Tunnel intelligent blasting design method based on geological sketch

Abstract

The invention provides an intelligent tunnel blasting design method based on geological sketch, which comprises the following steps: S1, acquiring related data, drilling parameters, explosive parameters and blasting control requirements of a tunnel face, and generating a two-dimensional model of the tunnel face; s2, according to the tunnel face two-dimensional model, the drilling parameters, the explosive parameters and the blasting control requirements, blasting parameters are calculated; and S3, forecasting the blasting effect, and optimizing and adjusting the blasting design according to the forecasted blasting effect. According to the method, a designer only needs to input information such as initial data, geological conditions and excavation quality requirements, and a computer automatically calculates and outputs blasting design, so that the blasting design is systematized, the workload is reduced, and the efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel blasting design, and in particular to a tunnel intelligent blasting design method based on geological sketch. Background Art

[0002] In the current drilling and blasting tunnel blasting design, most of the blasting design is done by professional blasting technicians relying on their rich experience and previous similar engineering cases. This method not only consumes a lot of manpower, but also has low efficiency and cannot be adjusted in time according to the development of joints on site and changes in rock strength. Therefore, it is urgent to propose a systematic tunnel intelligent blasting design method that can perform blasting design according to the degree of joint development and changes in rock strength. It is not only more efficient, but also can dynamically adjust the blasting design in real time to improve the blasting excavation effect. Summary of the invention

[0003] The purpose of the present invention is to address the defects of the prior art and provide a tunnel intelligent blasting design method based on geological sketches. The designer only needs to input initial data, geological conditions, excavation quality requirements and other information, and the computer automatically calculates and outputs the blasting design, which not only makes the blasting design systematic, but also reduces the workload and improves efficiency.

[0004] In order to solve the above technical problems, the present invention provides a tunnel intelligent blasting design method based on geological sketch, comprising:

[0005] S1. Obtain relevant data of the tunnel face, drilling parameters, explosive parameters, and blasting control requirements, and generate a two-dimensional model of the tunnel face;

[0006] S2. Calculate blasting parameters based on the two-dimensional model of the tunnel face, drilling parameters, explosive parameters, and blasting control requirements;

[0007] S3. Predict blasting effects and optimize and adjust blasting design based on the predicted blasting effects.

[0008] Further, step S1 includes:

[0009] S11. Obtaining tunnel face related data, including tunnel excavation section parameters, tunnel face photos, tunnel face excavation methods, and geological parameters;

[0010] S12, generating a tunnel profile according to tunnel excavation section parameters, and dividing the tunnel face into regions according to the excavation method;

[0011] S13. Identify rock joints and cracks in the photograph of the tunnel face and generate corresponding ones in the tunnel outline.

[0012] Further, step S2 includes:

[0013] S21. Calculate the blasting unit consumption and the number of blast holes;

[0014] S22. Arrange blast holes according to the number of blast holes, the area of ​​the tunnel face, and blast control requirements;

[0015] S23. Determine the charge structure of the blast hole.

[0016] Further, step S21 includes: blasting unit consumption Where, q-explosive consumption, kg / m 3 , f- rock strength coefficient, value is Rc / 10, Rc unit is MPa, p is the explosive force, mL, S- cross-sectional area, m 2 , k is the empirical coefficient.

[0017] Further, step S22 includes: the number of blastholes Where N is the number of blastholes, excluding the number of empty holes without charge; α is the charging coefficient, i.e. the ratio of the length of charge to the total length of the blasthole; γ is the mass of explosive per meter of cartridge, kg / m.

[0018] Furthermore, in step S22, the arrangement positions of the blastholes need to avoid rock joints and fissures.

[0019] Furthermore, in step S23, the blasthole charge structure includes a decoupling coefficient, a charge length, a blocking length and a profile requirement.

[0020] Further, step S3 includes:

[0021] S31, establishing a mapping relationship between blasting parameters and blasting effect parameters;

[0022] S32, combining kernel functions with different characteristics to obtain a fused kernel function;

[0023] S33, using the fusion kernel function to predict the blasting effect;

[0024] S34. Optimize and adjust the blasting design based on the predicted blasting effect.

[0025] Further, in step S32, the RBF kernel function, the Matérn kernel function, the linear kernel function, and the polynomial kernel function are combined to form a fusion kernel function;

[0026] Among them, the RBF kernel function is used to extract local features of data, and its covariance function is:

[0027]

[0028] The Matérn kernel function is a generalization of the RBF kernel and is also used to extract local correlation characteristics. Its covariance function is:

[0029]

[0030] The linear kernel function is used to describe the linear correlation between data, and the covariance function is:

[0031]

[0032] The polynomial kernel function is used to describe the nonlinear relationship between data, and the covariance function is:

[0033]

[0034] Where δ1~δ9 are the kernel function hyperparameters that need to be set;

[0035] The above kernel functions form a fusion kernel function in the following way:

[0036] k M (x i ,x j )=δ 10 ·k RBF (x i ,x j )+δ 11 ·k Matern (x i ,x j )+δ 12 ·k l (x i ,x j )+δ 13 ·k P (x i ,x j );

[0037] Where δ 10 ~δ 13 is the covariance weight of different kernel functions.

[0038] Further, step S33 includes: predicting the over-excavation and under-excavation, cycle footage, smooth blasting hole half-porosity, and block size after the tunnel face blasting.

[0039] The beneficial effects of the present invention are:

[0040] 1. The present invention only requires designers to input information such as initial data, geological conditions and excavation quality requirements, and the computer automatically calculates and outputs the blasting design, which not only systematizes the blasting design, but also reduces the workload and improves the efficiency.

[0041] 2. The existing empirical formula is not applicable to large-section tunnels such as highways and railways. The present invention adjusts the coefficients of blasting unit consumption and the number of blastholes after fitting and correction based on field test data to make it more reasonable.

[0042] 3. The present invention establishes a mapping relationship between blasting parameters and blasting effect parameters, combines kernel functions with different characteristics in a certain way, obtains a fused kernel function for more data characteristics, and improves prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is the principle diagram of the present invention;

[0044] Figure 2 It is a schematic diagram of dividing the surface area of ​​the two-dimensional model of the tunnel face of the present invention;

[0045] Figure 3 It is a schematic diagram of rock joints and cracks on the two-dimensional model of the tunnel face of the present invention;

[0046] Figure 4 This is a flow chart of blasting parameter calculation of the present invention;

[0047] Figure 5 This is a diagram showing the calculation results of blasting parameters of the present invention;

[0048] Figure 6 It is the blasthole arrangement diagram of the present invention. DETAILED DESCRIPTION

[0049] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] like Figure 1 As shown, this embodiment provides a tunnel intelligent blasting design method based on geological sketch, including:

[0051] S1. Obtain relevant data of the tunnel face, drilling parameters, explosive parameters, and blasting control requirements, and generate a two-dimensional model of the tunnel face; specifically including:

[0052] S11. Obtain face-related data, including tunnel excavation section parameters, face photos, face excavation methods, and geological parameters; tunnel excavation section parameters include tunnel excavation shape and size, which can be used to generate tunnel contours; face excavation methods include full-section excavation, step method, and core soil retention method. Different excavation methods have different arrangements of blastholes; geological parameters include rock type corresponding to the face, rock compressive strength Rc, rock integrity Kv, and rock grade.

[0053] S12, such as Figure 2 As shown, in this embodiment, the tunnel excavation section is a four-center circle structure, the step method is used for excavation, the tunnel profile is generated according to the tunnel excavation section parameters, and the face is divided into regions according to the excavation method;

[0054] S13, identify rock joints and cracks in the photograph of the tunnel face, and generate corresponding ones in the tunnel outline, such as Figure 3 shown.

[0055] S2, such as Figure 4 As shown in the figure, the blasting parameters are calculated according to the two-dimensional model of the tunnel face, drilling parameters, explosive parameters, and blasting control requirements; specifically, they include:

[0056] S21. Calculate the blasting unit consumption and the number of blast holes;

[0057] Among them, blasting unit consumption Where, q-explosive consumption, kg / m 3 , f- rock strength coefficient, value is Rc / 10, Rc unit is MPa, p is the explosive force, mL, S- cross-sectional area, m 2 , k is the empirical coefficient.

[0058] Table 1 List of typical engineering parameters

[0059]

[0060]

[0061] Combined with Table 1 By carrying out linear fitting analysis, it can be found that the average value of k is 2.2.

[0062] Number of blast holes Where N is the number of blastholes, excluding the number of empty holes without charge; α is the charging coefficient, i.e. the ratio of the length of charge to the total length of the blasthole; γ is the mass of explosive per meter of cartridge, kg / m.

[0063] S22. Arrange blast holes according to the number of blast holes, the area of ​​the face, and the blast control requirements; the blast holes include peripheral smooth blast holes, slot holes, bottom plate holes, and excavation holes.

[0064] 1. The blasting parameters of peripheral light blasting holes (i.e. peripheral holes) are designed according to Table 2, where l1 represents the designed footage:

[0065] Table 2

[0066]

[0067] 2. Blasting parameter design of slot hole

[0068] (1) Slot hole form

[0069] ① Secondary compound wedge-shaped cut, designed footage l1<1.8m;

[0070] ②Three-level compound wedge-shaped excavation, designed advance l1 ≥ 1.8m.

[0071] (2) Slot hole parameters

[0072] The slot hole parameters are shown in the table below.

[0073] The parameters of the two-stage compound wedge-shaped cutout are designed according to Table 3. h1, h2, and h3 refer to the hole depths of the first-stage, second-stage, and third-stage compound wedge-shaped cutouts, respectively:

[0074] Table 3

[0075]

[0076] Note: When the calculated value of the blockage length Z is less than 40cm, the uniform value is 40cm.

[0077] The parameters of the three-level compound wedge cut are designed according to Table 4:

[0078] Table 4

[0079]

[0080] Note: When the calculated value of the blockage length Z is less than 40cm, the uniform value is 40cm.

[0081] Charge amount for slotting holes and charge amount for auxiliary slotting holes

[0082] The charge of each slot hole should be 35% larger than the average charge of a single hole. The explosive consumption of the slot hole q1 and the explosive consumption of the two auxiliary slot holes q2 and q3 refer to the explosive consumption q above.

[0083] q1=q2=q3=(1+35%)q;

[0084] The charge amount of a single slot hole is Q1 = q1*a1*b1*h1;

[0085] 1# auxiliary slot hole single hole charge amount Q2=q2*a2*b2*h2;

[0086] 2# auxiliary slot hole single hole charge amount Q3 = q3*a3*b3*h3;

[0087] 3. Design of blasting parameters for bottom plate holes:

[0088] The hole spacing parameters of the bottom plate holes are designed according to Table 5.

[0089] Table 5

[0090]

[0091]

[0092] The charge of a single bottom hole Q4 = (l1-Z) / l y *m y, (l1-Z) is rounded down. The bottom plate hole charging section is continuously charged.

[0093] The number of holes in the bottom plate is N d =D / E (round up), D is selected according to the position of the step.

[0094] 4. Design of blasting parameters for tunneling holes

[0095] The number of excavation holes = total number of holes - number of peripheral blast holes - number of bottom plate holes - number of slot holes, that is, the number of excavation holes N j =NN z -N d -N t . Calculate the hole spacing based on the number of holes and then arrange them.

[0096] Charge quantity Q of the excavation hole j = total charge - charge of surrounding smooth blast holes - charge of bottom plate holes - charge of slot holes, charge of single hole of excavation hole Q5 = Q j / N j .

[0097] The blastholes should be arranged to avoid rock joints and fissures. Figure 6 shown.

[0098] S23, determine the charge structure of the blasthole, which includes the uncoupling coefficient, charge length, and blocking length. The calculation results are as follows: Figure 5 Except for the peripheral holes which use intermittent charging, other holes use continuous charging.

[0099] S3. Predict blasting effects and optimize and adjust blasting design based on the predicted blasting effects; specifically including:

[0100] S31, establishing a mapping relationship between blasting parameters and blasting effect parameters;

[0101] S32, combining the RBF kernel function, the Matérn kernel function, the linear kernel function, and the polynomial kernel function to form a fusion kernel function;

[0102] Among them, the RBF kernel function is used to extract local features of data, and its covariance function is:

[0103]

[0104] The Matérn kernel function is a generalization of the RBF kernel and is also used to extract local correlation characteristics. Its covariance function is:

[0105]

[0106] The linear kernel function is used to describe the linear correlation between data, and the covariance function is:

[0107]

[0108] The polynomial kernel function is used to describe the nonlinear relationship between data, and the covariance function is:

[0109]

[0110] Where δ1~δ9 are the kernel function hyperparameters that need to be set;

[0111] The above kernel functions form a fusion kernel function in the following way:

[0112] k M (x i ,x j )=δ 10 ·k RBF (x i ,x j )+δ 11 ·k Matern (x i ,x j )+δ 12 ·k l (x i ,x j )+δ 13 ·k P (x i ,x j );

[0113] Where δ 10 ~δ 13 is the covariance weight of different kernel functions.

[0114] S33. Use fusion kernel function to predict blasting effect; predict over-excavation and under-excavation, cycle footage, half-hole ratio of light blasting holes, and block size after blasting of the face.

[0115] S34. Optimize and adjust the blasting design according to the predicted blasting effect: determine the constraints on the blasting effect parameters, such as over-excavation and under-excavation, half-hole ratio and cycle footage, and optimize the blasting design by iterating the blasting effect prediction model to form a better solution.

[0116] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A tunnel intelligent blasting design method based on geological sketch, characterized by: include: S1. Obtain relevant data of the tunnel face, drilling parameters, explosive parameters, and blasting control requirements, and generate a two-dimensional model of the tunnel face; S2. Calculate blasting parameters based on the two-dimensional model of the tunnel face, drilling parameters, explosive parameters, and blasting control requirements; S3. Predict blasting effects and optimize and adjust blasting design based on the predicted blasting effects.

2. The tunnel intelligent blasting design method based on geological sketch according to claim 1 is characterized by: Step S1 includes: S11. Obtaining tunnel face related data, including tunnel excavation section parameters, tunnel face photos, tunnel face excavation methods, and geological parameters; S12, generating a tunnel profile according to tunnel excavation section parameters, and dividing the tunnel face into regions according to the excavation method; S13. Identify rock joints and cracks in the photograph of the tunnel face and generate corresponding ones in the tunnel outline.

3. The tunnel intelligent blasting design method based on geological sketch according to claim 2 is characterized by: Step S2 includes: S21. Calculate the blasting unit consumption and the number of blast holes; S22. Arrange blast holes according to the number of blast holes, the area of ​​the tunnel face, and blast control requirements; S23. Determine the charge structure of the blast hole.

4. The tunnel intelligent blasting design method based on geological sketch according to claim 3 is characterized by: Step S21 includes: blasting unit consumption Where, q-explosive consumption, kg / m 3 , f- rock strength coefficient, value is Rc / 10, Rc unit is MPa, p is the explosive force, mL, S- cross-sectional area, m 2 , k is the empirical coefficient.

5. The tunnel intelligent blasting design method based on geological sketch according to claim 4 is characterized by: Step S22 includes: the number of blastholes Where N is the number of blastholes, excluding the number of empty holes without charge; α is the charging coefficient, i.e. the ratio of the length of charge to the total length of the blasthole; γ is the mass of explosive per meter of cartridge, kg / m.

6. The tunnel intelligent blasting design method based on geological sketch according to claim 3 is characterized by: In step S22, the arrangement positions of the blastholes need to avoid rock joints and fissures.

7. The tunnel intelligent blasting design method based on geological sketch according to claim 3 is characterized by: In step S23, the blasthole charge structure includes the uncoupling coefficient, charge length, plugging length and profile requirements.

8. The tunnel intelligent blasting design method based on geological sketch according to claim 1 is characterized by: Step S3 includes: S31, establishing a mapping relationship between blasting parameters and blasting effect parameters; S32, combining kernel functions with different characteristics to obtain a fused kernel function; S33, using the fusion kernel function to predict the blasting effect; S34. Optimize and adjust the blasting design based on the predicted blasting effect.

9. The tunnel intelligent blasting design method based on geological sketch according to claim 8 is characterized by: In step S32, the RBF kernel function, the Matérn kernel function, the linear kernel function, and the polynomial kernel function are combined to form a fusion kernel function; Among them, the RBF kernel function is used to extract local features of data, and its covariance function is: The Matérn kernel function is a generalization of the RBF kernel and is also used to extract local correlation characteristics. Its covariance function is: The linear kernel function is used to describe the linear correlation between data, and the covariance function is: The polynomial kernel function is used to describe the nonlinear relationship between data, and the covariance function is: Where δ1~δ9 are the kernel function hyperparameters that need to be set; The above kernel functions form a fusion kernel function in the following way: k M (x i ,x j )=δ 10 ·k RBF (x i ,x j )+δ 11 ·k Matern (x i ,x j )+δ 12 ·k l (x i ,x j )+δ 13 ·k P (x i ,x j ); Where δ 10 ~δ 13 is the covariance weight of different kernel functions.

10. The tunnel intelligent blasting design method based on geological sketch according to claim 8 is characterized by: Step S33 includes: predicting the over-excavation and under-excavation, cycle footage, smooth blast hole half-porosity, and block size after the tunnel face blasting.

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