Early warning method for soil slope instability based on active waveguide acoustic emission technology

Abstract

The invention discloses a soil slope instability early warning method based on an active waveguide acoustic emission technology, which comprises the following steps of: drilling a hole in a potential sliding surface area of a soil slope, and arranging an active waveguide structure; based on an active waveguide structure, collecting an acoustic emission signal; performing parameter extraction and analysis on the acoustic emission signal; mutation analysis is carried out, and an early warning interval is constructed; and outputting multi-level early warning based on the early warning interval. According to the method, an active waveguide structure containing discrete filling particles is arranged in the soil slope, acoustic emission signals in the sliding deformation process of the slope are collected, the evolution law of acoustic emission parameters is analyzed, an early warning interval is built in combination with a gray-cuspidal point mutation model, and finally multi-level early warning of soil slope instability is achieved. The problems of large signal attenuation and low early warning precision in traditional slope monitoring are solved, the internal deformation precursor of the slope can be accurately captured, the instability risk is pre-judged in advance, and reliable technical support is provided for soil slope geological disaster prevention and control.

Description

Technical Field

[0001] This invention belongs to the field of slope geological disaster monitoring and early warning technology, specifically relating to a method for early warning of soil slope instability based on active waveguide acoustic emission technology. Background Technology

[0002] Soil slope instability is a common geological hazard in my country, characterized by its frequent and sudden occurrence, seriously threatening people's lives and property. Traditional slope monitoring methods, such as geodesy and InSAR technology, have limitations, including limited monitoring range, susceptibility to environmental interference, and difficulty in capturing early signs of internal deformation. Acoustic emission technology can provide early warning by monitoring elastic wave signals generated by the deformation and fracturing of soil and rock masses; however, traditional acoustic emission technology suffers from severe signal attenuation in soil slopes, limiting monitoring accuracy. Active waveguide acoustic emission technology reduces signal attenuation and enhances signal capture capabilities through waveguide structures; currently, however, there is a lack of systematic early warning methods based on this technology, making it difficult to meet practical engineering needs. Summary of the Invention

[0003] This invention is proposed to address the problems existing in the prior art, and its purpose is to provide a method for early warning of soil slope instability based on active waveguide acoustic emission technology.

[0004] The technical solution of this invention is: a method for early warning of soil slope instability based on active waveguide acoustic emission technology, comprising the following steps: A. Drill holes in the potential sliding surface area of ​​the soil slope and install source waveguide structures; B. Acquiring acoustic emission signals based on an active waveguide structure; C. Extract and analyze parameters from acoustic emission signals; D. Perform mutation analysis and construct early warning intervals; E. Based on the warning interval, output multi-level warnings.

[0005] Furthermore, in step A, boreholes are drilled in the potential sliding surface area of ​​the soil slope, and source waveguide structures are installed. The specific process is as follows: First, vertical boreholes are drilled in the potential sliding surface area of ​​the soil slope, with the borehole depth penetrating the potential sliding surface. Then, a waveguide rod, which is made of steel pipe, is installed inside the borehole; Finally, discrete particulate material is filled between the waveguide rod and the borehole wall to form an active waveguide structure.

[0006] Furthermore, two parallel planes are cut longitudinally at the top of the waveguide rod to install acoustic emission sensors, and Vaseline coupling agent is applied to the contact surface between the acoustic emission sensors and the waveguide rod.

[0007] Furthermore, step B, based on an active waveguide structure, acquires acoustic emission signals, as detailed below: First, connect the multi-channel acoustic emission device to the acoustic emission sensor; Then, acoustic emission signals generated by the mutual compression and friction of discrete particles during the slope sliding deformation process are collected in real time.

[0008] Furthermore, step C involves parameter extraction and analysis of the acoustic emission signal, the specific process of which is as follows: First, the collected acoustic emission signals are preprocessed to remove noise interference; Then, extract parameters such as acoustic emission count, cumulative AE count, main frequency domain distribution, and high-frequency band energy percentage. Finally, a quantitative relationship between the cumulative AE count and the sliding displacement is established through linear fitting.

[0009] Furthermore, the high-frequency band energy percentage parameter is calculated using the following formula: ; Among them, E 3j For high-frequency band component energy, E 0 This represents the total energy of the signal.

[0010] Furthermore, step D involves mutation analysis and the construction of warning intervals, as detailed below: First, generate the original sequence of cumulative AE counts; Then, a GM(1,1) grey prediction model is established, and the model parameters, namely the development coefficient a and the grey action quantity b, are solved by the least squares method to obtain the prediction sequence of the cumulative AE count. Next, a multi-order polynomial is used to fit the predicted sequence to determine whether there is a sudden change in the cumulative AE count and to determine the boundary of the sudden change in the cumulative AE count.

[0011] Furthermore, combining the high-frequency mutation characteristics of the acoustic emission signal in the main frequency domain, when a high-frequency signal of 300-350kHz continuously appears in the main frequency domain and the energy percentage of the high-frequency band increases to more than 20%, the high-frequency mutation boundary in the main frequency domain is determined; the two mutation boundaries constitute the early warning interval.

[0012] Furthermore, step E outputs multi-level early warnings based on the warning intervals, as detailed below: First, the early warning levels are classified based on the evolution characteristics of acoustic emission parameters; Then, when the acoustic emission count, amplitude, and energy begin to increase, and the signal points in the parameter correlation diagram expand, a blue warning is issued; Subsequently, a yellow warning is issued when continuous high-frequency signals appear in the main frequency domain; Subsequently, when the percentage of high-frequency band energy remains at a high level, an orange alert will be issued; Finally, a red alert is issued when the cumulative AE count sequence triggers the gray-cusp mutation model.

[0013] The beneficial effects of this invention are as follows: This invention utilizes an active waveguide structure containing discrete filling particles to collect acoustic emission signals during the slope sliding deformation process in soil slopes, analyzes the evolution law of acoustic emission parameters, and constructs an early warning interval by combining a gray-cusp catastrophe model, ultimately achieving multi-level early warning of soil slope instability.

[0014] This invention solves the problems of large signal attenuation and low early warning accuracy in traditional slope monitoring. It can accurately capture early signs of internal slope deformation and predict instability risks in advance, providing reliable technical support for the prevention and control of geological disasters on soil slopes. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the landslide monitoring method in this invention; Figure 2 This is a schematic diagram of the stress characteristics of the waveguide structure in this invention; Figure 3 This is a correlation diagram between the cumulative AE count and the displacement in this invention; Figure 4 This is a time-frequency signal characteristic diagram of acoustic emission in this invention; Figure 5 This is a graph showing the evolution of the percentage of energy in the high-frequency band of the acoustic emission signal in this invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings and embodiments: like Figures 1 to 5 As shown, a method for early warning of soil slope instability based on active waveguide acoustic emission technology includes the following steps: A. Drill holes in the potential sliding surface area of ​​the soil slope and install source waveguide structures; B. Acquiring acoustic emission signals based on an active waveguide structure; C. Extract and analyze parameters from acoustic emission signals; D. Perform mutation analysis and construct early warning intervals; E. Based on the warning interval, output multi-level warnings.

[0017] Step A involves drilling holes in the potential sliding surface area of ​​the soil slope and installing source waveguide structures. The specific process is as follows: First, vertical boreholes are drilled in the potential sliding surface area of ​​the soil slope, with the borehole depth penetrating the potential sliding surface. Then, a waveguide rod, which is made of steel pipe, is installed inside the borehole; Finally, discrete particulate material is filled between the waveguide rod and the borehole wall to form an active waveguide structure.

[0018] Two parallel planes are cut longitudinally at the top of the waveguide rod, and acoustic emission sensors are installed thereon. Vaseline coupling agent is applied to the contact surface between the acoustic emission sensors and the waveguide rod.

[0019] Step B involves acquiring acoustic emission signals based on an active waveguide structure. The specific process is as follows: First, connect the multi-channel acoustic emission device to the acoustic emission sensor; Then, acoustic emission signals generated by the mutual compression and friction of discrete particles during the slope sliding deformation process are collected in real time.

[0020] Step C involves parameter extraction and analysis of the acoustic emission signal. The specific process is as follows: First, the collected acoustic emission signals are preprocessed to remove noise interference; Then, extract parameters such as acoustic emission count, cumulative AE count, main frequency domain distribution, and high-frequency band energy percentage. Finally, a quantitative relationship between the cumulative AE count and the sliding displacement is established through linear fitting.

[0021] The specific formula for calculating the high-frequency band energy percentage parameter is as follows: ; Among them, E 3j For high-frequency band component energy, E 0 This represents the total energy of the signal.

[0022] Specifically, the quantization relationship between the cumulative AE count and the sliding displacement is as follows: ; Where N is the cumulative AE count; D is the sliding displacement (mm). α and β are empirical coefficients related to soil properties and waveguide structure, and are calibrated through experiments.

[0023] Step D involves mutation analysis and the construction of warning intervals. The specific process is as follows: First, generate the original sequence of cumulative AE counts; Then, a GM(1,1) grey prediction model is established, and the model parameters, namely the development coefficient a and the grey action quantity b, are solved by the least squares method to obtain the prediction sequence of the cumulative AE count. Next, a multi-order polynomial is used to fit the predicted sequence to determine whether there is a sudden change in the cumulative AE count and to determine the boundary of the sudden change in the cumulative AE count.

[0024] Based on the high-frequency mutation characteristics of the acoustic emission signal in the main frequency domain, when a high-frequency signal of 300-350kHz continuously appears in the main frequency domain and the energy percentage of the high-frequency band increases to more than 20%, the high-frequency mutation boundary in the main frequency domain is determined; the two mutation boundaries constitute the warning interval.

[0025] Step E outputs multi-level warnings based on the warning intervals. The specific process is as follows: First, the early warning levels are classified based on the evolution characteristics of acoustic emission parameters; Then, when the acoustic emission count, amplitude, and energy begin to increase, and the signal points in the parameter correlation diagram expand, a blue warning is issued; Subsequently, a yellow warning is issued when continuous high-frequency signals appear in the main frequency domain; Subsequently, when the percentage of high-frequency band energy remains at a high level, an orange alert will be issued; Finally, a red alert is issued when the cumulative AE count sequence triggers the gray-cusp mutation model.

[0026] Specifically, step D first generates the original sequence of cumulative AE counts, as follows: Grey prediction modeling for the cumulative AE count sequence: ; in, The original sequence; The sequence is generated by accumulation, where a and b are the development coefficient and the grey effect quantity, respectively. Perform an accumulation to generate a sequence ; ,in, iTable The cumulative AE count in the original sequence is shown as the number of AEs. i Data points, k This indicates the first element in the new sequence generated after one accumulation. k Data points.

[0027] Specifically, in step D, a GM(1,1) grey prediction model is established, and the model parameters, namely the development coefficient a and the grey action quantity b, are solved using the least squares method to obtain the prediction sequence of the cumulative AE count. The specific process is as follows: A GM(1,1) grey prediction model is established, and the model parameters, namely the development coefficient a and the grey action quantity b, are solved by the least squares method to obtain the cumulative AE count prediction sequence. .

[0028] Specifically, in step D, a multi-order polynomial is used to fit the predicted sequence to determine whether there is a sudden change in the cumulative AE count and to determine the boundary of the sudden change in the cumulative AE count. The specific process is as follows: The predicted sequence was fitted with a 5th-order polynomial and variable substitution was used. ,in t Represents the original variables, and the time parameters (or "state evolution parameters" of the cumulative AE count sequence) in the construction of the grey-cusp catastrophe model. s These are the transformed variables, i.e., the general state variables of the cusp catastrophe model. The offset coefficients are the translation compensation calculated based on 5th-order polynomial coefficients. This is then converted to the standard form of the cusp catastrophe model. Where p is the acoustic emission parameter of the entire system, representing the state variable time parameter; u and v can represent the control variables of the acoustic emission parameter time fitting sequence during the entire monitoring process; the constant term C is usually not considered. Calculate the bifurcation discriminant Δ = 8u³ + 27v². When Δ < 0, it is determined that a sudden change has occurred in the cumulative AE count, and the boundary of the cumulative AE count change is determined.

[0029] Specifically, in step A, boreholes are drilled in the potential sliding surface area of ​​the soil slope, and a source waveguide structure is installed. The specific process is as follows: First, drill vertical boreholes with a diameter of 80-100 mm in the potential sliding surface area of ​​the soil slope, with the borehole depth penetrating the potential sliding surface; Then, a waveguide rod is installed inside the borehole. The waveguide rod is made of steel pipe with a diameter of 28-32mm, an inner wall thickness of 4-6mm, and a length of 0.8-1.2m. Finally, discrete particulate material is filled between the waveguide rod and the borehole wall, using a layered compaction method, with each layer having a filling height of 8-12cm, to form an active waveguide structure.

[0030] Specifically, the discrete particulate material is glass sand with a particle size of 2-5 mm, a density of 2536 kg / m³, a filling density of 1600 kg / m³, and a porosity of 0.72. It needs to be compacted in layers, with each layer being 10 cm thick.

[0031] Specifically, step B involves acquiring acoustic emission signals based on an active waveguide structure, and the specific process is as follows: A PCI-2 type eight-channel acoustic emission device was used to connect the sensor. The sampling frequency was set to 1MHz, the sampling threshold to 40-50dB, and the preamplifier gain to 40dB. The acoustic emission signal generated by the mutual compression and friction of discrete particles during the slope sliding deformation was collected in real time. Simultaneously, a displacement meter was used to record the sliding displacement data, with a sampling interval of 5-15s.

[0032] Specifically, in step C, wavelet transform is used to decompose the acoustic emission signal into eight frequency bands, of which 312.5-500kHz is the high-frequency band, as shown in the following formula. ; Calculate the high-frequency band energy percentage, where E 3j For high-frequency band component energy, E0 This represents the total energy of the signal.

[0033] A quantitative relationship between cumulative AE count and sliding displacement was established through linear fitting, and the goodness of fit R0 was determined. 2 Not less than 0.98.

[0034] Specifically, the initial warning in step E is yellow, as detailed below: The cumulative AE count exceeds the baseline value by 200%; High-frequency energy percentage: 15%~20%; The parameter correlation graph has been expanded by 50%.

[0035] Specifically, in step E, the intermediate warning is orange, as detailed below: The slope of the cumulative AE count curve suddenly changes; High-frequency energy percentage: 20%~25%; High-frequency signals above 300kHz were continuously observed.

[0036] Specifically, the emergency warning in step E is marked in red, as detailed below: The cumulative AE count grows exponentially; High-frequency energy percentage > 25%; Specifically, when the main frequency domain of the signal expands to 300~350kHz and lasts for more than 5 minutes, it is determined to be the accelerated deformation stage.

[0037] Specifically, the quantitative relationship between displacement rate and acoustic emission count statistical parameters is as follows: ; in, V The displacement rate is (mm / min). For acoustic emission counting statistics parameters; k This is a proportionality coefficient, calibrated through experiments. Example

[0038] Taking a gradually changing soil slope as an example, the method of the present invention is applied for instability early warning. A method for early warning of soil slope instability based on active waveguide acoustic emission technology includes the following steps: A. Drill holes in the potential sliding surface area of ​​the soil slope and install a source waveguide structure. Active waveguide structure layout: Drill a vertical borehole with a diameter of 80mm in the potential sliding surface area of ​​the slope, with the borehole depth penetrating the potential sliding surface; select a steel pipe with a diameter of 30mm, an inner wall thickness of 5mm, and a length of 1.0m as a waveguide rod, and install it in the center of the borehole; fill the space between the waveguide rod and the borehole wall with glass sand (particle size 2-5mm), compact it in layers, with each layer having a filling height of 10cm; install a Nano-30 acoustic emission sensor on the top of the waveguide rod, and apply Vaseline coupling agent to the contact surface between the sensor and the waveguide rod.

[0039] B. Acquiring acoustic emission signals based on an active waveguide structure. Acoustic emission signal acquisition: The PCI-2 type 8-channel acoustic emission equipment was used to acquire the signal. The sampling frequency was set to 1MHz, the sampling threshold was 45dB, and the preamplifier gain was 40dB. Simultaneously, the slope sliding displacement data was recorded using a displacement meter with a sampling interval of 10s.

[0040] C. Parameter extraction and analysis of acoustic emission signals Acoustic emission parameter analysis: The collected acoustic emission signal was subjected to wavelet transform and decomposed into 8 frequency bands. The energy percentage of the high-frequency band from 312.5 to 500 kHz was calculated. A linear fitting relationship was established between the cumulative AE count and the sliding displacement, resulting in the fitting equation y = 135.2x² + 43262.4x + 2488.4, with R² = 0.998. Analysis revealed that when the slope entered the accelerated deformation stage, high-frequency signals of 300-350kHz continuously appeared in the main frequency domain of acoustic emission signals, and the energy percentage of the high-frequency band rose to over 20%.

[0041] D. Perform mutation analysis and construct early warning intervals; A GM(1,1) model is established for the cumulative AE count sequence to obtain the predicted sequence; The predicted sequence was fitted with a 5th-order polynomial to convert it into a cusp catastrophe model. The bifurcation discriminant Δ = -25452584.7 < 0 is calculated, indicating a sudden change in the cumulative AE count.

[0042] E. Based on the warning interval, output multi-level warnings.

[0043] Combining the high-frequency mutation in the main frequency domain, which accelerates the initial stage of deformation, and the mutation in the cumulative AE count, which accelerates the later stage of deformation, the warning interval is determined to be 580-620s of deformation time.

[0044] This invention utilizes an active waveguide structure containing discrete filling particles to collect acoustic emission signals during the slope sliding deformation process in soil slopes, analyzes the evolution law of acoustic emission parameters, and constructs an early warning interval by combining a gray-cusp catastrophe model, ultimately achieving multi-level early warning of soil slope instability.

[0045] This invention solves the problems of large signal attenuation and low early warning accuracy in traditional slope monitoring. It can accurately capture early signs of internal slope deformation and predict instability risks in advance, providing reliable technical support for the prevention and control of geological disasters on soil slopes.

Claims

1. A soil slope instability early warning method based on active waveguide acoustic emission technology, characterized in that: The method comprises the following steps: A. drilling a hole in the area of the potential sliding surface of the soil slope and arranging an active waveguide structure; B. collecting acoustic emission signals based on the active waveguide structure; C. parameter extraction and analysis of the acoustic emission signals; D. mutation analysis and construction of a warning interval; E. outputting multi-level warnings based on the warning interval.

2. The soil slope instability early warning method based on active waveguide acoustic emission technology according to claim 1, characterized in that: Step A drills a hole in the area of the potential sliding surface of the soil slope and arranges an active waveguide structure, and the specific process is as follows: First, a vertical hole is drilled in the area of the potential sliding surface of the soil slope, and the drilling depth penetrates the potential sliding surface; Then, a waveguide rod is installed in the hole, and the waveguide rod is made of steel pipe material; Finally, a discrete particle material is filled between the waveguide rod and the hole wall to form an active waveguide structure.

3. The soil slope instability early warning method based on active waveguide acoustic emission technology according to claim 2, characterized in that: Two parallel planes are cut along the longitudinal direction at the top of the waveguide rod, and an acoustic emission sensor is installed, and vaseline coupling agent is applied to the contact surface between the acoustic emission sensor and the waveguide rod.

4. The soil slope instability early warning method based on active waveguide acoustic emission technology according to claim 1, characterized in that: Step B collects acoustic emission signals based on the active waveguide structure, and the specific process is as follows: First, connect the multi-channel acoustic emission device to the acoustic emission sensor; Then, real-time collection of acoustic emission signals generated by the mutual extrusion and friction of discrete particles during the deformation process of the slope sliding.

5. The soil slope instability early warning method based on active waveguide acoustic emission technology according to claim 1, characterized in that: Step C performs parameter extraction and analysis on the acoustic emission signals, and the specific process is as follows: First, pre-process the collected acoustic emission signals to remove noise interference; Then, extract the acoustic emission count, cumulative AE count, main frequency domain distribution, and high-frequency band energy percentage parameters; Finally, establish a quantitative relationship between the cumulative AE count and the sliding displacement through linear fitting.

6. The soil slope instability early warning method based on active waveguide acoustic emission technology according to claim 5, characterized in that: The high-frequency band energy percentage parameter is calculated according to the following formula: ； where E 3j is the high band component energy, E 0 is the total signal energy.

7. The soil slope instability early warning method based on active waveguide acoustic emission technology according to claim 1, characterized in that: Step D performs mutation analysis and constructs a warning interval, and the specific process is as follows: First, generate the original sequence of the cumulative AE count; Then, establish a GM(1,1) gray prediction model, solve the model parameters, i.e., the development coefficient a and the gray action amount b, by the least square method, to obtain the prediction sequence of the cumulative AE count; Next, use multi-order polynomial fitting on the prediction sequence to determine whether the cumulative AE count has mutated and determine the mutation boundary of the cumulative AE count.

8. The soil slope instability early warning method based on active waveguide acoustic emission technology according to claim 7, characterized in that: Combined with the high-frequency mutation characteristics of the main frequency domain of the acoustic emission signal, when the main frequency domain continuously generates 300-350 kHz high-frequency signals and the high-frequency band energy percentage increases to more than 20%, the high-frequency mutation boundary of the main frequency domain is determined; the two mutation boundaries constitute the warning interval.

9. The soil slope instability early warning method based on active waveguide acoustic emission technology according to claim 1, characterized in that: Step E outputs multi-level warnings based on the warning interval, and the specific process is as follows: First, divide the warning levels according to the evolution characteristics of the acoustic emission parameters; Then, when the acoustic emission count, amplitude, and energy start to increase, and the parameter correlation diagram signal points expand, a blue warning is issued; Next, when continuous high-frequency signals appear in the main frequency domain, a yellow warning is issued; Next, when the high-frequency band energy percentage maintains a high level, an orange warning is issued; Finally, when the cumulative AE count sequence triggers the gray-cusp mutation model, a red warning is issued.

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