Porosity distribution measurement method for shield slurry used in shield construction

Abstract

The present application relates to the field of signal detection technology, and specifically to a method for measuring the porosity distribution of shield mud used in shield construction, comprising: obtaining a correction coefficient for the distance measurement between two positions based on the amplitude distribution of different frequencies in the spectrum diagram of each position, the difference between the amplitudes at the same frequency in the spectrum diagram of two positions, the mean of the signal strength data of all moments at each position, and the difference between the signal strength at the time of transmission and the signal strength received; obtaining a distance measurement value between the two positions based on the correction coefficient for the distance measurement between the two positions, clustering all positions by the distance measurement value between the two positions to obtain a number of clusters, and obtaining the porosity distribution of the shield mud by the number of clusters. The present application improves the accuracy of clustering and also improves the accuracy of the measurement results of the porosity distribution of the shield mud.

Description

Technical Field

[0001] The present application relates to the field of signal detection technology, and in particular to a method for measuring the porosity distribution of shield mud used in shield construction. Background Art

[0002] Shield mud is typically composed of water, clay, and polymer additives. During shield tunneling, the mud is injected in front of the shield machine cutterhead, providing cooling, lubrication, soil support, and transporting excavated debris. The porosity of shield mud refers to the proportion of pore space in the mud to its total volume. It reflects the size and distribution of the gaps between solid particles within the mud. Porosity influences the mud's rheological properties, density, compactability, and other physical properties, significantly impacting the construction process and project quality of shield tunnels.

[0003] In order to analyze the porosity of shield mud, ultrasonic sensors are installed at multiple locations on the surface of the shield machine to obtain signal strength data. According to the signal strength data of all locations on the shield machine surface, all locations are clustered using the Markov clustering algorithm, and the porosity distribution of the shield mud is obtained based on the clustering results. Since factors such as particle size, shape, and concentration in the shield mud affect the propagation characteristics of ultrasonic waves, the transmission and reception of ultrasonic signals are interfered, resulting in low accuracy of the clustering results based on signal strength data, which reduces the accuracy of the measurement results of the porosity distribution of the shield mud. Summary of the Invention

[0004] The present application provides a porosity distribution measurement method for shield slurry used in shield construction to solve the existing problems.

[0005] The porosity distribution measurement method of shield slurry used in shield construction in this application adopts the following technical solutions:

[0006] One embodiment of the present application provides a method for measuring the porosity distribution of shield slurry used in shield construction, the method comprising the following steps:

[0007] Using ultrasonic sensors at several different positions on the side surface of the shield machine, a signal strength data curve at each position is obtained;

[0008] The Fourier transform technique is used to obtain a spectrum diagram at each location based on the signal strength curve at each location, and the signal spectrum characteristics at each location are determined based on the amplitude distribution in the spectrum diagram at each location. The degree of signal similarity between any two locations is determined based on the difference in amplitude distribution at the same frequency in the spectrum diagrams of any two locations and the difference between the signal spectrum characteristics.

[0009] Determine the information loss amount at each location based on the difference between the transmitted signal and the received signal and the signal strength during the porosity measurement at each location; determine the correction coefficient of the distance metric between any two locations based on the signal similarity between the two locations and the information loss amount at the two locations;

[0010] The distance between any two positions is corrected according to the correction coefficient of the distance measurement between any two positions, and the distance measurement value between any two positions is determined. The clustering method is used to cluster all positions based on the distance measurement value between all positions to obtain several clusters. The porosity distribution of shield mud is evaluated through several clusters.

[0011] Preferably, the determining of the signal spectrum characteristics at each position includes the following specific steps:

[0012] Discretize the signal strength data curve at each location to obtain the signal strength data at each location at several consecutive moments;

[0013] The spectrum of each location is obtained by Fourier transform based on the signal strength data of each location at several consecutive moments;

[0014] The signal spectrum characteristics of each position are calculated based on the concentration and amplitude of the amplitude distribution at different frequencies in the spectrum diagram of each position.

[0015] Preferably, the calculation of the signal spectrum characteristics at each position includes the following specific steps:

[0016] The sum of the distribution variance of the amplitude at all frequencies on the spectrum graph of each position and the preset hyperparameter is used as the denominator;

[0017] The ratio of the mean value of the energy values ​​at all frequencies on the spectrum graph of each position to the denominator is used as the signal spectrum feature of each position.

[0018] Preferably, the step of determining the signal similarity between any two positions comprises the following specific steps:

[0019] Calculate the absolute value of the difference between the signal spectrum features at any two positions as a first difference value;

[0020] Calculate the absolute value of the difference between the amplitude proportions at any same frequency in the spectrum graph of any two positions and add the accumulated result at all the same frequencies as the second difference value;

[0021] The first difference value and the second difference value are used to determine the signal similarity between any two positions, wherein the signal similarity is negatively correlated with the first difference value and the second difference value, respectively.

[0022] Preferably, the method for obtaining the amplitude ratio is:

[0023] The ratio of the amplitude of each frequency in the spectrum graph at each position to the sum of the amplitudes of all frequencies is taken as the amplitude proportion of each frequency in the spectrum graph at each position.

[0024] Preferably, the determining of the information loss amount at each position includes the following specific steps:

[0025] The absolute value of the difference between the signal intensity of the transmitted signal and the received signal at each position during the porosity measurement is used as the numerator;

[0026] The ratio of the time interval between each signal transmitted and received at each position is added up over all signal transmission times at each position as the transmission loss at each position.

[0027] The ratio of the transmission loss to the average value of the signal strength at each position at all times is taken as the transmission loss amount at each position.

[0028] Preferably, the step of determining the correction coefficient of the distance measurement between any two locations includes the following specific steps:

[0029] For any two positions, the difference between the information loss during the porosity measurement at the two positions is calculated;

[0030] Determining a comprehensive difference parameter between the two positions using the signal similarity between the two positions and the difference, wherein the comprehensive difference parameter is positively correlated with the signal similarity and the difference;

[0031] The comprehensive difference parameters between all two positions are calculated respectively, all the comprehensive difference parameters are normalized by using a normalization method, and the normalization result of each comprehensive difference parameter is used as a correction coefficient of the distance metric between the corresponding two positions.

[0032] Preferably, the determining of the distance measurement value between any two locations comprises the following specific steps:

[0033] The sum of the correction coefficient of the distance measurement between any two positions and the constant parameter 1 is used as the correction weight, and the product of the correction weight and the spatial distance between any two positions is used as the distance measurement value between any two positions.

[0034] Preferably, the clustering method is used to cluster all locations based on the distance measurement between all locations, and the specific steps include the following:

[0035] The Markov clustering algorithm is used to divide all locations into several clusters based on the distance measurement between all locations.

[0036] Preferably, the porosity distribution of shield slurry is evaluated by using several clusters, including the following specific steps:

[0037] Counting the number of locations in each cluster, calculating the distribution variance of the number of locations in all clusters, and comparing the distribution variance with a preset threshold;

[0038] If the distribution variance is greater than a preset threshold, it is determined that the porosity distribution of the shield slurry is uneven;

[0039] If the distribution variance is less than or equal to a threshold value, it is determined that the porosity distribution of the shield slurry is uniform.

[0040] The beneficial effects of the technical solution of the present application are as follows: the present application obtains the signal spectrum characteristics of each position based on the amplitude distribution of different frequencies in the spectrum diagram of each position, obtains the signal similarity between the two positions based on the difference between the amplitudes at the same frequency in the spectrum diagram of the two positions and the difference between the signal spectrum characteristics of the two positions, thereby improving the accuracy of the signal difference analysis between different positions; obtains the signal strength at the time of transmission and the received signal strength, obtains the amount of information loss at each position during the detection process based on the mean of the signal strength data of all moments at each position and the difference between the signal strength at the time of transmission and the received signal strength, obtains the correction coefficient of the distance measurement between the two positions based on the signal similarity between the two positions and the difference between the amount of information loss during the detection process of the two positions, thereby improving the accuracy of the distance measurement between different positions; corrects the distance between the two positions based on the correction coefficient of the distance measurement between the two positions to obtain the distance measurement value between the two positions, clusters all positions based on the distance measurement value between the two positions to obtain several clusters, obtains the porosity distribution of the shield mud based on the several clusters, improves the accuracy of clustering, and thus improves the measurement accuracy of the porosity distribution uniformity of the shield mud. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 This is a flowchart of the implementation of the porosity distribution measurement method of shield mud used in shield construction provided in this application. DETAILED DESCRIPTION

[0043] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of the porosity distribution measurement method for shield slurry used in shield tunneling. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0044] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0045] The specific scheme of the method for measuring the porosity distribution of shield mud in shield construction provided by this application is described in detail below with reference to the accompanying drawings.

[0046] See also Figure 1 , which shows a flowchart of a method for measuring the porosity distribution of shield slurry in shield construction provided by an embodiment of the present application, the method comprising the following steps:

[0047] Step S100: using ultrasonic sensors at several different positions on the side surface of the shield machine, obtaining a signal strength data curve at each position.

[0048] It should be noted that, since the porosity of the shield mud is analyzed based on the signal strength data at different positions, it is necessary to first collect the signal strength data and perform the analysis based on the signal strength data.

[0049] Specifically, multiple ultrasonic sensors are evenly installed on the side surface of the shield machine, wherein the interval between two ultrasonic sensors is ten centimeters. The signal strength data curve within two hours is collected through the ultrasonic sensor at each position.

[0050] At this point, the signal strength data curve at each position of the shield machine is obtained.

[0051] Step S200: Using Fourier transform technology to obtain the spectrum diagram of each position based on the signal strength curve at each position, and determine the signal spectrum characteristics of each position based on the amplitude distribution in the spectrum diagram of each position; determine the signal similarity between any two positions based on the difference in amplitude distribution at the same frequency in the spectrum diagrams of any two positions and the difference between the signal spectrum characteristics.

[0052] It should be noted that in order to analyze the distribution of signal strength data in the frequency domain at each position over a period of time, that is, the energy distribution of signals at different frequencies, it is possible to first analyze the spectrum characteristics of the ultrasonic signal strength at each position by obtaining the spectrum diagram of each position over a period of time.

[0053] Specifically, the signal strength data curve of each position is discretized with one second as a time interval to obtain the signal strength data of each position at several consecutive moments. The spectrum diagram of each position is obtained based on the signal strength data of each position at several consecutive moments using the Fourier transform method. The process of obtaining the spectrum diagram by Fourier transform is a well-known technology and will not be described in detail here. Among them, the horizontal axis of the spectrum diagram represents different frequencies, and the vertical axis represents the amplitude. The spectrum diagram of each position is curve fitted using a fifth-order polynomial using the least squares method. The fitted curve is recorded as the spectrum curve of each position, and the function corresponding to the spectrum curve of each position is recorded as the spectrum curve function of each position.

[0054] According to the amplitude distribution of different frequencies in the spectrum diagram of each position, the signal spectrum characteristics of each position are obtained. As an embodiment, the specific calculation method of the signal spectrum characteristics of the i-th position is:

[0055]

[0056] Where, represents the signal spectrum characteristics at the i-th position, Represents the mean value of the energy value at all frequencies on the spectrum graph at the i-th position, Represents the distribution variance of the amplitude at all frequencies on the spectrum graph at the i-th position, It is a preset hyperparameter used to prevent the denominator from being 0. The value range is a positive number less than 0.01, here, The empirical value of is 0.001; the energy value at each frequency is equal to the square of the modulus of the amplitude corresponding to each frequency.

[0057] in, Indicates the strength of the signal at the i-th position, A larger value indicates a stronger signal strength and less interference. Reflects the distribution of all amplitudes on the spectrum graph at the i-th position, The larger the value, the more discrete the distribution of the amplitude; that is, The larger the value of , the higher the signal strength at the i-th position, the more concentrated the amplitude distribution, and the smaller the interference.

[0058] It should be noted that under normal circumstances, when there are no significant differences between any two locations, the difference in amplitude at the same frequency in the spectrum of the signals collected at the two locations can reflect the degree of signal similarity between the two locations. The smaller the difference in the signal spectrum characteristics between the two locations, the greater the degree of signal similarity between the two locations; the larger the difference in the signal spectrum characteristics between the two locations, the less similar the signals between the two locations.

[0059] Specifically, the ratio of the amplitude of each frequency in the spectrum graph at each position to the sum of the amplitudes of all frequencies is recorded as the amplitude proportion of each frequency in the spectrum graph at each position.

[0060] The degree of signal similarity between any two positions is obtained based on the difference between the amplitude proportions at the same frequency in the spectrum graph of any two positions and the difference between the signal spectrum characteristics of any two positions.

[0061] Specifically, the absolute value of the difference between the signal spectrum features at any two positions is calculated as the first difference value;

[0062] Secondly, the absolute value of the difference between the amplitude proportions at any same frequency in the spectrum graph of any two positions is calculated and the accumulated result at all the same frequencies is used as the second difference value;

[0063] Afterwards, the first difference value and the second difference value are used to determine the signal similarity between any two positions, and the signal similarity is negatively correlated with the first difference value and the second difference value, respectively.

[0064] It should be noted that negative correlation means that as the independent variable increases, the dependent variable decreases accordingly, and the two variables change in opposite directions. When the independent variable changes from large to small or from small to large, the dependent variable also changes from small to large or from large to small. The specific calculation relationship is determined by actual application and this application does not impose any special restrictions.

[0065] Preferably, as an embodiment, the specific calculation method of the signal similarity between any two positions is:

[0066]

[0067] Where, Indicates the Position and The signal similarity between the locations, 、 They are Position, The signal spectrum characteristics of each position, || is the absolute value calculation symbol, n represents the number of all frequencies in the spectrum graph of each position, 、 They are Position, The amplitude proportion of the kth frequency in the spectrum graph of the position.

[0068] Among them, the first difference value Reflects the Position and The smaller the value of the similarity of the signal strength at each position, the Position and The more similar the interference degree of the collected signals at the locations is, the more similar the second difference value is. It reflects the consistency of the amplitude distribution at the same frequency in the spectrum graph of the two positions. The larger the value of , the greater the signal similarity between the two locations.

[0069] At this point, the signal similarity between any two positions is obtained.

[0070] Step S300: Determine the amount of information loss at each position based on the difference between the transmitted signal and the received signal and the signal strength during the porosity measurement process at each position; determine the correction coefficient of the distance measurement between any two positions based on the signal similarity between the two positions and the information loss between the two positions.

[0071] It's important to note that when using ultrasonic sensors to measure shield mud porosity, different porosities produce different echo signals as the transmitted ultrasonic signal reflects off the medium. This means that the larger the porosity, the more information is lost during the reflection process. Therefore, this information loss during porosity measurement is used to provide feedback on the shield mud porosity.

[0072] Specifically, the signal strength and received signal strength at each location are recorded.

[0073] The information loss amount at each location during the detection process is obtained based on the difference between the mean value of the signal strength data at all times at each location, the mean value of the signal strength at all times of transmission at each location, and the mean value of the signal strength at all times of reception. Preferably, as an embodiment, the specific calculation process is as follows:

[0074] Here, we first calculate the transmission loss of each position based on the time interval between each transmitted signal and received signal and the change in signal strength at each position, and record the transmission loss of the i-th position as :

[0075]

[0076] Where M represents the number of times the signal is transmitted at the i-th position, 、 are the signal strengths of the mth transmitted and received signals at the i-th position, It is the time interval between the mth transmission and reception of the signal at the i-th position.

[0077] Secondly, the information loss of each position is calculated by combining the transmission loss of each position and the signal strength of the collected signal at each position. The transmission loss of the i-th position is recorded as :

[0078]

[0079] Where, Represents the mean value of the signal strength at all times at the i-th position.

[0080] in, It represents the degree of change between the transmitted signal strength and the received signal strength at the i-th position. The greater the degree of change, the greater the signal transmission loss at the i-th position during the porosity measurement process. The less interference the signal at the i-th position is at all times, the smaller the change in signal strength and the larger the mean value. The larger the value of , the greater the information loss at each position during the detection process.

[0081] Thus, the amount of information loss at each position during the porosity measurement is obtained.

[0082] Furthermore, for any two positions, when the degree of signal similarity between the two positions is greater, it means that the porosity of the shield mud at the two positions is more similar, and the distance metric between the two positions should be reduced when analyzing the porosity distribution uniformity of the shield mud by clustering the positions; at the same time, when the difference between the information loss amounts of the two positions during the detection process is smaller, it means that the impact of the porosity of the shield mud at the two positions on signal transmission is more similar, and the distance metric between the two positions should also be reduced when analyzing the porosity distribution uniformity of the shield mud by clustering the positions. Therefore, the present application obtains the correction coefficient of the distance metric between the two positions by the degree of signal similarity between the two positions and the difference between the information loss amounts of the two positions during the detection process.

[0083] Here, for any two positions, the signal similarity between the two positions is calculated first, and then the difference between the information loss amounts at the two positions during the porosity measurement process is calculated. The signal similarity and the difference are used to determine the comprehensive difference parameter between the two positions, wherein the comprehensive difference parameter is positively correlated with the signal similarity and the difference.

[0084] It should be noted that positive correlation means that as the independent variable grows, the dependent variable also grows, and the two variables change in the same direction. When the independent variable changes from large to small or from small to large, the dependent variable also changes from large to small or from small to large. The specific calculation relationship is determined by actual application and this application does not impose any special restrictions.

[0085] Furthermore, comprehensive difference parameters between all two positions are calculated, and all of the comprehensive difference parameters are normalized using a normalization method. The normalized result of each comprehensive difference parameter is used as a correction coefficient for the distance metric between the two positions. Normalization is a well-known technique in the field of data processing. Normalization methods include, but are not limited to, MAX-MIN normalization, Z-score normalization, and decimal scaling normalization. The specific process is not repeated here.

[0086] Preferably, as an embodiment, the specific calculation method of the correction coefficient is:

[0087]

[0088]

[0089] Where, It is Position and The comprehensive difference parameter between the positions, It is Position and The signal similarity between the locations, 、 They are Position, The amount of information lost during porosity measurement at each location;

[0090] It is Position and Correction factor for the distance measure between locations, 、 are the maximum and minimum values ​​of the comprehensive difference parameters between all positions, respectively.

[0091] At this point, the correction coefficient of the distance measurement between the two locations is obtained.

[0092] Step S400: Correct the distance between any two positions according to the correction coefficient of the distance measurement between any two positions, determine the distance measurement value between any two positions, cluster all positions based on the distance measurement value between all positions using a clustering method to obtain several clusters, and evaluate the porosity distribution of the shield mud through the several clusters.

[0093] Specifically, the side surface of the shield machine is unfolded to obtain a plane, which is recorded as the reference plane; each position has a corresponding point on the reference plane, and the spatial distance between any two positions is obtained.

[0094] Next, the sum of the correction coefficient for the distance metric between any two locations and a constant parameter 1 is used as a correction weight. The product of the correction weight and the spatial distance between any two locations is used as the distance metric between the two locations. A Markov clustering algorithm is then used to cluster all locations based on all of the distance metrics to obtain a number of clusters. The Markov clustering algorithm is a well-known technique, and the specific process is not described in detail here.

[0095] Furthermore, a threshold is preset , wherein this embodiment is based on This application does not impose any special restrictions on this. It may depend on the specific implementation situation.

[0096] Furthermore, the number of positions in each cluster is counted, and the distribution variance of the number of positions in all clusters is calculated. When the distribution variance is less than or equal to a threshold, the porosity distribution of the shield mud is determined to be uniform; when the distribution variance is greater than the threshold, the porosity distribution of the shield mud is determined to be uneven.

[0097] At this point, this embodiment is completed.

[0098] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

[0099] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for measuring the porosity distribution of shield slurry used in shield construction, characterized in that: The method comprises the following steps: Using ultrasonic sensors at several different positions on the side surface of the shield machine, a signal strength data curve at each position is obtained; The Fourier transform technique is used to obtain the spectrum of each location based on the signal strength curve at each location, and the signal spectrum characteristics of each location are calculated based on the concentration and amplitude of the amplitude distribution at different frequencies in the spectrum of each location; Taking the difference between the signal spectrum characteristics of any two positions as the first difference value; The cumulative sum of all amplitude ratio differences at the same frequency in the spectrum graphs of any two positions is taken as the second difference value; Determining a signal similarity between any two positions using the first difference value and the second difference value, wherein the signal similarity is negatively correlated with the first difference value and the second difference value, respectively; The difference between the signal intensity of the transmitted signal and the received signal at each position during the porosity measurement process is used as the numerator; The ratio of the time interval between each signal transmitted and received at each position is added up over all signal transmission times at each position as the transmission loss at each position. The ratio of the transmission loss to the average signal intensity at each position at all times is used as the transmission loss amount at each position; for any two positions, the difference between the information loss amounts at the two positions during the porosity measurement process is calculated; Determining a comprehensive difference parameter between the two positions using the signal similarity between the two positions and the difference, wherein the comprehensive difference parameter is positively correlated with the signal similarity and the difference; Using the normalized processing result of each of the comprehensive difference parameters as a correction coefficient for the distance metric between the corresponding two positions; correcting the distance between any two positions according to the correction coefficient of the distance metric between any two positions, determining the distance metric value between any two positions, clustering all positions based on the distance metric values ​​between all positions using a clustering method to obtain a number of clusters, counting the number of positions in each cluster, calculating the distribution variance of the number of positions in all clusters, and comparing the distribution variance with a preset threshold; If the distribution variance is greater than a preset threshold, it is determined that the porosity distribution of the shield slurry is uneven; If the distribution variance is less than or equal to a threshold value, it is determined that the porosity distribution of the shield slurry is uniform.

2. The porosity distribution measurement method for shield slurry in shield construction according to claim 1, characterized in that: The specific steps of calculating the signal spectrum characteristics at each position are as follows: The sum of the distribution variance of the amplitude at all frequencies on the spectrum graph of each position and the preset hyperparameter is used as the denominator; The ratio of the mean value of the energy values ​​at all frequencies on the spectrum graph of each position to the denominator is used as the signal spectrum feature of each position.

3. The porosity distribution measurement method for shield slurry used in shield construction according to claim 1, characterized in that: The method for obtaining the amplitude ratio is as follows: The ratio of the amplitude of each frequency in the spectrum graph at each position to the sum of the amplitudes of all frequencies is taken as the amplitude proportion of each frequency in the spectrum graph at each position.

4. The porosity distribution measurement method for shield slurry used in shield construction according to claim 1, characterized in that: The specific steps of determining the distance measurement value between any two locations are as follows: The sum of the correction coefficient of the distance measurement between any two positions and the constant parameter 1 is used as the correction weight, and the product of the correction weight and the spatial distance between any two positions is used as the distance measurement value between any two positions.

5. The method for measuring porosity distribution of shield slurry used in shield construction according to claim 1, characterized in that: The clustering method is used to cluster all locations based on the distance measurement values ​​between all locations, and the specific steps include the following: The Markov clustering algorithm is used to divide all locations into several clusters based on the distance measurement between all locations.

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