Method for optimizing blasting parameters of saturated zone low-temperature rock mass based on prediction model

Abstract

The invention discloses a prediction model-based saturated zone low-temperature rock mass blasting parameter optimization method, which comprises the following steps of S1, data acquisition and sample preparation: acquiring a rock mass sample, and obtaining rock mass mechanical parameters and energy evolution data through an indoor static test and a dynamic test; according to the method, the blasting parameters are optimized by establishing the prediction model, so that the blasting lumpiness of the saturated zone low-temperature rock mass is more reasonable, and the shoveling efficiency is improved; on the basis of an explosibility grading system and a blasting parameter optimization model, parameters such as explosive unit consumption can be reasonably determined, waste of explosives is avoided, and the blasting production cost is reduced; the boulder rate and the root rate are effectively reduced, and the blasting quality is improved; the method can be popularized in mine engineering projects, and technical reference is provided for similar projects; the intrinsic safety of on-site blasting construction is improved by reducing the boulder rate and the flying stone distance of a blasting site.

Description

Technical Field

[0001] The present invention relates to the technical field of cold region geotechnical engineering blasting, and in particular to a method for optimizing blasting parameters of saturated water zone low-temperature rock mass based on a prediction model. Background Art

[0002] In cold-region projects, blasting of low-temperature rock in saturated zones is affected by factors such as temperature, moisture content, and porosity. This leads to problems such as a high rate of large blocks, a high rate of root deposits, and low energy efficiency. Existing technologies lack quantitative correlation analysis between the dynamic mechanical properties of rock masses in low-temperature environments and blasting parameters. This results in blasting parameter optimization relying on empirical adjustments, making precise control difficult. To better address these issues, we propose a method for optimizing blasting parameters for low-temperature rock in saturated zones based on a predictive model. Summary of the Invention

[0003] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method for optimizing blasting parameters of low-temperature rock mass in a saturated zone based on a prediction model.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for optimizing blasting parameters of low-temperature rock mass in a saturated zone based on a prediction model comprises the following steps:

[0006] S1. Data collection and sample preparation: Collect rock samples and obtain rock mechanical parameters and energy evolution data through indoor static and dynamic tests;

[0007] S2. Constructing an explosiveness classification system: Based on cluster analysis and multivariate regression, and integrating rock wave velocity, wave impedance, strain rate, energy absorption ratio and other indicators, a calculation formula for the explosiveness index of saturated low-temperature rock mass is established: N = α·Vp+β·Z+γ·ε˙+δ·Ea;

[0008] Where Vp is the wave velocity, Z is the wave impedance, ε˙ is the strain rate, Ea is the energy absorption rate, and α, β, γ, and δ are weight coefficients;

[0009] S3. Establish a blasting parameter prediction model: Use the error back propagation model to build a blasting parameter optimization model. The input parameters include temperature, moisture content, porosity, and blastability index. The output parameters are explosive consumption, hole spacing, and delay time. The model is trained using field blasting test data to optimize network weights and thresholds.

[0010] S4. On-site application and dynamic adjustment: Blasting design is carried out according to the parameters output by the prediction model. Through high-speed photography and three-dimensional laser scanning, the blasting effect is monitored in real time, and the model parameters are corrected through feedback to dynamically optimize the blasting parameters.

[0011] Preferably, in step S1, the temperature of the rock sample includes 10°C, 0°C, -20°C, and -40°C, and the porosity of the rock sample includes 2%, 8%, 15%, and 25%.

[0012] Preferably, in step S1, the indoor static test includes a uniaxial compression test, a triaxial compression test, and a Brazilian splitting test.

[0013] Preferably, in step S1, the dynamic test is a split-Hopkinson pressure bar impact test.

[0014] Preferably, in step S1, the rock mass mechanical parameters include elastic modulus, peak stress, and damage coefficient.

[0015] Preferably, in step S3, the blasting effect includes the bulk rate, the root rate, and the shoveling efficiency.

[0016] Preferably, the uniaxial compression test uses a TAJW-2000 rock triaxial press to obtain the full stress-strain curve and uniaxial compressive strength, and the Brazilian splitting test determines the tensile strength of the rock mass by an indirect tensile method with a loading rate of 0.15 mm / min.

[0017] Preferably, in step S3, the monitoring means of the on-site blasting test include a high-speed camera, a three-dimensional laser scanner, and a geological radar. The high-speed camera records the expansion process of the blasting cracks, the three-dimensional laser scanner obtains the blast pile size distribution data, and the geological radar detects the foundation formation.

[0018] A method for optimizing blasting parameters of low-temperature rock mass in a saturated zone based on a prediction model comprises the following steps:

[0019] S1. Data collection and sample preparation: Collect rock samples and obtain rock mechanical parameters and energy evolution data through indoor static and dynamic tests;

[0020] S2. Constructing an explosiveness classification system: Based on cluster analysis and multivariate regression, and integrating rock wave velocity, wave impedance, strain rate, energy absorption ratio and other indicators, a calculation formula for the explosiveness index of saturated low-temperature rock mass is established: N = α·Vp+β·Z+γ·ε˙+δ·Ea;

[0021] Where Vp is the wave velocity, Z is the wave impedance, ε˙ is the strain rate, Ea is the energy absorption rate, and α, β, γ, and δ are weight coefficients;

[0022] S3. Establish a blasting parameter prediction model: Use the error back propagation model to build a blasting parameter optimization model. The input parameters include temperature, moisture content, porosity, and blastability index. The output parameters are explosive consumption, hole spacing, and delay time. The model is trained using field blasting test data to optimize network weights and thresholds.

[0023] S4. On-site application and dynamic adjustment: Blasting design is carried out according to the parameters output by the prediction model. Through high-speed photography and three-dimensional laser scanning, the blasting effect is monitored in real time, and the model parameters are corrected through feedback to dynamically optimize the blasting parameters.

[0024] Preferably, in step S1, the temperature of the rock sample includes 10°C, 0°C, -20°C, and -40°C, and the porosity of the rock sample includes 2%, 8%, 15%, and 25%.

[0025] Preferably, in step S1, the indoor static test includes a uniaxial compression test, a triaxial compression test, and a Brazilian splitting test.

[0026] Preferably, in step S1, the dynamic test is a split-Hopkinson pressure bar impact test.

[0027] Preferably, in step S1, the rock mass mechanical parameters include elastic modulus, peak stress, and damage coefficient.

[0028] Preferably, in step S3, the blasting effect includes the bulk rate, the root rate, and the shoveling efficiency.

[0029] Preferably, the uniaxial compression test uses a TAJW-2000 rock triaxial press to obtain the full stress-strain curve and uniaxial compressive strength, and the Brazilian splitting test determines the tensile strength of the rock mass by an indirect tensile method with a loading rate of 0.15 mm / min.

[0030] Preferably, in step S3, the monitoring means of the on-site blasting test include a high-speed camera, a three-dimensional laser scanner, and a geological radar. The high-speed camera records the expansion process of the blasting cracks, the three-dimensional laser scanner obtains the blast pile size distribution data, and the geological radar detects the foundation formation.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. In the present invention, by establishing a prediction model to optimize the blasting parameters, the blasting block size of the low-temperature rock mass in the saturated zone can be made more reasonable, thereby improving the shoveling efficiency.

[0033] 2. In the present invention, based on the explosiveness classification system and the blasting parameter optimization model, parameters such as explosive consumption can be reasonably determined, thereby avoiding waste of explosives and reducing blasting production costs.

[0034] 3. In the present invention, the large block rate and the root rate are effectively reduced, and the blasting quality is improved. Through this method, the large block rate of blasting of low-temperature rock in the saturated zone can be reduced from the current 8% to below 5%, and the root rate can be controlled from 6% to within 1%, thereby reducing the need for secondary blasting and reducing related costs and construction risks.

[0035] 4. The present invention can be promoted in mining engineering projects, provide technical references for similar projects, and promote the technical development of engineering blasting industry in construction operations under complex environments.

[0036] 5. In the present invention, by reducing the large block rate and flying rock distance at the blasting site, the inherent safety of on-site blasting construction is improved, providing technical support for the high-end, intelligent and green development of mine blasting construction, and promoting the improvement of the inherent safety of construction operations in complex environments in the engineering blasting industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention provides a flow chart of a method for optimizing blasting parameters of low-temperature rock mass in a saturated zone based on a prediction model. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0039] Reference Figure 1 A method for optimizing blasting parameters of low-temperature rock mass in a saturated zone based on a prediction model comprises the following steps:

[0040] S1. Data collection and sample preparation: Collect rock samples and obtain rock mechanical parameters and energy evolution data through indoor static and dynamic tests;

[0041] S2. Constructing an explosiveness classification system: Based on cluster analysis and multivariate regression, and integrating rock wave velocity, wave impedance, strain rate, energy absorption ratio and other indicators, a calculation formula for the explosiveness index of saturated low-temperature rock mass is established: N = α·Vp+β·Z+γ·ε˙+δ·Ea;

[0042] Where Vp is the wave velocity, Z is the wave impedance, ε˙ is the strain rate, Ea is the energy absorption rate, and α, β, γ, and δ are weight coefficients;

[0043] S3. Establish a blasting parameter prediction model: Use the error back propagation model to build a blasting parameter optimization model. The input parameters include temperature, moisture content, porosity, and blastability index. The output parameters are explosive consumption, hole spacing, and delay time. The model is trained using field blasting test data to optimize network weights and thresholds.

[0044] S4. On-site application and dynamic adjustment: Blasting design is carried out according to the parameters output by the prediction model. Through high-speed photography and three-dimensional laser scanning, the blasting effect is monitored in real time, and the model parameters are corrected through feedback to dynamically optimize the blasting parameters.

[0045] In step S1 , the temperature of the rock sample includes 10° C., 0° C., −20° C., and −40° C., and the porosity of the rock sample includes 2%, 8%, 15%, and 25%.

[0046] In step S1, the indoor static tests include uniaxial compression test, triaxial compression test, and Brazilian split test.

[0047] In step S1 , the dynamic test is a split-Hopkinson pressure bar impact test.

[0048] In step S1, the rock mass mechanical parameters include elastic modulus, peak stress, and damage coefficient.

[0049] In step S3, the blasting effect includes the large block rate, the root rate, and the shoveling efficiency.

[0050] The uniaxial compression test used a TAJW-2000 rock triaxial press to obtain the full stress-strain curve and uniaxial compressive strength. The Brazilian splitting test determined the tensile strength of the rock mass by the indirect tensile method with a loading rate of 0.15 mm / min.

[0051] In step S3, the monitoring means of the on-site blasting test include high-speed photography, three-dimensional laser scanners, and geological radars. The high-speed photography records the expansion process of blasting cracks, the three-dimensional laser scanner obtains the blast pile size distribution data, and the geological radar detects the foundation formation.

[0052] This solution has the following technical advantages: 1. Improve blasting efficiency: By establishing a prediction model to optimize the blasting parameters, the blasting block size of the saturated low-temperature rock mass can be made more reasonable, thereby improving the shoveling efficiency. 2. Reduce blasting costs: Based on the explosiveness classification system and the blasting parameter optimization model, parameters such as the unit consumption of explosives can be reasonably determined to avoid waste of explosives and reduce blasting production costs. 3. Improve blasting effect: Effectively reduce the large block rate and the root rate, and improve the blasting quality. This method can reduce the large block rate of the saturated low-temperature rock mass blasting from the current 8% to below 5%, and the root rate from 6% to within 1%, thereby reducing the need for secondary blasting and reducing related costs and construction risks. 4. Provide technical support and reference: It can be promoted in mining engineering projects, provide technical references for similar projects, and promote the technical development of the engineering blasting industry in construction operations under complex environments. 5. Improve the inherent safety of construction: Reduce the rate of large blocks and the distance of flying rocks at the blasting site, improve the inherent safety of on-site blasting construction, provide technical support for the high-end, intelligent and green development of mine blasting construction, and promote the improvement of the inherent safety of construction operations in complex environments in the engineering blasting industry.

[0053] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for optimizing blasting parameters of low-temperature rock mass in saturated water zone based on a prediction model, characterized in that: The following steps are involved: S1. Data collection and sample preparation: Collect rock samples and obtain rock mechanical parameters and energy evolution data through indoor static and dynamic tests; S2. Constructing an explosiveness classification system: Based on cluster analysis and multivariate regression, and integrating rock wave velocity, wave impedance, strain rate, energy absorption ratio and other indicators, a calculation formula for the explosiveness index of saturated low-temperature rock mass is established: N = α·Vp+β·Z+γ·ε˙+δ·Ea; Where Vp is the wave velocity, Z is the wave impedance, ε˙ is the strain rate, Ea is the energy absorption rate, and α, β, γ, and δ are weight coefficients; S3. Establish a blasting parameter prediction model: Use the error back propagation model to build a blasting parameter optimization model. The input parameters include temperature, moisture content, porosity, and blastability index. The output parameters are explosive consumption, hole spacing, and delay time. The model is trained using field blasting test data to optimize network weights and thresholds. S4. On-site application and dynamic adjustment: Blasting design is carried out according to the parameters output by the prediction model. Through high-speed photography and three-dimensional laser scanning, the blasting effect is monitored in real time, and the model parameters are corrected through feedback to dynamically optimize the blasting parameters.

2. The method for optimizing blasting parameters of saturated low-temperature rock mass based on a prediction model according to claim 1, characterized in that: In step S1, the temperature of the rock sample includes 10° C., 0° C., -20° C., and -40° C., and the porosity of the rock sample includes 2%, 8%, 15%, and 25%.

3. The method for optimizing blasting parameters of low-temperature rock mass in a saturated zone based on a prediction model according to claim 1, characterized in that: In step S1, the indoor static test includes a uniaxial compression test, a triaxial compression test, and a Brazilian split test.

4. The method for optimizing blasting parameters of saturated low-temperature rock mass based on a prediction model according to claim 1, characterized in that: In step S1, the dynamic test is a split-Hopkinson pressure bar impact test.

5. The method for optimizing blasting parameters of low-temperature rock mass in a saturated zone based on a prediction model according to claim 1, characterized in that: In step S1, the rock mass mechanical parameters include elastic modulus, peak stress, and damage coefficient.

6. The method for optimizing blasting parameters of saturated low-temperature rock mass based on a prediction model according to claim 1, characterized in that: In step S3, the blasting effect includes the bulk rate, the root rate, and the shoveling efficiency.

7. The method for optimizing blasting parameters of low-temperature rock mass in a saturated zone based on a prediction model according to claim 3, characterized in that: The uniaxial compression test uses a TAJW-2000 rock triaxial press to obtain the full stress-strain curve and uniaxial compressive strength. The Brazilian splitting test measures the tensile strength of the rock mass by an indirect tensile method at a loading rate of 0.15 mm / min.

8. The method for optimizing blasting parameters of saturated low-temperature rock mass based on a prediction model according to claim 1, characterized in that: In step S3, the monitoring means of the on-site blasting test include a high-speed camera, a three-dimensional laser scanner, and a geological radar. The high-speed camera records the expansion process of the blasting cracks, the three-dimensional laser scanner obtains the blast pile size distribution data, and the geological radar detects the foundation formation.

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