Field slope stability monitoring method based on acoustic emission

Abstract

The invention provides a field slope stability monitoring method based on acoustic emission, and belongs to the technical field of field slope stability monitoring. In order to solve the technical problems of large soil mass fracture signal attenuation, strong noise interference and poor real-time performance of traditional monitoring in the existing loess slope instability monitoring, the adopted technical scheme is as follows: an inclined hole channel is drilled in the main sliding direction of a slope body by adopting a hydraulic guide drilling machine; vertically inserting the manufactured passive waveguide device from the top surface of the slope top, burying the manufactured active waveguide device into the drilled inclined hole channel, and burying the surface of a slope body by using an in-situ soil body; the acoustic emission probes of the passive waveguide devices and the active waveguide devices are connected with a collection device through signal lines, and the acoustic emission probes send induced acoustic emission signals to the collection device; the acquisition device receives the acoustic emission signals, then preprocesses the acoustic emission signals, extracts time-frequency domain features, and transmits the time-frequency domain features to the cloud system through a wireless network; the method is applied to field slope stability monitoring.

Description

Technical Field

[0001] The invention provides a field slope stability monitoring method based on acoustic emission, belonging to the technical field of field slope stability monitoring. Background Art

[0002] Due to the unique characteristics of the Loess Plateau, such as collapsibility, developed vertical joints and high porosity, slope collapse disasters occurring in the area are characterized by suddenness and great destructive power. To cope with the frequent geological disasters on the Loess Plateau, real-time and reliable slope stability monitoring of the complex geology is necessary.

[0003] Currently, acoustic emission technology is mainly used for monitoring. However, traditional acoustic emission monitoring methods (such as GNSS and InSAR) mainly capture surface displacement and deformation, and it is difficult to capture the spatiotemporal evolution of micro-fracture signals inside the slope, resulting in a significant lag in early warning and difficulty in taking protective measures before the disaster occurs. In addition, the single-point vertical waveguide sensor currently used is affected by the anisotropy of loess wave velocity (the difference in longitudinal wave velocity is as high as 15%) and can only capture local sliding surface signals. The existing array sensor spacing is designed based on the homogeneous rock model, which is difficult to match the loess permeability gradient characteristics, which will lead to the failure of the spatiotemporal decoupling of shallow tensile fractures and deep shear signals on the slope. At the same time, the noise in the field environment (wind, vibration, temperature and humidity changes) can easily interfere with the accuracy of the monitoring signal, which will cause deviations in the monitoring data and affect the judgment results of the slope stability. Summary of the Invention

[0004] In order to solve the technical problems of large soil rupture signal attenuation, strong noise interference and poor real-time performance of conventional monitoring in loess slope instability monitoring, the present invention adopts a technical solution: providing a field slope stability monitoring method based on acoustic emission, which includes the following monitoring steps:

[0005] Step S1: using a hydraulic directional drilling rig to drill an inclined tunnel along the main sliding direction of the slope;

[0006] Step S2: Producing a passive waveguide device and an active waveguide device respectively:

[0007] When making a passive waveguide device, one end of the waveguide rod is connected and fixed to the acoustic emission probe;

[0008] When making the active waveguide device, one end of the waveguide rod is connected and fixed to the acoustic emission probe, and a rubber tube is wrapped around the outside of the waveguide rod, and the inside of the rubber tube is filled with quartz sand of different particle size distributions;

[0009] Insert the prepared passive waveguide device vertically from the top surface of the slope;

[0010] The prepared active waveguide device is buried in the inclined channel drilled in step S1;

[0011] Use in-situ soil to bury the slope surface;

[0012] Step S3: connecting the acoustic emission probes of each passive waveguide device and active waveguide device to the collection device using a signal line, and the acoustic emission probes send induced acoustic emission signals to the collection device;

[0013] Step S4: After receiving the acoustic emission signal, the acquisition device performs preprocessing, extracts time-frequency domain features, and transmits it to the cloud system via a wireless network;

[0014] Step S5: The cloud system performs noise suppression on the received data based on wavelet transform, and establishes a dynamic warning threshold based on historical data. When abnormal data is detected, a warning message is sent through the terminal device.

[0015] In step S1, the drilling axis of the hydraulic directional drilling rig and the slope normal are specifically at an angle of 30°±5°, and the drilling depth is controlled so that the length of the waveguide rod is 10±5 cm inside the soil surface.

[0016] In step S1, after the drilling is completed, a slurry with a bentonite to water ratio of 1:8 is used to maintain the pressure in the hole.

[0017] The waveguide rod used in step S2 is specifically made of Q235 carbon steel, with an outer diameter of 20.0±0.5 mm and a wall thickness of 2.0±0.2 mm;

[0018] The outer diameter of the wrapped rubber tube is 50.0±1.0mm, the wall thickness is 1.0±0.1mm, and the elongation at break is ≥600%;

[0019] The filling quartz sand is graded from 3 to 20 mm and has a density of >60%.

[0020] The multi-stage impedance matching design of the active waveguide device manufactured in step S2 satisfies the following formula:

[0021] ;

[0022] Where Z is the acoustic impedance, is the medium density, V is the sound velocity, and the quartz sand particle size gradation is optimized to make the 100kHz signal transmission loss ≤3dB;

[0023] The density and sound velocity of quartz sand are adjusted by particle size grading, so that the acoustic impedance ratio of the waveguide rod to the quartz sand is controlled within the range of 1.2-1.5, and the 100kHz signal transmission loss is ≤3dB.

[0024] In step S3, the ceramic surface of the acoustic emission probe is fixed to the end face of the waveguide rod by a polyurethane coupling agent, and the density of the polyurethane coupling agent is 1.2 g / cm 3, coupling thickness 0.2±0.05mm, the interface is sealed with epoxy resin;

[0025] The resonant frequency of the acoustic emission probe is 150kHz±5%, and the sampling frequency of the acquisition device is ≥100kHz.

[0026] The preprocessing performed by the acquisition device in step S4 is specifically as follows:

[0027] By bandpass filtering at 20-30kHz, characteristic parameters including amplitude, ring count, and energy data are extracted.

[0028] The specific method for the cloud system to perform noise suppression on the received data based on wavelet transform in step S5 is:

[0029] Specifically, the wavelet transform uses db4 wavelet and 5-layer decomposition to suppress noise. The calculation formula is:

[0030] ;

[0031] ;

[0032] Where, and are the approximation coefficient and detail coefficient of the jth layer respectively, is the approximate coefficient of the previous layer.

[0033] The specific method for establishing the dynamic warning threshold in step S5 is:

[0034] The expression for establishing the dynamic warning threshold is:

[0035] ;

[0036] Where, is the mean, is the standard deviation, is the confidence coefficient, is the rate of change weight, is the rate of change of characteristic parameters;

[0037] Based on the established thresholds, the cloud system sets corresponding warning rules, including:

[0038] When the characteristic parameter exceeds the mean + 1.5σ, a yellow warning is issued;

[0039] When the characteristic parameter exceeds the mean + 2σ, an orange warning is issued;

[0040] When the characteristic parameter exceeds the mean + 3σ or continuous bursts of high-amplitude signals occur, a red warning is issued.

[0041] The acoustic emission probes set in step S3 are arranged in a cross array with a longitudinal spacing of 2.0±0.5m and a lateral spacing of 3.0±0.5m. The bottom ends of the vertically arranged waveguide rods penetrate the sliding surface and are buried at a depth of ≥2.0m to form a three-dimensional monitoring network.

[0042] The present invention has the following beneficial effects compared to the prior art: the present invention provides an acoustic emission-type dynamic monitoring scheme for field slope stability suitable for complex geological environments. Based on this monitoring scheme, geological disaster monitoring is transformed from "passive response deformation monitoring" to "active prediction and damage identification". The waveguide monitoring device used is a multi-stage impedance matching design, which amplifies the acoustic emission signal and transmits it directionally to the terminal sensor array, significantly enhancing the intensity of the acoustic emission signal and realizing directional transmission of the signal, solving the problem of large attenuation of soil rupture signals and improving the signal-to-noise ratio; during monitoring, the present invention determines the burial depth of the acoustic emission probe to 10±5 cm inside the slope, making full use of the natural barrier formed at this depth, so that the attenuation rate of surface traffic vibration and environmental noise reaches more than 80%, greatly reducing the interference of surface traffic vibration and environmental noise, and ensuring the reliability of monitoring data; the present invention connects the acoustic emission probe to an automatic collection device, and through cooperation with a cloud platform, it can realize real-time collection, processing and transmission of acoustic emission signals, solving the problems of poor real-time performance and monitoring lag of traditional monitoring technology, and realizing high-precision, low-latency real-time monitoring and early warning of slope stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below with reference to the accompanying drawings:

[0044] Figure 1 A schematic diagram of the structure of the passive and active waveguide devices used in the present invention;

[0045] Figure 2 This is a rendering of the automatic loading field test of Example 1 of the present invention;

[0046] Figure 3 Schematic diagram of force monitoring during automatic loading process according to embodiment 1 of the present invention;

[0047] Figure 4 This is a diagram showing the effect of acoustic emission monitoring in Example 1 of the present invention;

[0048] Figure 5 This is a rendering of the effect of long-term field monitoring in Example 2 of the present invention.

[0049] In the figure: 1 is a waveguide rod, 2 is an acoustic emission probe, 3 is a signal line, 4 is a rubber tube, 5 is quartz sand, 6 is an active waveguide device, 7 is a passive waveguide device, 8 is a load-bearing plate, and 9 is a collection device. DETAILED DESCRIPTION

[0050] like Figures 1 to 4 As shown, the present invention provides a slope stability monitoring method, specifically a field slope stability dynamic monitoring solution using acoustic emission, suitable for complex geological environments. The method can not only monitor the occurrence of internal cracks in the slope in advance and effectively isolate the influence of external noise, but also achieve real-time monitoring. The monitoring method mainly includes the following steps:

[0051] Step S1: A hydraulic steerable drill is used to drill a hole along the main sliding direction of the slope. The drilling axis forms an angle of 30°±5° with the slope normal. The hole depth is controlled so that the length of the waveguide rod is 10±5 cm inside the soil surface to meet noise shielding requirements.

[0052] Step S2: The waveguide rod is made of steel, wrapped with a rubber tube, and filled with quartz sand of different particle size grades to produce an active waveguide device. The passive waveguide device is a single waveguide rod and does not require drilling. The active waveguide device is buried in the inclined channel drilled in step S1 and buried with in-situ soil on the slope surface.

[0053] Step S3: connecting the acoustic emission probes of each passive waveguide device and active waveguide device to the collection device through a highly shielded signal line, and the acoustic emission probes send induced acoustic emission signals to the collection device;

[0054] Step S4: After receiving the acoustic emission signal, the acquisition device performs preprocessing, extracts time-frequency domain features, and transmits it to the cloud system via the 5G wireless network;

[0055] Step S5: The cloud system adopts a distributed storage architecture, which can perform noise suppression on the received data based on wavelet transform, and establish dynamic warning thresholds based on historical data. When abnormal data is detected, warning information is sent through the terminal device.

[0056] Furthermore, in step S1, after the drilling is completed, a slurry with a ratio of bentonite to water of 1:8 is used to maintain the pressure in the hole.

[0057] Furthermore, in step S2, the waveguide rod is specifically made of Q235 carbon steel, with an outer diameter of 20.0±0.5mm and a wall thickness of 2.0±0.2mm; the outer diameter of the wrapped rubber tube is 50.0±1.0mm, the wall thickness is 1.0±0.1mm, and the elongation at break is ≥600%; the filled quartz sand is graded 3-20mm and has a density of >60%.

[0058] Furthermore, the multi-stage impedance matching design of the active waveguide device manufactured in step S2 satisfies the following formula:

[0059] ;

[0060] Where Z is the acoustic impedance, Where λ is the medium density and V is the sound velocity. The density of quartz sand (2.65 g / cm³) and the sound velocity (5600 m / s) are adjusted by particle size grading to control the acoustic impedance ratio of the steel rod to the quartz sand within the range of 1.2-1.5, so that the 100 kHz signal transmission loss is ≤3 dB.

[0061] Furthermore, in step S3, the acoustic emission probe resonant frequency is 150kHz±5%, the automatic acquisition device sampling frequency is ≥100kHz, and the ceramic surface of the probe is fixed to the end face of the waveguide rod by a polyurethane coupling agent with a density of 1.2g / cm 3 , coupling thickness 0.2±0.05mm, and the interface is sealed with epoxy resin.

[0062] Furthermore, in step S4, the pre-processing method is band-pass filtering 20-30kHz, and the characteristic parameters extracted include amplitude, ringing count, energy and other data.

[0063] Furthermore, in step S5, the wavelet transform is db4 wavelet, and the 5-layer decomposition is used to perform noise suppression. The calculation formula is:

[0064] ;

[0065] ;

[0066] Where, and are the approximation coefficient and detail coefficient of the jth layer respectively, is the approximate coefficient of the previous layer.

[0067] Furthermore, in step S5, the dynamic warning threshold formula is:

[0068] ;

[0069] Where, is the mean, is the standard deviation, is the confidence coefficient, is the rate of change weight, is the rate of change of characteristic parameters.

[0070] Furthermore, in step S5, the cloud warning system sets a three-level warning mechanism: yellow warning (characteristic parameters exceed the mean + 1.5σ), orange warning (exceed the mean + 2σ), and red warning (exceed the mean + 3σ or continuous sudden high-amplitude signals appear), and the warning response time is ≤10s.

[0071] Furthermore, the acoustic emission probe array is arranged in a cross-shaped manner (longitudinal spacing 2.0±0.5m, lateral spacing 3.0±0.5m), the longitudinal waveguide rod is 25mm reinforced (wall thickness 3mm), the bottom end penetrates the sliding surface and the burial depth is ≥2.0m, forming a three-dimensional monitoring network.

[0072] In the embodiment of the present invention, embodiment 1 is an automatic loading field test solution, such as Figure 1-4 As shown, the following steps are included:

[0073] First follow Figure 1 Prepare five 1.2m-long active waveguides and one 1.5m-long passive waveguide. Use an excavator to trim the slope into a rectangular shape (2m x 2m x 2.5m). Use a hydraulic directional drill to drill five holes in a crosswise pattern, aligned with the slope's main sliding direction.

[0074] Active waveguides were buried in a cross pattern on the slope's front face, ensuring full coverage and capturing acoustic emission signals at various locations. The interior was filled with well-graded gravel to amplify the signal. The tops of the steel waveguide rods were connected to acoustic emission sensor probes via couplant. At the top of the slope, steel waveguide rods were vertically inserted, extending through the slope. These were also connected to acoustic emission sensor probes via couplant. The passive waveguide system was vertically pressed into the top of the slope using static pressure. The voids surrounding the waveguide system were carefully filled with in-situ soil and compacted.

[0075] Before monitoring, excavation was carried out at the slope angle, and the excavation depth was controlled at 20cm±2cm. The loading method adopted the ton bag step-by-step loading method. The ton bags prepared in advance (standard weight of a single bag is 0.5 tons) were lifted one by one to the high-strength pressure plate 2 (size 1m×2m, thickness 20mm) by a 25-ton truck crane. The ton bags were kept still for half an hour. Figure 3 As shown, after half an hour, the number of ton bags was increased again until the slope failed. When the amplitude of the acoustic emission signal showed continuous abnormal values ​​and visible cracks appeared on the slope surface, the loading was stopped and the final loading weight and failure form were recorded.

[0076] Finally, by recording the test data, the dynamic threshold is calculated in real time for early warning. The results are as follows: Figure 4 shown.

[0077] Based on the above experiments, the second embodiment of the present invention is a long-term monitoring solution in the field, such as Figure 5 As shown, the following steps are included:

[0078] Combined with geotechnical survey data, the main sliding direction of the slope is accurately identified. Five waveguide rods are arranged in a cross pattern on the slope face, starting from the top and ending at a preset interval of 20 meters. Three waveguide rods are set along the main sliding direction to form the main monitoring axis, and two waveguide rods are set perpendicular to the main sliding direction as auxiliary monitoring lines. This establishes a monitoring network to ensure comprehensive coverage of slope displacement and deformation.

[0079] A customized acoustic emission probe array is installed at the optimal position of each waveguide rod at a buried depth of 10±5 cm. Each array consists of three wide-band piezoelectric ceramic acoustic emission sensors with different sensitivities, covering the acoustic emission signal frequency band of 10kHz-1MHz. It is tightly bonded to the inner wall of the waveguide rod with epoxy resin glue to ensure efficient signal transmission.

[0080] Finally, the acoustic emission probe is connected to the automatic acquisition device and the power supply system through a low-noise signal connection line to perform real-time feature extraction and storage of the acoustic emission signal.

[0081] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above 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 field slope stability monitoring method based on acoustic emission, characterized by: The monitoring steps include the following: Step S1: using a hydraulic directional drilling rig to drill an inclined tunnel along the main sliding direction of the slope; Step S2: Producing a passive waveguide device and an active waveguide device respectively: When making a passive waveguide device, one end of the waveguide rod is connected and fixed to the acoustic emission probe; When making the active waveguide device, one end of the waveguide rod is connected and fixed to the acoustic emission probe, and a rubber tube is wrapped around the outside of the waveguide rod, and the inside of the rubber tube is filled with quartz sand of different particle size distributions; Insert the prepared passive waveguide device vertically from the top surface of the slope; The prepared active waveguide device is buried in the inclined channel drilled in step S1; Use in-situ soil to bury the slope surface; Step S3: connecting the acoustic emission probes of each passive waveguide device and active waveguide device to the collection device using a signal line, and the acoustic emission probes send induced acoustic emission signals to the collection device; Step S4: After receiving the acoustic emission signal, the acquisition device performs preprocessing, extracts time-frequency domain features, and transmits it to the cloud system via a wireless network; Step S5: The cloud system performs noise suppression on the received data based on wavelet transform, and establishes a dynamic warning threshold based on historical data. When abnormal data is detected, a warning message is sent through the terminal device.

2. The method for monitoring slope stability in the field based on acoustic emission according to claim 1, characterized in that: In step S1, the drilling axis of the hydraulic directional drilling rig and the slope normal are specifically at an angle of 30°±5°, and the drilling depth is controlled so that the length of the waveguide rod is 10±5 cm inside the soil surface.

3. The field slope stability monitoring method based on acoustic emission according to claim 1 is characterized by: In step S1, after the drilling is completed, a slurry with a bentonite to water ratio of 1:8 is used to maintain the pressure in the hole.

4. The method for monitoring slope stability in the field based on acoustic emission according to claim 1, characterized in that: The waveguide rod used in step S2 is specifically made of Q235 carbon steel, with an outer diameter of 20.0±0.5 mm and a wall thickness of 2.0±0.2 mm; The outer diameter of the wrapped rubber tube is 50.0±1.0mm, the wall thickness is 1.0±0.1mm, and the elongation at break is ≥600%; The filling quartz sand is graded from 3 to 20 mm and has a density of >60%.

5. The method for monitoring slope stability in the field based on acoustic emission according to claim 1, characterized in that: The multi-stage impedance matching design of the active waveguide device manufactured in step S2 satisfies the following formula: ； Where Z is the acoustic impedance, is the medium density, V is the sound velocity, and the quartz sand particle size gradation is optimized to make the 100kHz signal transmission loss ≤3dB; The density and sound velocity of quartz sand are adjusted by particle size grading, so that the acoustic impedance ratio of the waveguide rod to the quartz sand is controlled within the range of 1.2-1.5, and the 100kHz signal transmission loss is ≤3dB.

6. The method for monitoring slope stability in the field based on acoustic emission according to claim 1, characterized in that: In step S3, the ceramic surface of the acoustic emission probe is fixed to the end surface of the waveguide rod by a polyurethane coupling agent, and the density of the polyurethane coupling agent is 1.2 g / cm 3 , coupling thickness 0.2±0.05mm, the interface is sealed with epoxy resin; The resonant frequency of the acoustic emission probe is 150kHz±5%, and the sampling frequency of the acquisition device is ≥100kHz.

7. The method for monitoring slope stability in the field based on acoustic emission according to claim 1, characterized in that: The preprocessing performed by the acquisition device in step S4 is specifically as follows: By bandpass filtering at 20-30kHz, characteristic parameters including amplitude, ring count, and energy data are extracted.

8. The method for monitoring slope stability in the field based on acoustic emission according to claim 1, characterized in that: The specific method for the cloud system to perform noise suppression on the received data based on wavelet transform in step S5 is: Specifically, the wavelet transform uses db4 wavelet and 5-layer decomposition to suppress noise. The calculation formula is: ； ； Where, and are the approximation coefficient and detail coefficient of the jth layer respectively, is the approximate coefficient of the previous layer.

9. The method for monitoring slope stability in the field based on acoustic emission according to claim 1, characterized in that: The specific method for establishing the dynamic warning threshold in step S5 is: The expression for establishing the dynamic warning threshold is: ； Where, is the mean, is the standard deviation, is the confidence coefficient, is the rate of change weight, is the rate of change of characteristic parameters; Based on the established thresholds, the cloud system sets corresponding warning rules, including: When the characteristic parameter exceeds the mean + 1.5σ, a yellow warning is issued; When the characteristic parameter exceeds the mean + 2σ, an orange warning is issued; When the characteristic parameter exceeds the mean + 3σ or continuous bursts of high-amplitude signals occur, a red warning is issued.

10. The method for monitoring slope stability in the field based on acoustic emission according to claim 1, characterized in that: The acoustic emission probes set in step S3 are arranged in a cross array with a longitudinal spacing of 2.0±0.5m and a lateral spacing of 3.0±0.5m. The bottom ends of the vertically arranged waveguide rods penetrate the sliding surface and are buried at a depth of ≥2.0m to form a three-dimensional monitoring network.

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