Municipal concrete drainage pipeline leakage detection and positioning method

Abstract

The invention discloses a municipal concrete drainage pipeline leakage detecting and positioning method. Arranging a plurality of parallel measuring lines above the to-be-detected pipeline to form a detection grid, and collecting radar full-waveform data; known pipeline space attributes are introduced as constraints, and three-dimensional space distribution of dielectric constants and conductivity of soil around a pipeline is obtained through full-waveform inversion; respectively substituting the parameters into a soil dielectric constant model and a conductivity model, and jointly inverting three-dimensional distribution of the water content and porosity of the soil; the spatial position of a leakage point or section is judged by analyzing the three-dimensional morphological characteristics of the area with abnormal water content and porosity; a leakage severity index is further calculated to quantitatively evaluate the leakage grade, and verification can be carried out in combination with a pipeline endoscopic detection result.

Description

Technical Field

[0001] This invention belongs to the field of non-destructive detection and condition assessment of underground pipeline defects, and particularly relates to a method for detecting and locating leakage in municipal concrete drainage pipes. Background Technology

[0002] Municipal drainage pipe networks are crucial urban infrastructure. During their long service life, due to factors such as material aging, uneven foundation settlement, and external load impacts, cracks or deformations often develop in the pipes and their joints, leading to sewage leakage. This leakage not only wastes water resources and pollutes the environment but also continuously erodes the surrounding backfill soil, weakening its structural strength and potentially causing secondary disasters such as road collapses, posing a serious threat to urban and public safety.

[0003] Timely detection and precise location of potential leaks in drainage pipes are crucial for accident prevention and targeted remediation. Traditional detection methods primarily rely on direct excavation for inspection. While the results are readily apparent, they suffer from significant drawbacks such as large-scale engineering work, high costs, road damage, and traffic disruption, making them unsuitable for large-scale pipeline health surveys and periodic monitoring. Therefore, various non-destructive testing technologies have emerged. Closed-circuit television (CCTV) technology can directly observe the internal condition of pipes, but it is ineffective in detecting changes in the surrounding soil. Acoustic testing methods are sensitive to specific types of active leaks, but are heavily affected by environmental noise and cannot assess the cumulative soil impact of historical leaks.

[0004] Ground-penetrating radar (GPR), a geophysical method sensitive to differences in the electrical properties of underground media, boasts advantages such as rapid detection, high resolution, and non-destructive operation, and has been introduced into underground pipeline detection and shallow subsurface defect investigation. Its traditional application typically relies on data collected from a single survey line, qualitatively determining the presence of anomalies by analyzing the travel time, amplitude, or waveform characteristics of reflected waves. However, this method faces multiple bottlenecks when addressing the specific problem of pipeline leakage. First, single profile data is insufficient to reconstruct the three-dimensional spatial morphology of the leakage-affected area, making it impossible to accurately determine the specific location of the leakage point along the pipeline's longitudinal direction, nor to quantify the diffusion range of sewage in the soil. Second, conventional interpretation methods underutilize the rich dielectric information contained in the full waveform data of radar, relying heavily on empirical interpretation, resulting in poor quantification and objectivity of the inversion results. Furthermore, in complex urban underground environments, if information such as the precise spatial location of known pipelines is not effectively incorporated as constraints, the ambiguity of the inversion process will significantly increase, easily leading to false anomalies or location errors. Finally, existing methods usually stop at a binary judgment of "whether there is leakage" and lack a quantitative assessment system that links geophysical anomalies with the severity of engineering defects. As a result, their conclusions are difficult to effectively support the decision on repair priorities.

[0005] Therefore, current technology lacks a detection method that can comprehensively utilize multi-dimensional radar data, fuse prior information, achieve precise three-dimensional spatial localization of pipeline leaks, and quantitatively assess the severity of leaks. This deficiency limits the in-depth application of ground-penetrating radar technology in the precise operation and maintenance of drainage pipe networks. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for detecting and locating leaks in municipal concrete drainage pipes.

[0007] The specific details of the invention are as follows:

[0008] A method for detecting and locating leakage in municipal concrete drainage pipes, based on non-contact ground-penetrating radar full-waveform data acquired above the pipe, obtains the dielectric constant and conductivity distribution of the soil surrounding the pipe through full-waveform inversion, and then jointly inverts the soil moisture content and porosity distribution to determine the leakage situation and its affected area. The method includes the following steps:

[0009] Step 1: Lay ground-penetrating radar survey lines above the drainage pipe to be inspected and collect full radar waveform data containing arrival time, amplitude and phase information;

[0010] Step 2: Based on the radar full waveform data, obtain the spatial distribution of dielectric constant and conductivity of the soil around the pipeline using the full waveform inversion method;

[0011] Step 3: Introduce the dielectric constant and conductivity into the soil dielectric constant model and conductivity model respectively, and invert the water content and porosity distribution of the soil around the pipeline;

[0012] Step 4: Based on the abnormal characteristics of water content and porosity in the inversion results, determine whether there is pipeline leakage and the area of ​​external seepage:

[0013] Normal, disease-free condition: The moisture content, porosity, and electrical conductivity of the soil around the pipeline are close to the background values, with uniform spatial distribution and no obvious abnormalities;

[0014] Wastewater seepage occurs when the water content and porosity of the soil around the pipe are significantly higher than the background value, forming a continuous abnormal zone that corresponds spatially to the location of the drainage pipe. In this case, it is determined that there is pipe leakage, and the seepage area is identified by the size of the abnormal zone.

[0015] Preferably, in step 1, the deployment of ground-penetrating radar survey lines specifically involves: deploying multiple parallel ground-penetrating radar survey lines along the direction of the drainage pipeline to be inspected, forming a detection grid covering the pipeline and the soil on both sides; in step 2, the three-dimensional spatial distribution of the dielectric constant and conductivity of the soil surrounding the pipeline is obtained.

[0016] Furthermore, the feature is that, in step 1, the spacing between the multiple parallel measuring lines is determined according to the pipe diameter and burial depth of the drainage pipe, and its value ranges from 0.5 times to 1.5 times the pipe diameter, and is not greater than half of the wavelength corresponding to the center frequency of the antenna, so as to ensure the spatial sampling rate of anomalies around the pipe.

[0017] Furthermore, the feature is that, before performing the full waveform inversion in step 2, the known spatial attribute information of the drainage pipeline is introduced as an inversion constraint condition, and the known spatial attribute information includes one or more of the following: the horizontal position of the pipeline centerline, the burial depth, and the pipe diameter.

[0018] Preferably, in step 2, when performing full waveform inversion based on radar detection data, multi-band joint inversion, iterative optimization, or regularization constraint methods can be used to improve the stability and accuracy of the inversion results of the spatial distribution of dielectric constant and conductivity of the soil around the pipeline.

[0019] Preferably, in step 3, the dielectric constant model and the conductivity model are used to form a dual constraint, and the water content and porosity of the soil around the pipeline are solved together to improve the accuracy of the inversion results.

[0020] Preferably, in step 4, a schematic diagram of the water content and porosity of the soil around the pipeline is generated to visually display the leakage range.

[0021] Preferably, in step 4, after determining that there is pipeline leakage, the leakage point is further located: in the three-dimensional spatial distribution of water content and porosity, the three-dimensional morphology of the abnormal area is identified; if the abnormal area presents a local high-value core that intersects with or is adjacent to the pipeline on the vertical cross-section, and is discontinuously distributed along the pipeline direction on the horizontal plane, then the vertical projection position of the high-value core on the pipeline centerline is determined as a potential pipeline leakage point or leakage section.

[0022] Furthermore, in step 4, a leakage severity index is calculated for the location identified as a leakage point or leakage section. The index is obtained by weighted summation of the relative deviations of the abnormal water content and abnormal porosity values ​​obtained by inversion at the location with the background values, and is used to quantitatively assess the severity level of leakage.

[0023] Furthermore, the information on potential pipeline leak points or leak sections located is spatially compared and verified with the results of pipeline endoscopic inspection. If the endoscopic inspection finds structural defects at the corresponding location, then that location is finally confirmed as the leak source.

[0024] The present invention has at least the following beneficial effects:

[0025] 1. This invention, based on ground-penetrating radar technology, obtains the three-dimensional spatial distribution of dielectric constant and conductivity of the soil surrounding the pipeline through full-waveform inversion. Combined with a soil physical model, it achieves quantitative inversion of key physical properties such as soil moisture content and porosity. This not only effectively assesses the degree of pipeline leakage and identifies areas of sewage infiltration, but also enables precise spatial location of leakage points or sections along the longitudinal direction of the pipeline through three-dimensional data. Furthermore, it is suitable for large-scale surveys of potential hazards and defects in municipal drainage pipelines, as well as detailed inspections of key sections.

[0026] 2. This invention employs a strategy of jointly inverting the dielectric constant and conductivity to solve for the dual physical property parameters of water content and porosity, significantly improving the accuracy and reliability of the inversion results. Based on the obtained high-precision spatial distribution information of soil water content and porosity, combined with relevant parameters determined by indoor experiments, the soil compaction distribution can be further calculated and determined, thus providing direct and quantitative data support for the scientific evaluation of the long-term stability of the backfill soil around the pipeline and the roadbed above it.

[0027] 3. By introducing the precise spatial properties of the known pipeline as constraints for the full waveform inversion, and combining it with multi-line three-dimensional gridded detection data, the ambiguity of the inversion process is effectively suppressed, and the accuracy and reliability of the medium parameter inversion near the pipeline, the core target body, are greatly improved. This makes the spatial correspondence between leakage anomaly signals and pipeline structure clearer, laying a solid foundation for accurate positioning.

[0028] 4. Based on the determination of leakage, this invention further constructs a quantitative assessment model for the severity of leakage. By calculating the weighted deviation index of water content and porosity anomalies relative to background values, a quantitative classification of leakage severity levels is achieved, enabling the detection results to move from qualitative judgment to quantitative assessment, and providing a scientific and intuitive basis for priority decision-making and risk management in pipeline maintenance.

[0029] 5. By spatially comparing and verifying the potential leak points located by this method with the results of pipe endoscopy, a dual evidence chain of "external physical property anomaly detection" and "internal structural defect inspection" is formed. This multi-source data fusion verification mechanism greatly improves the credibility and engineering guidance value of the leak diagnosis results, avoiding the limitations of a single detection method. Attached Figure Description

[0030] Figure 1 : Schematic diagram of the geological distribution in the lower part of the area to be tested, where 1 is silty soil layer, 2 is silty clay soil layer, 3 is clay soil layer, 4 is communication cable protection pipe, 5 is sewage pipe, 6 is gas pipe, and 7, 8 and 9 are anomalies distributed in 1, 2 and 3 respectively.

[0031] Figure 2 Flowchart of raw data processing for ground-penetrating radar;

[0032] Figure 3 Radar waveforms generated based on forward modeling;

[0033] Figure 4 Flowchart of the full waveform inversion algorithm;

[0034] Figure 5 Schematic diagram of conductivity distribution based on full waveform inversion;

[0035] Figure 6 Schematic diagram of dielectric constant distribution based on full waveform inversion;

[0036] Figure 7 Schematic diagram of water content distribution in the soil outside the pipe after sewage seepage. Detailed Implementation

[0037] The following embodiments illustrate the present invention in detail. In the description of these embodiments, specific details such as particular system structures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0038] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0039] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0040] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0041] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0042] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0043] Example 1

[0044] This embodiment is intended to illustrate the technology of the present invention.

[0045] Step 1: On-site collection of ground-penetrating radar data: Before the test, the as-built data of the drainage pipeline and the geological data of the area to be tested should be collected. On-site inspection and recording of surface obstacles and strong electromagnetic interference sources should be carried out. The antenna frequency should be matched according to the pipeline burial depth and imaging quality. The survey line should be arranged perpendicular to the pipeline direction. After obtaining the raw ground-penetrating radar data, image processing should be performed, including background removal and automatic gain control, to improve the imaging quality.

[0046] Step 2: Ground Penetrating Radar Full Waveform Inversion: Based on measured radar data, the dielectric parameter distribution of the underground medium is reconstructed. Through continuous forward modeling iterations, the optimal fit between the simulated reflected wave data and the measured ground penetrating radar data is obtained. The defined objective function is:

[0047]

[0048] In the formula, The number of sources. The number of receivers for each source, This indicates the length of the radar recording window. For the first The spatial coordinate vectors of each receiver It is the first Individual stimulation in The observation data received at the location It is the first Simulation data from a source pair for forward modeling of a guessing model, parameter vector Given the dielectric constant vector to be solved and conductivity vector composition.

[0049] To make the object's outline clearer within the background region, total variation regularization is introduced, resulting in a new objective function:

[0050]

[0051] in, As a regularization factor, Represents the total variation operator

[0052] A multi-scale inversion strategy is introduced into the full waveform inversion, decomposing the inversion problem into different scales. First, inversion is performed in the low-frequency band. Then, the inversion results from the low-frequency band are used as the initial model for the high-frequency band inversion, and frequency-by-frequency inversion is performed to obtain the global extremum, avoiding getting trapped in local minima and cycle jumps. Simultaneously, the L-BFGS algorithm with parameter adjustment capabilities is introduced into the full waveform inversion to reduce the crosstalk effect of the two parameters during inversion, achieving simultaneous inversion of dielectric constant and conductivity while reducing the algorithm's time complexity.

[0053] Step 3: Inversion of soil physical properties outside the pipe: The dielectric constant model and the conductivity model of the soil are combined into a joint equation. The dielectric constant vector obtained in step 2 is then used to invert the soil properties. Substituting the dielectric constant model of the soil, preferably the Looyenga dielectric constant model, the conductivity vector obtained in step 2 is used. Substituting the soil electrical conductivity model, preferably the Archie electrical conductivity model, and simultaneously solving for the volumetric water content. and porosity In particular, when the region appears and At that time, it was determined to be a cavity, that is , The fitting parameters in the formula should be calibrated before detection.

[0054] The Looyenga dielectric constant model is expressed as:

[0055]

[0056] In the formula, , , and These represent the volumes of air, soil particles, water, and soil sample, respectively. , , and These represent the relative permittivity of air, soil particles, water, and soil samples, respectively. , , , These are the parameters to be fitted.

[0057] The above equation can be expressed as the relative permittivity of the soil sample. Regarding volumetric water content and porosity The expression:

[0058] ,

[0059]

[0060] Substituting into the original equation, we get:

[0061]

[0062] Generalized Archie conductivity model:

[0063]

[0064] In the formula, Pore ​​fluid conductivity, denoted as saturation, and m and n are fitting parameters.

[0065] Rewrite the above formula as soil conductivity. Regarding volumetric water content and porosity The expression:

[0066]

[0067] Substituting into the original equation, we get:

[0068]

[0069] Step 4: Determine the sewage seepage situation: Draw a distribution map based on the soil physical property parameters around the pipe obtained by inversion, and identify abnormal areas. When the water content and porosity of the soil around the pipe are significantly higher than the background value and a continuous abnormal area appears, it is determined that there is sewage seepage at this location. The seepage area is identified by the area of ​​the abnormal area.

[0070] Example 2

[0071] The leakage detection method for municipal concrete drainage pipes 5 includes the following steps:

[0072] Reference Appendix Figure 1 The soil layers, from shallow to deep, are 1, 2, and 3, which are silt, silty clay, and clay, respectively. Pipes 4, 5, and 6 are, respectively, communication cable protection pipes, sewage pipes, and gas pipes. Specific parameters are shown in Table 1. Anomalies 7, 8, and 9 are distributed in soil layers 1, 2, and 3, respectively. Leakage detection is performed on municipal concrete drainage pipe 5.

[0073] Table 1 Pipeline Parameters

[0074]

[0075] Step 1: Collect ground-penetrating radar data

[0076] Reference Appendix Figure 2 Ground penetrating radar is used to detect the target object. The survey line should be perpendicular to the direction of drainage pipe 5 and kept as straight as possible. Pay attention to clearing surface obstacles and ensure that the antenna moves smoothly. Record the horizontal position, two-way travel time and signal amplitude data. The antenna frequency is selected as 800MHz, the time window length is set to 20ns, and the measurement point spacing is 0.05m. After acquiring the raw data, image processing is performed to improve the data quality.

[0077] Background removal algorithms are used to suppress ground clutter and system noise. Time-varying gain compensation is then applied to compensate for electromagnetic wave attenuation with depth, making the signal strength more consistent across shallow and deep regions. In the filtering stage, bandpass filtering is used to retain effective signals, combined with Radon transform to eliminate multiple wave interference. During coordinate transformation, velocity analysis is used to convert two-way travel time into depth coordinates.

[0078] Step 2: Ground Penetrating Radar Full Waveform Inversion

[0079] Data preparation: The preprocessed ground-penetrating radar data, including the horizontal coordinates, two-way travel time, and signal amplitude of each point, are converted into waveform datasets.

[0080] Initial model construction: Based on prior geological information, an initial spatial distribution of dielectric constant and conductivity is constructed.

[0081] Forward Simulation: Reference Appendix Figure 3 The forward modeling is performed using the finite-difference time-domain method, with the current dielectric parameter model and antenna parameters as inputs, to calculate the simulated waveform dataset.

[0082] Residual calculation and convergence judgment: Calculate the norm residual between the simulated waveform data and the measured waveform data, compare the residual with a preset threshold, if the residual is less than the threshold or the number of iterations reaches the preset upper limit, then proceed to the result output stage; otherwise, output the residual signal and proceed to the gradient update stage.

[0083] Gradient update and model correction: The gradient of the residual with respect to the medium parameters is calculated using the adjoint state method, and the gradient is iteratively updated using the L-BFGS optimization algorithm to correct the current medium parameter model.

[0084] Output results: The converged medium parameter distribution is converted into a visual graph and output.

[0085] Step 3: Inversion of soil physical properties outside the pipe

[0086] The dielectric parameter distribution of the underground medium was obtained through full waveform inversion. The dielectric parameter values ​​of the soil surrounding drainage pipe 5 were then substituted into the derived Looyenga and Archie models for calculation. The fitting parameters in the models... , , , Based on the actual site conditions, samples were taken for calibration, and the water content and porosity of the soil around the pipeline were calculated.

[0087] Looyenga model:

[0088]

[0089] Archie model:

[0090]

[0091] Step 4: Assess the extent of sewage leakage.

[0092] Based on the soil physical property parameters obtained from the inversion, a distribution map is drawn to identify abnormal areas: As shown in the figure, the distribution map of soil moisture content outside a certain part of drainage pipe 5 is shown. The dark blue background represents the low moisture content area, the red background represents the high moisture content area, and the colored ring represents the diffusion of water to the surrounding soil. The outermost layer of the colored ring is connected to the dark blue background, which clearly defines the pollution range of the infiltrated water in the soil.

[0093] Example 3

[0094] Based on Example 1, in order to further improve the ability to analyze the morphology of leakage spaces, we implemented multi-line three-dimensional data acquisition and full waveform inversion imaging.

[0095] In step 1, we laid out five parallel ground-penetrating radar (GPR) survey lines above the target pipe section along the pipe's direction, with a line spacing of 0.3 meters, forming a detection grid covering the pipe and the surrounding soil. During data acquisition, the starting coordinates and survey point positions of each survey line were strictly recorded to ensure the accuracy of data spatial positioning.

[0096] In step 2, the full waveform inversion process is crucial. (See attached document.) Figure 2 This figure shows radar waveforms generated based on forward modeling, containing rich information such as direct waves, pipe-reflected waves, and possible anomalous body scattered waves. It forms the data foundation for full waveform inversion. (See attached reference.) Figure 4 This flowchart clearly outlines the core iterative process of the full waveform inversion algorithm, including forward modeling, residual calculation, gradient update, and model correction. Following this flowchart, we treated the data from the five survey lines as a single dataset for 3D joint inversion. During the inversion iteration, we strictly adhered to the attached... Figure 4The logic shown involves continuously comparing the residuals between simulated and measured data, and then using the L-BFGS algorithm to optimize and update the dielectric parameter model of the underground medium.

[0097] The final output is the three-dimensional spatial distribution of the dielectric constant and conductivity of the soil surrounding the pipeline. (See attached reference.) Figure 5 and attached Figure 6 These figures respectively illustrate schematic diagrams of conductivity and dielectric constant profiles obtained through full waveform inversion. In this embodiment, we obtained a similar but more complex three-dimensional data volume. By analyzing these three-dimensional distribution maps, it can be clearly seen that around a certain location in the pipe, both the dielectric constant and conductivity exhibit an anomalous region that differs significantly from the surrounding background medium. This anomalous volume displays a specific shape and distribution direction in three-dimensional space.

[0098] Example 4

[0099] Based on the three-dimensional dielectric parameter distribution obtained in Example 3, we perform step 3 to perform accurate soil physical property parameter inversion and achieve quantitative identification of leakage.

[0100] We simultaneously substituted the three-dimensional dielectric constant and conductivity data obtained from the inversion in Example 3 into the Looyenga and Archie models calibrated using local soil samples. By solving this set of joint equations, we obtained the three-dimensional distribution of soil volumetric water content θ and porosity n within the study area. This process fully utilizes the different response characteristics of dielectric constant and conductivity to soil water content and pore structure, forming a dual constraint, which significantly improves the uniqueness and accuracy of the inversion results.

[0101] Reference Appendix Figure 7 The figure illustrates the distribution of water content in the soil outside the pipe after sewage infiltration. In this embodiment, we generated a similar three-dimensional water content distribution map. Through analysis of this three-dimensional data volume, we not only identified the attached... Figure 7 The annular high-moisture-content anomaly zone shown can be calculated more precisely, including its volume, the highest central moisture content, and its longitudinal extension along the pipeline. For example, we can quantitatively determine that there is an abnormally saturated body with an average volumetric moisture content exceeding 35% and a volume of approximately 2.5 cubic meters beneath the pipeline between chainages K0+122 and K0+128. This quantitative description elevates the assessment of leakage scale from qualitative judgment to quantitative analysis, providing precise data support for subsequent risk assessment and remediation plan design.

[0102] Example 5

[0103] This embodiment comprehensively applies the aforementioned inversion results to demonstrate how to perform leakage mode analysis and diagnosis through multi-parameter profiles.

[0104] After completing the physical property parameter inversion in Example 4, we extracted a typical vertical profile passing through the center of the suspected leakage point. On this profile, we simultaneously present the jointly interpreted dielectric constant profile, conductivity profile, water content profile, and porosity profile obtained from the inversion.

[0105] Reference Appendix Figure 5 Appendix Figure 6 and similar appendices Figure 7 Based on the results, we make a comprehensive judgment. Normal dense soil typically exhibits a high dielectric constant. Electrical conductivity Moisture content The values ​​are all low and evenly distributed. When water leakage occurs in the pipeline, it may mainly manifest as low water content. And the increase in dielectric constant ε, while the conductivity The change is not significant. When sewage leakage occurs, the high ion content of the sewage will simultaneously affect the dielectric constant. Electrical conductivity and moisture content Significant increase, as shown in the appendix Figure 5 Appendix Figure 6 and attached Figure 7 The abnormal features revealed by collaboration.

[0106] By comparing the spatial agreement, amplitude, and morphology of these abnormal physical property parameters, the nature of the leaking fluid and the severity of the leak can be further inferred. For example, a region with highly overlapping and large anomalies in water content θ and conductivity σ strongly indicates continuous leakage of domestic sewage. This comprehensive diagnostic model based on multi-parameter fusion greatly improves the interpretability and reliability of leakage detection results, and can effectively distinguish between different types of pipeline defects and external interference.

[0107] Example 6

[0108] Based on the quantitative identification results of Examples 4 and 5, we constructed and applied a quantitative assessment model for leakage severity to achieve an objective classification of leakage hazard levels.

[0109] The core of this model is defining a leakage severity index, denoted as . This index is determined by two key physical properties: volumetric water content. With porosity The degree of anomaly is jointly determined. First, background values ​​need to be determined in the normal soil outside the leakage-affected zone. and Then, within the abnormal leakage zone, the maximum value of the abnormal amplitude of each parameter is taken and recorded as follows: and .

[0110] Leakage Severity Index Calculated using the following formula:

[0111]

[0112] in, and These are weighting coefficients, and their sum is 1. Specific values ​​can be determined based on local soil type and engineering experience. For example, for silt, which is more sensitive to changes in moisture content, a weighting coefficient of 1 can be used. =0.7, =0.3; for clays with stronger structure, a value of 0.3 can be taken. =0.5, =0.5.

[0113] We will calculate The value is compared with a preset grading threshold to classify the severity of leakage into different levels, for example:

[0114] when A leakage of less than 0.5 μL is defined as "minor" leakage.

[0115] When 0.5 ≤ A leakage rate less than 1.0 is defined as "moderate".

[0116] when A leakage of ≥ 1.0 is defined as “serious” leakage.

[0117] Applying this model to the abnormal region mentioned in Example 4, if the measured = 0.30 (meaning the moisture content increased from the background value of 0.25 to 0.55). = 0.10 (porosity increases from 0.35 to 0.45), take =0.6, =0.4, then = 0.6*(0.30 / 0.25) + 0.4*(0.10 / 0.35) ≈ 0.72 + 0.11 ≈ 0.83, which is therefore classified as "moderate" leakage. This quantitative result provides a direct and scientific basis for making differentiated remediation decisions.

[0118] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for detecting and locating leakage in municipal concrete drainage pipes, characterized in that: Based on the full waveform data of ground penetrating radar collected non-contactly above the pipeline, the dielectric constant and conductivity distribution of the soil around the pipeline are obtained through full waveform inversion. Then, the soil moisture content and porosity distribution are obtained through joint inversion, and the leakage situation and the scope of influence are determined accordingly.

2. The method for detecting and locating leakage in municipal concrete drainage pipes according to claim 1, characterized in that, Includes the following steps: Step 1: Lay ground-penetrating radar survey lines above the drainage pipe to be inspected and collect full radar waveform data containing arrival time, amplitude and phase information; Step 2: Based on the radar full waveform data, obtain the spatial distribution of dielectric constant and conductivity of the soil around the pipeline using the full waveform inversion method; Step 3: Introduce the dielectric constant and conductivity into the soil dielectric constant model and conductivity model respectively, and invert the water content and porosity distribution of the soil around the pipeline; Step 4: Based on the abnormal characteristics of water content and porosity in the inversion results, determine whether there is pipeline leakage and the area of ​​external seepage: Normal, disease-free condition: The moisture content, porosity, and electrical conductivity of the soil around the pipeline are close to the background values, with uniform spatial distribution and no obvious abnormalities; Wastewater seepage occurs when the water content and porosity of the soil around the pipe are significantly higher than the background value, forming a continuous abnormal zone that corresponds spatially to the location of the drainage pipe. In this case, it is determined that there is pipe leakage, and the seepage area is identified by the size of the abnormal zone.

3. The method for detecting and locating leakage in municipal concrete drainage pipes according to claim 2, characterized in that, In step 1, the deployment of ground-penetrating radar survey lines specifically involves: deploying multiple parallel ground-penetrating radar survey lines along the direction of the drainage pipeline to be inspected, forming a detection grid covering the pipeline and the soil on both sides; in step 2, the three-dimensional spatial distribution of the dielectric constant and conductivity of the soil surrounding the pipeline is obtained.

4. The method for detecting and locating leakage in municipal concrete drainage pipes according to claim 3, characterized in that, In step 1, the spacing between the multiple parallel measuring lines is determined according to the diameter and burial depth of the drainage pipe, and its value ranges from 0.5 to 1.5 times the pipe diameter, and is not greater than half of the wavelength corresponding to the center frequency of the antenna.

5. The method for detecting and locating leakage in municipal concrete drainage pipes according to claim 3, characterized in that, Before performing the full waveform inversion in step 2, the known spatial attribute information of the drainage pipeline is introduced as an inversion constraint. The known spatial attribute information includes one or more of the following: the horizontal position of the pipeline centerline, the burial depth, and the pipe diameter.

6. The method for detecting and locating leakage in municipal concrete drainage pipes according to claim 2, characterized in that, In step 2, when performing full waveform inversion based on radar detection data, multi-band joint inversion, iterative optimization, or regularization constraint methods can be used.

7. A method for detecting and locating leakage in municipal concrete drainage pipes according to claim 2, characterized in that, In step 3, the dielectric constant model and the conductivity model are used to form a dual constraint, and the water content and porosity of the soil around the pipeline are solved together.

8. A method for detecting and locating leakage in municipal concrete drainage pipes according to claim 2, characterized in that, In step 4, a schematic diagram of the water content and porosity of the soil around the pipeline is generated to visually display the leakage range.

9. A method for detecting and locating leakage in municipal concrete drainage pipes according to claim 2, characterized in that, In step 4, after determining that there is pipeline leakage, the leakage point is further located: in the three-dimensional spatial distribution of water content and porosity, the three-dimensional morphology of the abnormal area is identified; if the abnormal area presents a local high-value core that intersects with or is adjacent to the pipeline on the vertical cross section, and is discontinuously distributed along the pipeline direction on the horizontal plane, then the vertical projection position of the high-value core on the pipeline centerline is determined as a potential pipeline leakage point or leakage section.

10. A method for detecting and locating leakage in municipal concrete drainage pipes according to claim 9, characterized in that, In step 4, for the locations identified as leakage points or leakage sections, a leakage severity index is further calculated. The index is obtained by weighted summation of the relative deviations of the water content anomaly value and porosity anomaly value obtained by inversion at the location with the background value. The information on potential pipeline leak points or leak sections obtained from the location is spatially compared and verified with the results of pipeline endoscopic inspection. If the endoscopic inspection finds structural defects at the corresponding location, then the location is finally confirmed as the leak source.

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