Monitoring and early warning method for near-road falling type dangerous rock under driving excitation

Abstract

The invention belongs to the technical field of geological disaster monitoring and early warning, and particularly relates to a near-road falling type dangerous rock monitoring and early warning method under driving excitation. The method utilizes a sensor technology to capture the vibration response of the rock mass when a vehicle passes, calculates the natural vibration frequency early warning threshold value of the dangerous rock mass based on fracture mechanics and structural dynamics, obtains a natural vibration frequency monitoring curve of the dangerous rock mass according to monitoring, and combines the natural vibration frequency early warning threshold value of the dangerous rock mass. And carrying out real-time monitoring and early warning on the stability of the dangerous rock. According to the method, the defects existing in a traditional method are overcome, an effective way is provided for real-time evaluation and early warning of the stability of the dangerous rock, the accuracy and response speed of dangerous rock monitoring can be improved, and safety accidents caused by falling of the dangerous rock are reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of geological disaster monitoring and early warning, and specifically relates to a road-falling dangerous rock monitoring and early warning method under vehicle driving stimulation. Background Art

[0002] As a common geological disaster, dangerous rocks pose a serious threat to personnel safety and infrastructure, especially dangerous rocks near roads. If they fall in busy traffic sections, they may cause traffic accidents and casualties. At the same time, they also endanger the safety of road structures, cause road damage, and interrupt transportation.

[0003] In order to avoid the harm that dangerous rocks may cause, the common practice now is to conduct regular crack and displacement monitoring of dangerous rocks. Although this can evaluate the stability of dangerous rocks to a certain extent, it has problems such as difficulty in monitoring and untimely early warning. With the development of vibration sensor technology, real-time monitoring and data analysis based on vibration sensors have become possible. Rock mass vibration characteristics, such as vibration frequency, have been shown to be closely related to the stability of dangerous rocks. However, in the study of vibration monitoring of dangerous rock masses, long-term real-time monitoring is often impossible due to problems such as difficulty in obtaining vibration sources. Summary of the invention

[0004] In view of the above-mentioned defects of the prior art, the object of the present invention is to provide a method for monitoring and early warning of dangerous rocks falling near the road, so as to improve the accuracy and response speed of dangerous rock monitoring and realize real-time monitoring and early warning.

[0005] In order to achieve the above object, the present invention provides a method for monitoring and early warning of dangerous rocks falling on the road under driving stimulation, comprising the following steps:

[0006] 1) Investigate the basic characteristics of dangerous rock mass, including size, weight, elastic modulus, fracture surface inclination and fracture toughness;

[0007] 2) Arrange several vibration signal sensors at different positions of the dangerous rock mass according to the height of the dangerous rock near the road;

[0008] 3) Using a vibration signal sensor to collect rock mass vibration data y(t) caused by traffic loads and transmit it to a processor;

[0009] 4) Using a processor to pre-process the vibration signal, the vibration data is decomposed, denoised, detrended, and reconstructed using the complete ensemble empirical mode decomposition method of CEEMDAN adaptive noise;

[0010] 5) Use the fast Fourier transform method to process the reconstructed data and analyze the frequency domain characteristics (frequency distribution characteristics) of the reconstructed data;

[0011] 6) According to the frequency domain characteristics of the reconstructed data, the natural vibration frequency f of the dangerous rock mass is extracted n ;

[0012] 7) Based on the theory of structural dynamics and fracture mechanics, the natural vibration frequency threshold f of the dangerous rock mass is calculated ni ;

[0013] 8) Based on the long-term monitoring, the monitoring curve of the natural vibration frequency of the dangerous rock mass is obtained, combined with the natural vibration frequency threshold f of the dangerous rock mass. ni , monitor and warn of dangerous rock masses. If the risk level of the dangerous rock mass is red (unstable state) and orange (understable state), a warning will be issued; if the risk level of the dangerous rock mass is yellow (basically stable state) and green (stable state), monitoring will continue.

[0014] Furthermore, the rock mass vibration data y(t) in step 3) can be decomposed into

[0015] y(t)=x(t)+σδ(t) (0.5)

[0016] Where x(t) is the effective vibration signal; σ is the noise intensity coefficient; δ(t) is the noise signal. When there is no car on the road, y(t) = σδ(t). The signal collected at this time is a noise signal, which can be identified based on the characteristics of the noise signal. The noise signal has the following characteristics: ① Any noise signal can be regarded as a stable random signal, its amplitude is independent and the noise at any time is unrelated; ② The noise signal has low energy and the amplitude is less than 0.01m / s2, but the vibration energy will not decay in the time domain, and its mean is 0; ③ The noise signal distribution band is wide, in which the low-frequency and high-frequency components account for a large proportion.

[0017] Further, the specific steps of the CEEMDAN empirical mode decomposition method in step 4) are as follows: first, the original signal vibration signal is decomposed by CEEMDAN, which is based on the fully adaptive noise ensemble empirical mode decomposition. The method is: after each first-order IMF component is calculated, white noise (or the IMF component of white noise) is added to the residual value again and the mean of the IMF component at this time is calculated, and it is iterated successively. Several signal component IMFs are obtained through the fully adaptive noise ensemble empirical mode decomposition (CEEMDAN).

[0018] By analyzing the frequency and amplitude of each IMF, the noise components in the signal are identified and removed, and the remaining valid IMF components are added to reconstruct the vibration signal.

[0019] Furthermore, in step 5), the fast Fourier transform algorithm decomposes the waveform data Y(t) into the sum of sinusoidal waves of different frequencies, namely:

[0020]

[0021] Where Y(t) is the time domain data of the vibration signal; FFT(f) is the frequency domain data of the vibration signal after Fourier transformation; -jft is a complex signal; f is the frequency.

[0022] According to formula (1.6), the reconstructed vibration signal is transformed by FFT to obtain its corresponding frequency domain diagram, and the natural vibration frequency f of the rock mass can be extracted. n .

[0023] Furthermore, in step 7), the dangerous rock mass can be simplified into a single particle model, such as Figure 4 As shown. According to the structural dynamics theory, the vibration equation of this model is:

[0024]

[0025] Where m is the mass of the dangerous rock mass; k is the stiffness coefficient of the bonding layer; d(t) is the deflection equation of the particle.

[0026] According to the vibration equation (1.7), the angular frequency w and the natural vibration frequency f can be obtained n :

[0027]

[0028]

[0029]

[0030] Where E is the elastic modulus of the dangerous rock mass, I is the moment of inertia of the dangerous rock bonding layer, b is the bonding width. When simplified to a plane problem, b takes unit width; e is the bonding length.

[0031]

[0032] Furthermore, in step 7), based on the principle of fracture mechanics, a monitoring threshold of the natural frequency of the dangerous rock mass can be derived, and an early warning is issued when the threshold is exceeded.

[0033]

[0034] Where K e (f ni ) is: the joint fracture strength factor of the main controlling structural surface of dangerous rock; K c F is the fracture toughness of intact rock in dangerous rock; s is the stability coefficient of dangerous rock.

[0035] Ke(fni)=KI(fni)+KII(fni)(0.13)

[0036]

[0037] The left side of formula (1.14) is a function about f ni A function of c is the mechanical parameter of dangerous rock, F s The stability coefficient of dangerous rock is determined according to DB50T 143-2018 "Chongqing Geological Hazard Prevention and Control Engineering Investigation Code".

[0038] Substitute K c and F s Substituting the specific value into formula (1.14), we can get a formula that is only about f ni The equation can then be solved by the Newton-Raphson method or the trust region method to obtain f ni The three corresponding monitoring thresholds f n1 、f n2 、f n3 .

[0039] Based on the above monitoring thresholds, dangerous rocks are classified into: f ni <f n1 Unstable state, red; f n1 ≤f ni <f n2 Under-stable state, orange; f n2 ≤f ni <f n3 It is basically stable, yellow, f ni ≥f n3 It is stable state, green.

[0040] Furthermore, in step 7), based on the principle of fracture mechanics, in order to solve f ni , we need to find the corresponding K first Ⅰ (f ni ), K Ⅱ (f ni The detailed solution process is given below.

[0041] (1) K Ⅰ (f ni ) solution process:

[0042] ① Stress intensity factor K of bending moment Ⅰ1 :

[0043] a=He (0.15)

[0044]

[0045]

[0046] Where a is the crack length, which can be measured by the monitoring threshold f of the natural vibration frequency of the rock mass. ni To express.

[0047]

[0048] M=WL (0.19)

[0049]

[0050] Among them, W is the deadweight of the dangerous rock mass, H is the height of the dangerous rock mass, and β is the inclination angle of the main controlling structural surface.

[0051] ② Stress intensity factor K of tensile stress Ⅰ2 :

[0052]

[0053]

[0054]

[0055] All kinds of stress intensity factors are superimposable, so the type I stress intensity factor K Ⅰ :

[0056] K I (f ni )=K I1 +K I2 (0.24)

[0057] (2) Stress intensity factor K of shear stress Ⅱ (f ni ):

[0058]

[0059]

[0060] The monitoring and early warning method provided by the present invention has the following characteristics: by using sensor technology to capture the vibration response of the rock mass when the vehicle passes by, and combining data analysis to conduct real-time evaluation and early warning of the stability of the dangerous rock, the accuracy and response speed of dangerous rock monitoring are effectively improved, and the safety accidents caused by the fall of dangerous rocks are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of the monitoring and early warning method of road-falling dangerous rocks under driving incentive;

[0062] Figure 2 This is a schematic diagram of vibration signal monitoring of dangerous rock mass falling near the road;

[0063] Figure 3This is a schematic diagram of the size of the falling dangerous rock mass;

[0064] Figure 4 It is a single-particle model of falling dangerous rock mass;

[0065] Figure 5 This is a schematic diagram of the dangerous rock frequency monitoring curve. DETAILED DESCRIPTION

[0066] The present invention will be further described below in conjunction with the embodiments.

[0067] See also Figure 1 This embodiment discloses a method for monitoring and warning of dangerous rocks falling on the road under driving stimulation, which specifically includes the following steps:

[0068] 1) Taking a dangerous rock in Wanzhou, Chongqing as an example, the basic characteristics of the dangerous rock mass are obtained through investigation and exploration, including the size parameters of the dangerous rock mass: H = 10m, L = 2m, and the weight per unit length is W = 9.8×10 5 N, mass per unit length m = 10 5 kg, the fracture surface inclination angle is β = 85°, and the elastic modulus of dangerous rock is E = 5.0 × 10 4 MPa, fracture toughness of dangerous rock K c =2100kPa·m 0.5 ;

[0069] 2) According to the height and volume of the dangerous rock mass near the road, 1-3 vibration signal sensors are arranged at different positions of the dangerous rock mass. It is recommended to place a sensor at the top, bottom and middle of the dangerous rock mass with a larger volume. This strategy aims to ensure that data is collected from the key positions of the dangerous rock mass in order to accurately capture its vibration changes at different heights. At the same time, a sensor is arranged at the position of the bedrock near the road for signal comparison, which helps to pre-process the vibration signal;

[0070] 3) Using a vibration signal sensor to collect rock mass vibration data y(t) caused by traffic loads and transmit it to a processor;

[0071] 4) Use the processor to pre-process the vibration signal, and use the CEEMDAN empirical mode decomposition method to decompose the vibration data - reduce noise - remove trend terms - and reconstruct;

[0072] 5) Based on the fast Fourier transform method, the reconstructed data is processed using formula (1.6), and the frequency domain characteristics (frequency distribution characteristics) of the reconstructed data are analyzed;

[0073] 6) According to the frequency domain characteristics of the reconstructed data, the natural vibration frequency f of the dangerous rock mass is extracted n ;

[0074] 7) Based on the theory of structural dynamics and fracture mechanics, formula (1.11) and formula (1.14) are used to establish the natural vibration frequency threshold f of the dangerous rock mass: ni The equation can be solved by using the Newton-Raphson method or the trust-region method to obtain three corresponding monitoring thresholds f n1 =174.6Hz, f n2 =234.6Hz, f n3 =258.9Hz.

[0075] 8) Based on long-term monitoring, the natural vibration frequency monitoring curve of dangerous rock mass is obtained, such as Figure 5 As shown. Combined with the natural vibration frequency threshold f of the dangerous rock mass ni , monitor and warn of dangerous rock masses. If the risk level of the dangerous rock mass is red (unstable state) or orange (understable state), a warning will be issued; if the risk level of the dangerous rock mass is yellow (basically stable state) or green (stable state), monitoring will continue.

[0076] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. Similarly, in order to simplify the present invention and help understand one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. Wherein, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself is a separate embodiment of the present invention.

[0077] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A method for monitoring and early warning of dangerous rocks falling on the road under driving stimulation, characterized in that: The following steps are involved: 1) Investigate the basic characteristics of dangerous rock mass, including size, weight, elastic modulus, fracture surface inclination and fracture toughness; 2) Arrange several vibration signal sensors at different positions of the dangerous rock mass according to the height of the dangerous rock near the road; 3) Using a vibration signal sensor to collect rock mass vibration data y(t) caused by traffic loads and transmit it to a processor; 4) Using a processor to pre-process the vibration signal, and using the complete ensemble empirical mode decomposition method of CEEMDAN adaptive noise to decompose, reduce noise, remove trend terms, and reconstruct the vibration data; 5) Using the fast Fourier transform method to process the reconstructed data and analyze the frequency domain characteristics of the reconstructed data; 6) According to the frequency domain characteristics of the reconstructed data, the natural vibration frequency f of the dangerous rock mass is extracted n ; 7) According to structural dynamics and fracture mechanics, calculate the natural vibration frequency threshold f of the dangerous rock mass ni ; 8) Based on long-term monitoring, the monitoring curve of the natural vibration frequency of the dangerous rock mass is obtained, combined with the natural vibration frequency threshold f of the dangerous rock mass. ni , monitor and warn of dangerous rock masses.

2. The method for monitoring and early warning of dangerous rocks falling on the road under driving stimulation according to claim 1 is characterized in that: The rock mass vibration data y(t) in step 3) is decomposed into: y(t)=x(t)+σδ(t) Where x(t) is the effective vibration signal; σ is the noise intensity coefficient; δ(t) is the noise signal.

3. The method for monitoring and early warning of dangerous rocks falling on the road under driving stimulation according to claim 1 is characterized in that: In step 4), the original signal vibration signal is first decomposed by CEEMDAN, and a data decomposition method based on fully adaptive noise set empirical mode decomposition is adopted, specifically: after each first-order IMF component is calculated, white noise or the IMF component of white noise is re-added to the residual value, and the mean of the IMF component at this time is calculated, and it is iterated successively; several signal component IMFs are obtained by fully adaptive noise set empirical mode decomposition, and the noise components in the signal are identified and removed by analyzing the frequency and amplitude of each IMF; finally, the remaining valid IMF components are added to reconstruct the vibration signal.

4. The method for monitoring and early warning of dangerous rocks falling on the road under driving stimulation according to claim 1 is characterized in that: In step 5), the fast Fourier transform algorithm decomposes the waveform data Y(t) into the sum of sine waves of different frequencies FFT(f): Wherein, Y(t) is the time domain data of the vibration signal; FFT(f) is the frequency domain data of the vibration signal after Fourier transformation; -jft is a complex signal; f is the frequency. Perform FFT transformation on the reconstructed vibration signal to obtain its corresponding frequency domain diagram.

5. The method for monitoring and early warning of dangerous rocks falling on the road under driving stimulation according to claim 1 is characterized in that: In step 7), the dangerous rock mass is simplified into a single particle model. According to the vibration equation (1.1), Where m is the mass of the dangerous rock mass; k is the stiffness coefficient of the bonding layer; d(t) is the deflection equation of the particle, and the relationship between the rock mass vibration frequency and the bonding length is obtained (1.2) Where E is the elastic modulus of the dangerous rock mass; e is the bonding length; and L is the distance from the center of mass of the dangerous rock mass to the bonding layer.

6. The method for monitoring and early warning of dangerous rocks falling on the road under driving stimulation according to claim 1 is characterized in that: In step 7), Among them, K e (f ni ) is the combined fracture strength factor of the main controlling structural surface of dangerous rock; K c F is the fracture toughness of intact rock in dangerous rock; s is the stability coefficient of dangerous rock; K e (f ni ) into formula (1.3), we can get f ni The calculation formula is: The left side of formula (1.4) is a ni A function of c is the mechanical parameter of dangerous rock, F s is the stability coefficient of dangerous rock; K c and F s Substitute the specific value into formula (1.4), and we get f ni The equation is then solved by the Newton-Raphson method or the trust region method to obtain f ni The three corresponding monitoring thresholds f n1 、f n2 、f n3。 7. The method for monitoring and early warning of dangerous rocks falling on the road under driving stimulation according to claim 6 is characterized in that: The stable state judgment and early warning method of the dangerous rock mass is: ni <f n1 It is unstable, red warning; f n1 <f ni ≤f n2 It is an unstable state, orange warning; f n2 <f ni ≤f n3 It is basically stable, yellow warning, f ni ≥f n3 It is in a stable state, with green warning.

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