Method and apparatus for modal acoustic emission fractal assessment of water permeation damage in rock mass pipelines

Abstract

This invention belongs to the field of underground pipeline engineering safety monitoring, and discloses a method and device for modal acoustic emission fractal assessment of cracked and permeable damage in rock pipelines. The method collects modal acoustic emission signals from the rock pipeline during monitoring, extracts four parameters: energy, duration, ring count, and rise time, constructs fractal characteristic indices based on the parameter set, and combines this with a softmax classification method to assess the degree of cracking and permeability. The device includes a modal acoustic emission acquisition module and a signal analysis module, in which the cracking and permeability fractal analysis can be evaluated. This invention requires simple hardware, provides accurate identification results, and exhibits good real-time performance and predictive capabilities, enabling early warning of cracked and permeable conditions in rock pipelines.

Description

Technical Field

[0001] This invention belongs to the field of underground pipeline engineering safety monitoring, specifically relating to a modal acoustic emission fractal assessment method and device for water permeation damage in rock pipelines. Background Technology

[0002] Underground rock pipelines are a crucial component of urban infrastructure, undertaking vital tasks such as stormwater and sewage drainage, and energy supply. These pipelines often operate in harsh environments characterized by high pressure, drastic temperature fluctuations, and strong corrosion. These conditions easily lead to pipe wall cracking and water leakage, potentially causing pipeline ruptures and systemic failures. Pipeline accidents can cause urban flooding and rainwater backflow, posing a significant threat to public safety. Therefore, intelligent real-time monitoring of the cracking and water leakage status of underground rock pipelines is of paramount importance.

[0003] Traditional methods for detecting the permeability of underground pipelines mainly include time-domain reflectometry, frequency-domain reflectometry, and neutron radar. Their main principle is to utilize the dielectric properties of rock particles under different permeability states. However, these methods require excavation for inspection of underground pipelines, necessitating water supply shutdowns and work stoppages, resulting in significant manpower and financial costs. For urban underground pipelines where direct maintenance by personnel is difficult, a method for real-time, trenchless, and non-stop monitoring of the permeability of underground rock pipelines is urgently needed.

[0004] Modal acoustic emission (MAE) is the release of elastic ultrasonic waves by rock mass pipeline structures under stress. Moisture affects the mechanical parameters of the rock mass, altering the slip friction effect between rock particles, and consequently changing parameters such as MAE energy, duration, ring count, and rise time. The correlation between this permeability state and MAE parameters has been widely recognized by academia and industry, and can solve the real-time and intelligent problems of monitoring permeability in underground rock mass pipelines. Currently, some studies utilize MAE for assessing the permeability state of underground rock mass pipelines. For example, patent application CN114526451A discloses a method and device for identifying the acoustic emission wave hierarchy of seepage in underground space rock mass pipelines. This method uses sensors to collect acoustic emission signals from nearby monitoring nodes, analyzes signal parameter characteristics, and uses the dynamic step characteristics of the signal to identify the seepage state. However, in real engineering projects, the distance between the sensing node and the sound source is uncertain; the rock mass pipeline environment is harsh, the pipe wall has anisotropy, and the tortuous distribution of underground pipelines causes dispersion and distortion of the acoustic emission signal during propagation, which can easily interfere with the assessment results. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a modal acoustic emission fractal assessment method and apparatus for water permeability damage in rock mass pipelines. By constructing a set of reconstructed sequence matrices based on extracted energy, duration, ringing count, and rise time parameters, a set of relevant fractal correlation dimension indices is calculated. Softmax is then used to assess the water permeability state through the sum of probabilities of the four parameters.

[0006] The modal acoustic emission fractal assessment method for water permeation damage in rock mass pipelines described in this invention includes the following steps:

[0007] Step 1: Obtain modal acoustic emission signals of underground rock pipes under different permeability conditions, extract parameters such as emission signal energy, duration, ring count, and rise time, and construct a set of parameter recombination sequence matrices under different permeability conditions;

[0008] Step 2: For the recombined sequence matrix of each parameter, calculate the set of fractal correlation dimension indices for different parameters;

[0009] Step 3: Based on the fractal correlation dimension index of each parameter and different permeability states, calculate the judgment probability of each parameter using the softmax classification method to evaluate the permeability state.

[0010] Furthermore, in step 1, a set of recombined sequence matrices is constructed based on the signal energy, duration, ring count, and rise time parameters under different water permeability states. The specific method is as follows:

[0011] (1) Record the energy parameter sequence under dry conditions Where t is the number of valid signals, and t is an even number; energy parameters under different permeability states are extracted. Where r represents the permeable state; construct the energy parameter recombination sequence matrix:

[0012]

[0013] (2) Record the duration parameter sequence under the dry state. Where t is the number of valid signals, and t is an even number; extract the duration parameter under different permeability conditions. Where r represents the permeable state; construct the duration parameter recombination sequence matrix:

[0014]

[0015] (3) Record the sequence of ringing count parameters under dry conditions. Where t is the number of valid signals, and t is an even number; the ringing count parameters under different permeability conditions are extracted. Where r represents the permeability state; construct the ringing count parameter recombination sequence matrix:

[0016]

[0017] (4) Record the rise time parameter sequence under dry conditions. Where t is the number of valid signals, and t is an even number; the rise time parameter under different permeability conditions is extracted. Where r represents the permeable state; construct the rise time parameter recombination sequence matrix:

[0018]

[0019] Through the above steps, a set of parameter recombination sequence matrices is obtained.

[0020] Furthermore, in step 2, the fractal correlation dimension index set of signal energy, duration, ring count, and rise time parameters is calculated. The specific steps are as follows:

[0021] Reconstructing sequence matrices based on energy parameters For the first row, i.e., the first permeable state, let it be denoted as vector x = (x1, x2, ... x N Construct an m-dimensional Euclidean space sequence set:

[0022]

[0023] Where N is the number of signal sampling points, m is the Euclidean space dimension, and τ is the time scale parameter. m The cumulative parameter is expressed as N. m =N-(m-1)τ; Given a threshold h, calculate the correlation integral function:

[0024]

[0025] Where H is the Heaviside function, and its expression is:

[0026]

[0027] d ij The distance between points in the sequence in Euclidean space:

[0028]

[0029] Where x i and x j A point in a European-style space;

[0030] The calculated point pairs (InC) m The slope of the linear fit of (h),Inh) is the fractal correlation dimension index. Calculate the fractal correlation dimension index for each permeability state, denoted as .

[0031] The same operation was performed on the other three parameters to obtain a set of fractal correlation dimension indices for the signal energy, duration, ring count, and rise time parameters, denoted as:

[0032]

[0033] Furthermore, in step 2, the softmax classification method is used to assess the permeability state. The specific steps are as follows:

[0034] (1) Taking the fractal correlation dimension index of the energy parameter as input and r permeability states as output, the softmax function outputs the probability value P(y) of sample x belonging to a certain permeability state under the energy parameter criterion. i |x e ), i = 1, 2, ..., r, the expression is:

[0035]

[0036] (2) Taking the fractal correlation dimension index of the duration parameter as input and r permeability states as output, the softmax function outputs the probability value P(y) of sample x belonging to a certain permeability state under the duration parameter criterion. i |x c ), i = 1, 2, ..., r, the expression is:

[0037]

[0038] (3) Taking the fractal correlation dimension index of the ringing count parameter as input and r permeability states as output, the softmax function outputs the probability value P(y) of sample x belonging to a certain permeability state under the ringing count parameter criterion. i |x z ), i = 1, 2, ..., r, the expression is:

[0039]

[0040] (4) Taking the fractal correlation dimension index of the rise time parameter as input and r permeability states as output, the softmax function outputs the probability value P(y) of sample x belonging to a certain permeability state under the rise time parameter criterion. i |x s ), i = 1, 2, ..., r, the expression is:

[0041]

[0042] Calculate the sum of the probabilities of the four parameter criteria, i.e.:

[0043] P(y i )=P(y i |xe )+P(y i |x c )+P(y i |x z )+P(y i |x s )

[0044] The permeability state corresponding to the maximum sum of probabilities calculated based on four parameters is used as the evaluation result.

[0045] A modal acoustic emission fractal assessment device for water permeation damage in rock mass pipelines, which can utilize the above-mentioned method, includes a modal acoustic emission acquisition module and a signal processing module installed on the pipeline surface;

[0046] The modal acoustic emission acquisition module is used to acquire the modal acoustic emission signals of the pipeline and is connected to the signal processing module;

[0047] The signal processing module includes a parameter sequence extraction and recombination module, a fractal correlation dimension index calculation module, and a softmax evaluation module. The parameter sequence extraction and recombination module is used to extract energy, duration, ring count, and rise time parameters, and recombine these parameters to construct a set of parameter recombination sequence matrices. The fractal correlation dimension index calculation module is used to calculate the fractal correlation dimension index set of energy, duration, ring count, and rise time parameters. The softmax evaluation module is used to calculate the criterion probabilities of energy, duration, ring count, and rise time parameters, and uses the sum of the probabilities of the four parameters to evaluate the current water permeability state.

[0048] The beneficial effects of this invention are as follows: This invention utilizes four modal acoustic emission parameters of the pipe wall to construct a set of fractal correlation dimension indicators. By mining the fractal features of the acoustic emission characteristic parameter curves, it mines the signal complexity fluctuations of modal acoustic emission under different permeability states. Combined with a softmax classifier to analyze the statistical characteristics of the acoustic emission signal, it provides a probabilistic assessment of the permeability state, exhibiting stronger resistance to environmental interference. Compared to existing technologies, this invention overcomes interference caused by the anisotropy of pipe materials and complex structural geometry, resulting in better reliability and scientific rigor. The hardware involved in this invention is simple, and the assessment of the permeability state of underground rock pipelines exhibits high accuracy, strong intelligence, and good real-time performance. It possesses excellent real-time and predictive capabilities, enabling early warning of rock pipeline cracking and permeability, achieving trenchless, uninterrupted, real-time monitoring, which is beneficial for intelligent maintenance of urban underground pipelines. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the structure of the device described in this invention;

[0050] Figure 2 This is a flowchart of the method described in this invention;

[0051] Figure 3 This is a schematic diagram of modal acoustic emission signals under different water permeability conditions;

[0052] Figure 4 This is a schematic diagram showing the fractal correlation dimension index results corresponding to the four parameters. Detailed Implementation

[0053] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0054] like Figure 2 As shown, the rock mass pipeline cracking and water permeability modal acoustic emission fractal evaluation method of the present invention includes the following steps:

[0055] Step 1: Obtain modal acoustic emission signals of underground rock pipes under different permeability conditions, extract parameters such as emission signal energy, duration, ring count, and rise time, and construct a set of parameter recombination sequence matrices under different permeability conditions;

[0056] Step 2: For the recombined sequence matrix of each parameter, calculate the set of fractal correlation dimension indices for different parameters;

[0057] Step 3: Based on the fractal correlation dimension index of each parameter and different permeability states, calculate the judgment probability of each parameter using the softmax classification method to evaluate the permeability state.

[0058] In step 1, the set of recombined sequence matrices for signal energy, duration, ring count, and rise time parameters under different permeability states is constructed using the following method:

[0059] (1) Record the energy parameter sequence under dry conditions Where t is the number of valid signals, and t is an even number; energy parameters under different permeability states are extracted. Where r represents the permeable state; construct the energy parameter recombination sequence matrix:

[0060]

[0061] (2) Record the duration parameter sequence under the dry state. Where t is the number of valid signals, and t is an even number; extract the duration parameter under different permeability conditions. Where r represents the permeable state; construct the duration parameter recombination sequence matrix:

[0062]

[0063] (3) Record the sequence of ringing count parameters under dry conditions. Where t is the number of valid signals, and t is an even number; the ringing count parameters under different permeability conditions are extracted. Where r represents the permeability state; construct the ringing count parameter recombination sequence matrix:

[0064]

[0065] (4) Record the rise time parameter sequence under dry conditions. Where t is the number of valid signals, and t is an even number; the rise time parameter under different permeability conditions is extracted. Where r represents the permeable state; construct the rise time parameter recombination sequence matrix:

[0066]

[0067] Through the above steps, a set of parameter recombination sequence matrices is obtained.

[0068] In step 2, the fractal correlation dimension index set of signal energy, duration, ring count, and rise time parameters is calculated. Specifically, the sequence matrix is ​​reconstructed using the energy parameters. For example, the first row (representing the first permeable state) is denoted as vector x = (x1, x2, ... x...). N Construct an m-dimensional Euclidean space sequence set:

[0069]

[0070] Where N is the number of signal sampling points, m is the Euclidean space dimension, and τ is the time scale parameter. m The cumulative parameter is expressed as N. m =N-(m-1)τ; Given a threshold h, calculate the correlation integral function:

[0071]

[0072] Where H is the Heaviside function, and its expression is:

[0073]

[0074] d ij The distance between points in the sequence in Euclidean space:

[0075]

[0076] Where x i and x j A point in a European-style space;

[0077] Point-to-point (InC) mThe slope of the linear fit of (h),Inh) is the fractal correlation dimension index. Calculate the fractal correlation dimension index for each permeability state, denoted as .

[0078] The same operation was performed on the other three parameters to obtain a set of fractal correlation dimension indices for the signal energy, duration, ring count, and rise time parameters, denoted as:

[0079]

[0080] In step 2, the softmax classification method evaluates the permeability state, and the specific steps are as follows:

[0081] (1) Taking the fractal correlation dimension index of the energy parameter as input and r permeability states as output, the softmax function outputs the probability value P(y) of sample x belonging to a certain permeability state under the energy parameter criterion. i |x e ), i = 1, 2, ..., r, the expression is:

[0082]

[0083] (2) Taking the fractal correlation dimension index of the duration parameter as input and r permeability states as output, the softmax function outputs the probability value P(y) of sample x belonging to a certain permeability state under the duration parameter criterion. i |x c ), i = 1, 2, ..., r, the expression is:

[0084]

[0085] (3) Taking the fractal correlation dimension index of the ringing count parameter as input and r permeability states as output, the softmax function outputs the probability value P(y) of sample x belonging to a certain permeability state under the ringing count parameter criterion. i |x z ), i = 1, 2, ..., r, the expression is:

[0086]

[0087] (4) Taking the fractal correlation dimension index of the rise time parameter as input and r permeability states as output, the softmax function outputs the probability value P(y) of sample x belonging to a certain permeability state under the rise time parameter criterion. i |x s ), i = 1, 2, ..., r, the expression is:

[0088]

[0089] Calculate the sum of the probabilities of the four parameter criteria, i.e.:

[0090] P(y i )=P(y i |x e )+P(y i |x c )+P(y i |x z )+P(y i |x s )

[0091] The permeability state corresponding to the maximum sum of probabilities calculated based on four parameters is used as the evaluation result.

[0092] Taking five permeability grades as an example, the weighing method was used to control the rock mass pipe samples, achieving moisture contents of 0% (dry state), 25%, 50%, 75%, and 100% (saturated state). Modal acoustic emission signals of different permeability grades were collected under stress. The signals, after amplification and filtering, are shown below. Figure 3 As shown in Table 1, four parameters—energy, duration, ring count, and rise time—were extracted, and recombination sequences for these four parameters were constructed. Taking the energy parameter as an example, the recombination sequence results for different permeability states are shown in Table 1.

[0093] Table 1 Recombination sequences of energy parameters

[0094]

[0095] The fractal correlation dimension index with different parameters was calculated, and the results are as follows: Figure 4 As shown in Table 2, the permeability states of five samples were set to 0%, 25%, 50%, 75%, and 100% using the weighing method. The probability of each permeability state was calculated using the softmax function, and the results are shown in Table 2.

[0096] Table 2 Output probabilities of the softmax function

[0097]

[0098] As shown in the table, the maximum output probability of the softmax function falls within the actual permeability state of the test sample, which is consistent with reality. Through the above examples, it can be seen that the probabilistic identification method proposed in this invention can adjust the output probability value in real time as the permeability state of the sample changes. The change in permeability state at the engineering site is a gradual process; therefore, this method has good dynamism, real-time performance, and accuracy.

[0099] Based on the above method, the present invention also provides a fractal evaluation device for modal acoustic emission of rock mass pipeline cracking and water permeability, such as... Figure 1As shown, the device includes a modal acoustic emission acquisition module (1) and a signal processing module (2) installed on the surface of the pipe. The modal acoustic emission acquisition module (1) is used to acquire modal acoustic emission signals from the pipe and is connected to the signal processing module. The signal processing module (2) consists of a parameter sequence extraction and recombination module, a fractal correlation dimension index calculation module, and a softmax evaluation module. The parameter sequence extraction and recombination module is used to extract energy, duration, ring count, and rise time parameters and recombine each parameter to construct a parameter recombination sequence matrix set. The fractal correlation dimension index calculation module is used to calculate the fractal correlation dimension index set of energy, duration, ring count, and rise time parameters. The softmax evaluation module is used to calculate the criterion probability of energy, duration, ring count, and rise time parameters and use the sum of the four parameter probabilities to evaluate the permeability state.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to further limit the present invention. All equivalent changes made based on the description and drawings of the present invention are within the protection scope of the present invention.

Claims

1. A method for evaluating acoustic emission fractal of rock mass pipeline cracking and water permeation damage mode, characterized in that, Comprise the following steps: Step 1, obtaining the pipe modal acoustic emission signals of the underground rock mass under different water permeation states, extracting the energy, duration, ring count and rise time parameters of the emission signals, and constructing a parameter reorganization sequence matrix set under different water permeation states; Step 2, for each parameter reorganization sequence matrix, calculate the fractal correlation dimension index set of different parameters; Step 3, according to the fractal correlation dimension index of each parameter and the different water permeation states, calculate the judgment probability of each parameter by the softmax classification method, and evaluate the water permeation state; Wherein, the fractal correlation dimension index set of signal energy, duration, ring count and rise time parameters in step 2 is calculated, and the specific steps are as follows: Reorganizing sequence matrix for energy parameters For the first row, i.e. the first water permeation state, denoted as vector x = (x1, x2, … x N ), an m-dimensional Euclidean space sequence set is constructed: where N is the number of signal samples, m is the Euclidean space dimension, τ is the time scale parameter, N m is the cumulative parameter, expressed as N m = N - (m - 1)τ; given a threshold h, the correlation integral function is calculated: Wherein H is the Heaviside function, and the expression is: d ij is the distance between the sequence points in the Euclidean space. where x i and x j are points in Euclidean space; The slope of the fitted linear part of the point pair (InC m The slope of the fitted linear part of the point pair (InC The slope of the fitted linear part of the point pair (InC The same operation is performed on the other three parameters, and the fractal correlation dimension index set of signal energy, duration, ring count and rise time parameters is obtained, and the result is recorded as: In step 3, the softmax classification method is used to evaluate the water permeation state, and the specific steps are as follows: (1) The fractal correlation dimension index of the energy parameter is input, and r kinds of water permeation states are output. The softmax function outputs the probability value P(y i |x e ) of the sample x belonging to a certain water permeation state under the energy parameter criterion, i.e., i=1, 2,..., r, and the expression is as follows: (2) The fractal correlation dimension index of the duration parameter is input, and r kinds of water permeation states are output. The softmax function outputs the probability value P(y i |x c ) of the sample x belonging to a certain water permeation state under the duration parameter criterion, i.e. i = 1, 2, …, r, and the expression is: (3) The fractal correlation dimension index of the ring count parameter is input, and r kinds of water permeation states are output. The softmax function outputs the probability value P(y i |x z ) of the sample x belonging to a certain water permeation state under the ring count parameter criterion, i.e. i = 1, 2, …, r, and the expression is: (4) The fractal correlation dimension index of the rising time parameter is input, and r kinds of permeable states are output. The softmax function outputs the probability value P(y i |x s ) of the sample x belonging to a certain permeable state under the rising time parameter criterion, i.e. i = 1, 2, …, r, and the expression is: Calculate the total probability of four parameter criteria, that is: P(y i ) = P(y i |x e ) + P(y i |x c ) + P(y i |x z ) + P(y i |x s ) The water permeation state corresponding to the maximum value of the probability total calculated based on the four parameters is taken as the evaluation result.

2. The method of claim 1, wherein, In step 1, the reorganization sequence matrix set of signal energy, duration, ring count and rise time parameters under different water permeation states is constructed, and the specific method is as follows: (1) Energy parameter sequence in dry state Wherein t is the number of effective signals, and t is an even number; extract energy parameters in different water permeation states Wherein r is the water permeation state; construct energy parameter reorganization sequence matrix: (2) the sequence of duration parameters in dry state where t is the number of effective signals, and t is an even number; extracting the duration parameters in different water permeation states where r is the water permeation state; constructing a reorganized sequence matrix of duration parameters (3) the ringing count parameter sequence in the dry state wherein t is the number of effective signals, and t is an even number; extracting ringing count parameters in different water permeation states wherein r is the water permeation state; constructing a ringing count parameter reorganization sequence matrix: (4) the sequence of rising time parameters in dry state wherein t is the number of effective signals, and t is an even number; extracting the rising time parameters in different water permeation states wherein r is the water permeation state; constructing a reorganized sequence matrix of rising time parameters: By the above steps, the parameter recombination sequence matrix set is obtained 3. The rock mass pipeline cracking and water permeation damage mode acoustic emission fractal evaluation device is characterized in that, The device uses any one of the methods of claims 1-2, and the device comprises a modal acoustic emission acquisition module installed on the surface of the pipeline, a signal processing module; The modal acoustic emission acquisition module is used to acquire the pipe modal acoustic emission signals, and is connected to the signal processing module; The signal processing module comprises a parameter sequence extraction and reorganization module, a fractal correlation dimension index calculation module and a softmax evaluation module; the parameter sequence extraction and reorganization module is used to extract the energy, duration, ring count and rise time parameters, and reorganize each parameter to construct a parameter reorganization sequence matrix set; the fractal correlation dimension index calculation module is used to calculate the fractal correlation dimension index set of energy, duration, ring count and rise time parameters; the softmax evaluation module is used to calculate the judgment probability of energy, duration, ring count and rise time parameters, and evaluate the water permeation state.

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