibration velocity energy transmission is mainly related to 8 types of factors, namely: rock mass grade of foundation pit wall, rock mass weathering degree, soil mass grade, water content of rock and soil mass, geological survey, maximum charge of a single section, blasting effect , The distance from the blasting center to the measuring point. The eight types of factors are represented by symbols, as shown in Table 1. Create a factor set C.

There are many factors that affect the blasting vibration velocity, and different factors or different levels of the same factor will have different effects on the blasting vibration velocity. These eight factors are considered from three aspects: engineering geological factors, hydrogeological factors and design and construction factors. Among them, the rock mass grade of the foundation pit wall, the weathering degree of the rock mass, and the soil mass grade are considered from the engineering geological factors; the water content of the rock and soil mass is considered from the hydrogeological factors; The distance between the measuring points is considered from the design and construction factors. These eight factors are the main factors affecting the blasting vibration velocity, and researching them is more comprehensive and meaningful for the study of blasting vibration velocity.

Table 1 Corresponding symbols of the influencing factors of blasting vibration velocity

C _i represents the influence attribute of the surrounding environment in the factor set C.

(1-2) Classify these 8 types of factors. Because the classification of factors is based on the characteristics of factors, some are qualitatively divided, some are quantitatively divided, and the classification results are shown in Table 2. Among them, the rock mass grade of the foundation pit wall is to classify some rock masses with similar stability into one class according to the hardness and integrity of the rock mass.

Table 2 Classification of 8 types of factors

Further, the determination of the blasting vibration velocity risk assessment set in step (2) is as follows: according to Table 3, the risk assessment grade is given according to the impact of blasting vibration velocity on the surrounding environment. D represents the evaluation set.

Table 3 D-level division of blasting vibration velocity evaluation set

Further, in step (3), the membership function and the membership are respectively determined according to non-quantifiable factors and quantifiable factors. For unquantifiable factors, the Karwowski membership function is used, which is an empirical membership function commonly used in engineering and has accuracy. For quantifiable factors, an intermediate quadratic parabolic membership function is used, which has a simple form and a small amount of calculation, and can reflect the changing process of the data to the greatest extent; higher.

Further, in step (4), be specifically:

(4-1) Determination of information entropy: For the foundation pit blasting, perform 6 times the set method to quantify the factors, and obtain the quantified set c={c ₁ ,c ₂ ,c ₄ ,c ₅ ,c ₆ ,c ₈ }, H(D|{c _i }) represents the information entropy of the factor c _i in the influencing factor set relative to D in the risk assessment set, which is expressed as:

(4-2) Weight calculation:

Wi _reflects the importance of a certain attribute relative to the overall attribute in the whole evaluation system. The corresponding influence factor subset c _i weight is:

where σ _CD (ci )=p(D _i | _{ci )} .

(4-3) Get the final matrix:

Arrange the above weights into a matrix form to obtain a matrix after normalized weights, denoted by A.

Using the quadratic parabolic distribution, the membership matrix is obtained as R.

Then the final matrix is B, B=A×R.

Further, step (5) is specifically to obtain the maximum membership degree according to the principle of maximum membership degree. According to the maximum membership degree, the scope of the evaluation set is judged, and the risk level is determined.

The beneficial effects of the present invention are:

On the basis of quantifying the surrounding environment information, the invention takes into account the blasting vibration velocity risk assessment under various data, and finally determines the blasting vibration velocity required by the project; it avoids carrying out only according to the blasting specification without considering the surrounding environment. The blasting vibration velocity determination method is more scientific and reasonable.

Description of drawings

FIG. 1 is a technical flow chart of the implementation of the present invention.

Detailed ways:

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Example. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Example 1:

The deep foundation pit of a subway station has a total length of 202 meters and is an underground two-story island station with a standard section width of 18.7m and a height of 14.605m. There are government service centers, construction companies, private houses and other important buildings around. The geology is relatively simple. The depth of the vault is 17.8-38.9m, the overlying rock is 14-36m, and most of the deep foundation pits are located in the slightly weathered rock. The stratum is the 182nd layer of micro-weathered fine-grained granite (thickness 25-64m, red flesh, slightly developed-undeveloped joints and fissures, relatively complete hard-hard rock, basic rock mass grade III, saturated rock). Engineering geological features: The station belongs to the denudation residual hill-denudation slope landform, and the construction is carried out by the underground excavation method. The stratum that the tunnel passes through is mainly fine-grained granite micro-weathering and fine-grained granite micro-weathering joint development zone and fine-grained granite (massive broken rock). The groundwater in the site is mainly bedrock fissure water with a small amount of water. Select the influencing factor set and risk assessment set, as shown in Table 1 and Table 2. Calculate the information entropy, the corresponding importance and the normalized weight matrix.

Table 4 Normalized weight distribution table

H(D|C)=0.1174.

Arrange the normalized weights in the table into a matrix form.

A=[0.1735, 0.1643, 0.1476, 0.1348, 0.1938, 0.1860].

A quadratic parabolic distribution is used. The membership matrix is R,

The final matrix is B, then

but

B=[0.2128, 0.5770, 0.6136, 0.3994, 0.0995]

Table 5 Value range of membership degree for blasting vibration velocity grade evaluation

Evaluation level Safety Safer early warning more dangerous Danger Regional division [1,0.8] [0.6,0.8) [0.4,0.6) [0.2,0.4) [0,0.2)

When monitoring the blasting vibration data of the deep foundation pit construction, 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 relatively safe state. The maximum blasting vibration velocity obtained from the monitoring of Yongnian Road Station is 0.95cm/s. According to the monitoring report, the blasting did not cause large-scale damage to the surrounding buildings, which is consistent with the assessment results.

Claims (9)

1. a deep foundation pit blasting vibration velocity risk level big data evaluation method is characterized in that comprising the following steps: (1) Determine the environmental impact factors around the blasting project, divide the risk levels of the factors, and use the collective method to quantify the factors; (2) Determine the risk assessment set, classify the risk assessment of the impact of blasting vibration speed on the surrounding environment, and determine the standard for later assessment; (3) Determine the membership function and membership degree according to the quantification factors of the convention; (4) Calculate the information entropy of each influencing factor, determine the weight of each influencing factor considering the information entropy, obtain the weight matrix of the influencing factors, and then establish the primary matrix of surrounding environmental risk assessment, that is, the degree of membership matrix, and further obtain the impact of blasting vibration velocity on the surrounding environment. Evaluate the final matrix; (5) Search the membership degree of the ultimate risk evaluation matrix, determine the level of the evaluation set where the factor is located according to the standard of step (2), and obtain the risk evaluation result. 2. a kind of deep foundation pit blasting vibration velocity risk level big data evaluation method as claimed in claim 1, is characterized in that: in step (1), choose blasting vibration velocity influence factor to be specifically set up factor set C, comprise foundation pit wall Rock mass grade C ₁ , rock mass weathering degree C ₂ , soil mass grade C ₃ , water content of rock and soil mass C ₄ , geological survey C ₅ , single-stage maximum charge C ₆ , blasting effect C ₇ , blasting center to measuring point distance C ₈ . 3. a kind of deep foundation pit blasting vibration velocity risk grade big data evaluation method as claimed in claim 2 is characterized in that: in step (1), the division of risk grade is carried out according to the following table:

4. a kind of deep foundation pit blasting vibration velocity risk level big data evaluation method as claimed in claim 1, is characterized in that: in step (2), blasting vibration velocity risk assessment set is determined, and carry out according to the following table:

5. a kind of deep foundation pit blasting vibration velocity risk grade big data evaluation method as claimed in claim 1, is characterized in that: during step (3) membership degree function and membership degree, determine respectively according to non-quantifiable factor and quantifiable factor; For unquantifiable factors, the Karwowski membership function is used; for quantifiable factors, the intermediate quadratic parabolic membership function is used. 6. a kind of deep foundation pit blasting vibration velocity risk level big data evaluation method as claimed in claim 1, is characterized in that: in step (4), the determination of information entropy is specifically: for foundation pit blasting, carry out 6 collection methods Covenant quantification factors, get the quantification set c={c ₁ , c ₂ , c ₄ , c ₅ , c ₆ , c ₈ }, H(D|{c _i }) represents the factor c _i in the set of influencing factors relative to the risk The information entropy of D in the evaluation set is expressed as:

7. a kind of deep foundation pit blasting vibration velocity risk level big data evaluation method as claimed in claim 6, is characterized in that: in step (4), weight calculation is specifically: W _i is reflected in a certain attribute in the whole evaluation system The importance relative to the overall attribute; the corresponding weight of the subset c _i of the influencing factors is:

where σ _CD (ci )=p(D _i | _{ci )} . 8. a kind of deep foundation pit blasting vibration velocity risk grade big data evaluation method as claimed in claim 7, is characterized in that: in step (4), obtain the ultimate matrix specifically: Arrange the weights into a matrix form, and get the matrix after normalized weights, which is represented by A; Using the quadratic parabolic distribution, the membership matrix is obtained as R; Then the final matrix is B, B=A×R. 9 . The big data evaluation method for deep foundation pit blasting vibration velocity risk level according to claim 1 , wherein step (5) is specifically to obtain the maximum membership degree according to the principle of maximum membership degree. 10 . According to the maximum membership degree, the scope of the evaluation set is judged, and the risk level is determined.

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