Blasting vibration simulation wave optimization method and system

Abstract

The invention provides a blasting vibration simulation wave optimization method and system, and the method comprises the steps: obtaining a blasting vibration actual measurement waveform and an initial model parameter, inputting the initial model parameter to a pre-constructed single-hole blasting vibration prediction model, and obtaining an output waveform; and acquiring optimal model parameters by adopting a plurality of experimental design methods based on the actually measured waveform and the output waveform, and outputting an optimized blasting vibration simulation wave according to the optimal model parameters. According to the blasting vibration simulation wave optimization method provided by the invention, blasting vibration waves with relatively high field fitting degree can be quickly generated, the output peak vibration velocity, duration and dominant frequency have relatively high goodness of fit with field actually measured waveforms, and based on theoretical derivation, the inaccuracy caused by dependence on empirical formulas in the prior art is overcome.

Description

Technical Field

[0001] The invention relates to the technical field of blasting engineering, and in particular to a blasting vibration simulation wave optimization method and system. Background Art

[0002] In step blasting, tunnel excavation, and demolition blasting, the blasting vibration effect is the most harmful of all. When the blasting vibration reaches the critical damage value of the surrounding structures of the building or rock mass, it will cause damage to the building or rock mass structure, resulting in casualties and property losses. Only by accurately predicting the blasting vibration can the blasting vibration be controlled and the damage caused by the vibration effect be reduced. The existing blasting vibration simulation wave output system has the following defects:

[0003] 1) There is no software or program that outputs non-stationary blasting vibration waveform in the blasting direction;

[0004] 2) People often output blasting vibration waves and obtain vibration wave attribute values ​​(peak vibration velocity, main frequency and other predicted values) based on empirical formulas, lacking a rigorous theoretical derivation process, and the errors in the obtained waveform attributes are large;

[0005] 3) The general empirical formula requires a large number of field tests to fit the position parameter values ​​in the test, which has great limitations and it is difficult to accurately output the blasting vibration wave properties for a small number of tests. Summary of the invention

[0006] The purpose of the present invention is to provide a blasting vibration simulation wave optimization method and system, aiming to solve the above-mentioned problems in the prior art.

[0007] An embodiment of the present invention provides a method for optimizing a blasting vibration simulation wave, comprising:

[0008] Acquire the measured blasting vibration waveform and initial model parameters, input the initial model parameters into a pre-built single-hole blasting vibration prediction model to obtain an output waveform; and

[0009] Based on the measured waveform and the output waveform, a variety of experimental design methods are used to obtain optimal model parameters, and optimized blasting vibration simulation waves are output according to the optimal model parameters.

[0010] An embodiment of the present invention provides a blasting vibration simulation wave optimization system, comprising:

[0011] An output module, used to obtain the measured blasting vibration waveform and initial model parameters, and input the initial model parameters into a pre-built single-hole blasting vibration prediction model to obtain an output waveform; and

[0012] The optimization module is used to obtain the optimal model parameters by adopting a variety of experimental design methods based on the measured waveform and the output waveform, and output the optimized blasting vibration simulation wave according to the optimal model parameters.

[0013] An embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned blasting vibration simulation wave optimization method when executed by the processor.

[0014] An embodiment of the present invention further provides a computer-readable storage medium, on which a program for implementing information transmission is stored, and when the program is executed by a processor, the steps of the above-mentioned blasting vibration simulation wave optimization method are implemented.

[0015] The use of the embodiments of the present invention can include the following beneficial effects: the blasting vibration simulation wave optimization method proposed in the embodiments of the present invention can quickly generate blasting vibration waves that have a high degree of fit with the site, and the output peak vibration velocity, duration, and main frequency have a high degree of consistency with the waveforms measured on site, and are based on theoretical derivation, overcoming the inaccuracy of the prior art that relies on empirical formulas. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 is a flow chart of a method for optimizing blasting vibration simulation waves according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of the imported measured waveforms of an embodiment of the present invention;

[0019] Figure 3 2 is a schematic diagram of a program for compiling measured waveforms according to an embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of comparison results between a simulated waveform and a measured waveform according to an embodiment of the present invention;

[0021] Figure 5 It is a schematic diagram of a blasting vibration simulation wave optimization system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0023] Method Embodiment

[0024] According to an embodiment of the present invention, a method for optimizing blasting vibration simulation waves is provided. Figure 1 is a flow chart of the blasting vibration simulation wave optimization method according to an embodiment of the present invention, such as Figure 1 As shown, the blasting vibration simulation wave optimization method according to an embodiment of the present invention specifically includes:

[0025] Step S101, obtaining the measured waveform of blasting vibration and initial model parameters, and inputting the initial model parameters into a pre-built single-hole blasting vibration prediction model to obtain an output waveform;

[0026] Step S102, based on the measured waveform and the output waveform, a plurality of experimental design methods are used to obtain optimal model parameters, and an optimized blasting vibration simulation wave is output according to the optimal model parameters, specifically including:

[0027] Calculate the attribute value of the output waveform, obtain the residual similarity threshold by comparing the measured waveform with the attribute value of the output waveform, and perform a single-factor change test on the model parameters to obtain the variation law between the model parameters and the waveform attributes, and obtain a better model parameter interval by trial and error method according to the variation law, and divide the better model parameter interval into equal intervals based on the residual similarity threshold as the evaluation standard, and obtain better model parameters by orthogonal test design for the divided model parameters according to the evaluation standard, and obtain the optimal model parameters of the single-hole blasting vibration prediction model by using range analysis for the better model parameters;

[0028] The step of calculating the attribute value of the output waveform specifically includes:

[0029] Calculating the expected attribute of the output waveform, and using the expected attribute as the attribute value of the output waveform;

[0030] The attribute values ​​include peak time, peak vibration speed, duration and main frequency.

[0031] The above technical solution of the embodiment of the present invention is described in detail below in combination with the specific situation of the blasting vibration simulation wave optimization method of the embodiment of the present invention.

[0032] The technical problem solved by the embodiment of the present invention is how to simulate a waveform similar to the waveform actually measured on site, so the simulation program of the blasting vibration wave includes:

[0033] 1. Construct a single-hole blasting vibration prediction model;

[0034] 2. Import waveform;

[0035] 3. Confirmation of the number of waveform outputs by the model under fixed parameters; specifically, the output waveform attribute values ​​(peak velocity, peak time, duration, and main frequency) of the prediction model under fixed model parameters are random, so the method of outputting multiple sets of waveforms is used to determine the attribute values ​​of the output waveforms of the prediction model: the expectations of multiple sets of waveforms are used as the waveform attribute values ​​of this set of model parameters. After verifying that all parameter models output 500 sets of waveforms, the attribute values ​​of the 500 sets of waveforms are counted, and the characteristic distribution of the waveform attribute values ​​is similar;

[0036] 4. Set the residual similarity to quantify the similarity between the two waveforms;

[0037] 5. Determine the approximate range of model parameters by trial and error method; specifically, under the premise of the same group of model parameters, change the model parameters by a single factor to obtain the influence of each model parameter on the model attributes; after determining the effect of each model parameter, use the trial and error method to obtain the approximate model parameter range; Introduction to trial and error method: first determine the relationship between the model output waveform attributes and the model parameters, and extract the attributes of the field measured waveform, and infer the approximate range of the model parameters through the attributes of the field measured waveform;

[0038] 6. Determine the optimal model parameters by orthogonal design; specifically, the following includes: on the basis of the trial and error method, using residual similarity as the evaluation standard, and adopting orthogonal design to obtain the optimal model parameters within the interval; that is, after determining the model parameter interval, divide each parameter interval into equal intervals. The orthogonal test of the embodiment of the present invention has 5 levels, and the interval is divided into 4 equal intervals. The upper and lower limits of each interval, a total of 5 data, are used as the levels of the orthogonal test. For example: the parameter interval [30, 70] is divided into intervals [30, 40], [40, 50], [50, 60], [60, 70], so the 5 levels are: 30, 40, 50, 60, 70;

[0039] 7. Use range analysis to obtain the optimal model parameters within the interval.

[0040] In summary, the technical problem solved by the embodiments of the present invention is how to simulate a waveform similar to the single-hole waveform measured on site, so as to accurately simulate the seismic wave waveform generated under the blasting parameters and geological conditions with fewer field tests. The seismic wave simulation program includes: importing the waveform, importing the blasting vibration wave monitoring data into the simulation program; setting the waveform residual similarity to quantitatively describe the similarity between the two waveforms; constructing a blasting vibration wave model, and obtaining the influence of each model parameter on the waveform attributes (peak time, peak vibration velocity, duration, main frequency), and on the basis of the above, using the trial and error method to obtain the approximate model parameter range, using the orthogonal experimental design to further obtain the model's better parameters, and using the range analysis on the basis of the orthogonal design to further obtain the optimal model parameters; then using the optimal model parameters to output the waveform as the simulation waveform for the field test. The specific implementation process is as follows:

[0041] 1. Constructing a blasting vibration prediction model

[0042] The frequency equation is constructed according to the process of modulating white noise, the gamma function is used to construct the intensity equation, the attenuation function is used to represent the non-stationarity of the frequency, and the blasting vibration prediction model describing the non-stationarity of blasting vibration is constructed by combining the frequency equation and the intensity equation.

[0043]

[0044] ω(τ)=ηe -γτ (4)

[0045] Where X(t) represents the prediction model, A(t) represents the intensity equation, x(t) represents the frequency equation, I0 represents the intensity value, α and β represent the gamma function parameters, η represents the initial angular frequency, γ represents the attenuation coefficient, ξ represents the damping ratio, S represents the power spectrum density, and h[t-τ,λ(τ)] represents the modified unit impact velocity attenuation equation. represents the intermediate variable, that is, the variance of the vibration equation, t represents the time variable, unit is / s, e represents the natural constant, which is taken as 2.718, f(t) represents the white noise sequence impact equation, τ represents the relative time variable, w(τ) represents the white noise impact sequence, λ(τ) represents the time variable and the relative time variable participate in the operation together, ω(τ) represents the frequency attenuation equation.

[0046] 2. Import waveform

[0047] The prediction model is based on the MATLAB platform to complete data import: the blasting vibration wave curve of the field measured waveform is imported into the MATLAB calculation program, and the field measured waveform is output after importing the program. Figure 2 As shown, specifically including:

[0048] 1. Export the blasting vibration waveform as an xlsx document;

[0049] 2. Place the xlsx document and the blasting vibration prediction model program in the default path of MATLAB;

[0050] 3. Compile and import the program, load the waveform (Note: the xlsx file should contain the time variable and the waveform velocity, the xlsx file name and the default path of matlab are in English), such as Figure 3 shown.

[0051] 3. Confirmation of the number of waveforms output by the model under fixed parameters

[0052] The randomness of white noise leads to the randomness of the waveform output by the same group of model parameters. Therefore, the attribute value of the waveform output by one model parameter cannot be used as the attribute value of the waveform output by this group of model parameters. According to the law of large numbers, as the number of repeated tests increases, the frequency of events will approach probability. Therefore, if the same group of model parameters output multiple waveforms, as long as the distribution of the attribute of the model output waveform is determined to be fixed, the expectation of multiple groups of waveforms can be used as the waveform attribute under this model parameter. Study how many groups of waveforms to output to make the distribution fixed: output 2 groups of 200 waveforms, 2 groups of 500 waveforms, and 2 groups of 700 waveforms from the same group of model parameters (as shown in Table 1); extract the peak time, peak vibration velocity, duration, and main frequency of each group of waveforms; use the KS double test to test whether the characteristic distribution of each group of waveforms is similar. Substitute the two groups of data into the KS double test. According to the provisions of the KS double test, the closer the p value is to 1, the more similar the distribution of the two groups of data is. Therefore, the KS-double test is used to test the distribution similarity of the three groups of two different times.

[0053]

[0054] in:

[0055]

[0056] In the formula, x represents the sample to be tested, x(i) represents the i-th sample, j represents the cumulative summation operation subscript, m represents the number of sample 1, n represents the number of sample 2, and F 1m represents the empirical distribution function of sample 1, F 2n represents the distribution function of sample 2 experience, D mn Indicates F 1m (x) and F 2n The maximum absolute value of the difference between (x), N e represents intermediate variable 1, the expression is controlled by variables m and n, λ represents intermediate variable 2, the expression is controlled by variable D mn With N e control.

[0057] Table 1 Mean values ​​of waveform attributes and KS double test P values ​​for different number of cyclic tests

[0058]

[0059] It can be seen from Table 1 that although 2 rounds of 700 tests can ensure that the p value is 0.95 or above, the increase in the p value is not much compared with 2 rounds of 500 tests. Therefore, under the premise of ensuring the accuracy of the computer, in order to shorten the calculation time, the attribute expectation of the waveform output by the model parameter 500 times is determined as the attribute value of the model parameter output waveform.

[0060] 4. Set the residual similarity to quantify the similarity between two waveforms

[0061] The calculation formula of residual similarity is as follows:

[0062]

[0063] in,

[0064] In the formula, x i , They represent the corresponding amplitudes of the measured blasting signal and the simulated blasting signal at the i-th time point, n represents the signal length, σ i represents the credibility of the i-th point, α i represents the weight of the i-th point, C E The residual credibility quantification index representing the simulated signal and the measured signal is called residual similarity. Combining the above formula, it can be seen that the higher the simulated waveform similarity, the closer its value is to 1.

[0065] The single-hole blasting vibration prediction model constructed by the embodiment of the present invention outputs 8 groups of simulated waveforms, compares the simulated waveforms with a group of measured waveforms, and counts the errors with the largest peak values ​​of the 5 peaks in each group of simulated waveforms and the measured waveforms. The statistical results are shown in Table 2. It can be seen from Table 1 that the residual similarity C E The larger the value, the smaller the average value of each waveform error. The comparison results based on the simulation curve and the field measured curve are as follows: Figure 4 As shown in Table 2, when the mean error between the peaks is not greater than 20%, the simulated waveform is considered to be similar to the measured waveform. E =0.40, the mean values ​​of the peak errors of the two waveforms are 19.6% and 19.8% respectively, so the embodiment of the present invention selects C E = 0.4 as the threshold of residual similarity.

[0066] Table 2 Error, average error, and residual similarity of the five largest peaks between the eight groups of simulated waveforms and the field measured waveforms

[0067]

[0068] 5. Trial and error method to determine the approximate range of the model

[0069] On the basis of step 4, the parameters I0=2000, α=3.5, β=20, η=150, γ=2, ξ=0.1 are used, and the model parameters are changed by 20%, 40%, 60%, and 80% to study the change of waveform attributes with the model parameters, as shown in Table 3;

[0070] Table 3 Changes of waveform attributes with model parameters

[0071]

[0072] Note: This process only explores the effect of model parameter changes on waveform properties, laying the foundation for trial and error.

[0073] 6. Orthogonal design to determine the optimal model parameters

[0074] The attribute value of a field-measured waveform is taken as the research object, and the attribute value of the field-measured waveform is extracted. According to the change law of waveform attributes with model parameters, the approximate interval of model parameters is inferred. The approximate interval is divided into the following standards at equal intervals, and an orthogonal experiment with 6 factors and 5 levels is designed, as shown in Table 4. The residual similarity between the output waveform of each group of parameters and the field-measured waveform is calculated, as shown in Table 5, and the value C with the largest residual similarity is selected. E =0.521 is taken as the optimal parameters of the model. The optimal parameters of the model are I0=30, α=1.87, β=21, η=252, γ=5.6, ξ=0.12.

[0075] Table 4 Orthogonal test of 6 factors and 5 levels

[0076] serial number <![CDATA[I0]]> a b η γ ξ 1 20.00 1.36 16.80 224.00 5.60 0.08 2 22.50 1.53 18.90 252.00 6.30 0.09 3 25.00 1.70 21.00 280.00 7.00 0.10 4 27.50 1.87 23.10 308.00 7.70 0.11 5 30.00 2.04 25.20 336.00 8.40 0.12

[0077] Table 5 Residual similarity between the output waveform of each set of parameters in the orthogonal test and the field measured waveform

[0078]

[0079]

[0080] 7. Determine the theoretical optimal model parameters by range analysis

[0081] Based on the orthogonal design, the range analysis is carried out to obtain the K value of the five parameters under the calculated I0, and the residual similarity under this parameter is compared. The I0 with the largest K value is selected, and the remaining parameters are similar. The obtained six parameters are used as the theoretical optimal model parameters, as shown in Table 6. The waveform output by the optimal model parameters simulates the waveform measured on site.

[0082] Table 6 Range Analysis

[0083]

[0084] System Example

[0085] According to an embodiment of the present invention, a blasting vibration simulation wave optimization system is provided. Figure 5 Schematic diagram of the blasting vibration simulation wave optimization system according to an embodiment of the present invention. Figure 5 As shown, the blasting vibration simulation wave optimization system according to an embodiment of the present invention specifically includes:

[0086] An output module 50 is used to obtain the measured blasting vibration waveform and initial model parameters, and input the initial model parameters into a pre-built single-hole blasting vibration prediction model to obtain an output waveform;

[0087] The optimization module 52 is used to obtain the optimal model parameters by using a variety of experimental design methods based on the measured waveform and the output waveform, and output the optimized blasting vibration simulation wave according to the optimal model parameters, specifically for:

[0088] Calculate the attribute value of the output waveform, obtain the residual similarity threshold by comparing the measured waveform with the attribute value of the output waveform, and perform a single-factor change test on the model parameters to obtain the variation law between the model parameters and the waveform attributes, and obtain a better model parameter interval by trial and error method according to the variation law, and divide the better model parameter interval into equal intervals based on the residual similarity threshold as the evaluation standard, and obtain better model parameters by orthogonal test design for the divided model parameters according to the evaluation standard, and obtain the optimal model parameters of the single-hole blasting vibration prediction model by using range analysis for the better model parameters;

[0089] wherein, calculating the expected attribute of the output waveform, and using the expected attribute as the attribute value of the output waveform;

[0090] The attribute values ​​include peak time, peak vibration speed, duration and main frequency.

[0091] The embodiment of the present invention is a system embodiment corresponding to the above-mentioned method embodiment. The specific operations of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0092] In summary, the embodiments of the present invention have the following beneficial effects:

[0093] 1) It can quickly generate blasting vibration waves that are highly compatible with the site;

[0094] 2) Based on theoretical derivation, it overcomes the inaccuracy of the existing technology that relies on empirical formulas;

[0095] 3) The output peak vibration velocity, duration, and main frequency are highly consistent with the waveform measured on site;

[0096] 4) Use multiple samples to eliminate model randomness.

[0097] Device Example 1

[0098] An embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps described in the method embodiment when executed by the processor.

[0099] Device Example 2

[0100] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by a processor, the steps described in the method embodiment are implemented.

[0101] The computer-readable storage medium in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk or optical disk, etc.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing blasting vibration simulation waves, characterized in that: The method is implemented based on a blasting vibration wave simulation program and includes: Acquire the measured blasting vibration waveform and initial model parameters, input the initial model parameters into a pre-built single-hole blasting vibration prediction model to obtain an output waveform; and Based on the measured waveform and the output waveform, a variety of experimental design methods are used to obtain optimal model parameters, and optimized blasting vibration simulation waves are output according to the optimal model parameters.

2. The method according to claim 1, characterized in that The optimal model parameters are obtained by using a variety of experimental design methods based on the measured waveform and the output waveform, specifically including: Calculate the attribute value of the output waveform, obtain the residual similarity threshold by comparing the attribute value of the measured waveform with the output waveform, and perform a single-factor change test on the model parameters to obtain the variation law between the model parameters and the waveform attributes. According to the variation law, a trial-and-error method is used to obtain a better model parameter interval. Taking the residual similarity threshold as the evaluation standard, the better model parameter interval is divided into equal intervals. The divided model parameters are subjected to orthogonal experimental design according to the evaluation standard to obtain better model parameters. The better model parameters are subjected to range analysis on the better model parameters to obtain the optimal model parameters of the single-hole blasting vibration prediction model.

3. The method according to claim 2, characterized in that Calculating the attribute value of the output waveform specifically includes: Calculate the expected attribute of the output waveform, and use the expected attribute as the attribute value of the output waveform.

4. The method according to claim 3, characterized in that The attribute values ​​include peak time, peak vibration speed, duration and main frequency.

5. A blasting vibration simulation wave generation system, characterized in that: The blasting vibration simulation wave optimization method used in any one of claims 1 to 4 comprises: An output module, used to obtain the measured blasting vibration waveform and initial model parameters, and input the initial model parameters into a pre-built single-hole blasting vibration prediction model to obtain an output waveform; and The optimization module is used to obtain the optimal model parameters by adopting a variety of experimental design methods based on the measured waveform and the output waveform, and output the optimized blasting vibration simulation wave according to the optimal model parameters.

6. The system according to claim 5, characterized in that The optimization module is specifically used for: Calculate the attribute value of the output waveform, obtain the residual similarity threshold by comparing the attribute value of the measured waveform with the output waveform, and perform a single-factor change test on the model parameters to obtain the variation law between the model parameters and the waveform attributes. According to the variation law, a trial-and-error method is used to obtain a better model parameter interval. Taking the residual similarity threshold as the evaluation standard, the better model parameter interval is divided into equal intervals. The divided model parameters are subjected to orthogonal experimental design according to the evaluation standard to obtain better model parameters. The better model parameters are subjected to range analysis on the better model parameters to obtain the optimal model parameters of the single-hole blasting vibration prediction model.

7. The system according to claim 6, characterized in that The optimization module is specifically used for: Calculate the expected attribute of the output waveform, and use the expected attribute as the attribute value of the output waveform.

8. The system according to claim 7, characterized in that The attribute values ​​include peak time, peak vibration speed, duration and main frequency.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the blasting vibration simulation wave optimization method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the blasting vibration simulation wave optimization method according to any one of claims 1 to 4 are implemented.

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