An airport pavement structure detection method

Abstract

This invention relates to a method for detecting airport pavement structures. The method employs active surface wave (FWD) detection technology, comprising: acquiring vibration signals through a seismic source and extracting surface wave dispersion curves; and inverting the underground shear wave velocity structure of the airport based on the propagation characteristics of the surface waves in the underground medium, thereby detecting the airport pavement structure. Experiments using acquisition parameters revealed that a small hammer is suitable for testing shallow areas, while FWD is suitable for testing deep areas; 1 to 3 hammer blows per shot point are optimal; a smaller FWD hammer load setting results in a shallower detection depth, while a larger setting results in a deeper detection depth; when using FWD as the seismic source, a minimum offset distance of 6-7m is optimal; the pavement spacing setting needs to consider both the number of detectors, and optimal values ​​for both are necessary to improve lateral resolution, with 0.5-1m being optimal during testing. The test results obtained using this invention are consistent with the current condition of an airport pavement structure, demonstrating the feasibility of active surface wave detection technology in airport pavement structure detection.

Description

Technical Field

[0001] This invention relates to the field of airport pavement inspection technology, and more specifically, to a method for inspecting airport pavement structures. Background Technology

[0002] With the rapid development of the aviation industry, the development of domestic airports is also accelerating, leading to higher requirements for pavement structures. Airport pavement structures, under the long-term effects of aircraft and vehicle loads and temperature variations, have long suffered from various defects, including cracks, damage, and misalignment, posing potential safety risks to airport operations and remaining a key focus of domestic civil aviation. To analyze the causes of pavement structural defects, airports have used various detection methods, such as core drilling and ground-penetrating radar (GPR). However, core drilling has a limited detection range, is time-consuming and labor-intensive, and causes significant damage to the airport pavement structure. Therefore, GPR is currently the most widely used detection technology at airports. This non-destructive testing technology greatly reduces detection costs and time; however, its detection depth is shallow, only revealing the location and type of defects, lacking the physical parameters needed for further analysis of the pavement structural defects.

[0003] For example, Chinese invention patent CN 120374611 B discloses a method and system for detecting airport pavement damage. This method involves installing airport pavement monitoring equipment on an airport inspection vehicle used for inspecting the airport. The equipment acquires 2D pavement images and 3D pavement point cloud data in real time, driven by the vehicle. It then extracts target 2D images and target 3D point cloud data related to suspected pavement damage areas and performs joint low-rank sparse decomposition and fusion processing to obtain a target fused image that meets the requirements of multimodal monitoring error correction. A pavement damage detection model is then used to identify pavement damage in this fused image to obtain the actual pavement damage status information at the suspected damage areas. This allows for rapid and highly accurate automatic measurement of airport pavement damage status across the entire airport under inspection, with the assistance of the airport inspection vehicle. However, this invention analyzes pavement damage through images, only determining the location and type of damage, and cannot perform further analysis. Summary of the Invention

[0004] To address the aforementioned technical deficiencies in existing technologies, the present invention aims to provide a method for detecting airport pavement structures. This method involves collecting vibration signals by excitation from a seismic source, extracting surface wave dispersion curves, and utilizing the propagation characteristics of surface waves in underground media to invert the underground shear wave velocity structure of the airport, thereby detecting the airport pavement structure.

[0005] In recent years, surface wave detection (SWDM) technology has been frequently used in geological exploration. This technology extracts dispersion curves from vibration signals generated by a seismic source to invert the underground shear wave velocity structure. The location of defects is determined by the shear wave velocity structure, and the inverted wave velocity values ​​are equivalent to shear wave velocity values, which can also be used for subsequent defect analysis. Depending on the type of seismic source, SWDM is divided into active source SWDM and passive source SWDM. Active source SWDM (also known as artificial source SWDM) generates surface wave signals by artificially exciting a seismic source (such as an air gun, hammer, explosive, or tamping), and receives the response of these signals after propagation in the underground medium, thereby inverting the underground structure. This technology has been applied in various scenarios, but its application in airports is relatively limited. Although active source SWDM is less commonly used in airports, verification has shown that its application in airports is feasible.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following solution: This invention provides a method for inspecting airport pavement structures. This testing method employs active source surface wave detection technology and includes: The parameter acquisition experiment involves collecting vibration signals through a seismic source and extracting the surface wave dispersion curve. The underground shear wave velocity structure is inverted based on the propagation characteristics of the surface wave in the underground medium. The location of the disease can be determined based on the shear wave velocity structure. The inverted wave velocity value is equivalent to the shear wave velocity value and can also be used for subsequent disease cause analysis.

[0007] The test results obtained using the detection method of this invention are consistent with the current condition of the airport pavement structure, proving the feasibility of active source surface wave detection technology in airport pavement structure detection.

[0008] Preferably, the test structure selected for the parameter acquisition experiment is a cement concrete roadbed structure; the SmartSolo IGU-16HR single-component detector is used as the receiving instrument for the parameter acquisition experiment; the seismic source for the parameter acquisition experiment is an FWD (falling weight deflectometer) and a 1kg hammer, with a 2cm thick metal pad used when the hammer strikes.

[0009] In any of the above schemes, it is preferred that the parameters selected in the acquisition parameter experiment include the seismic source, hammer load, number of hammer blows, minimum offset distance, and track spacing. The hammer load is set to 100-160kN, the number of hammer blows per shot point is considered to be in the range of 1-7 times, the minimum offset distance is 5-10m, and the track spacing is 0.2-2m.

[0010] In any of the above schemes, it is preferred that the surface wave dispersion curve is extracted using the phase-shifting method, which includes: The acquired time-domain surface wave signal is transformed into a frequency-domain surface wave signal through Fourier transform, and the influence of amplitude spectrum is eliminated by normalization to obtain the normalized spectrum. For a given frequency, by scanning within the possible phase velocity range and summing each channel, the phase velocity spectrum corresponding to that frequency can be obtained. When the scanning phase velocity equals the true phase velocity, the summation result reaches its maximum value. The phase velocity at this point corresponds to the phase velocity at that frequency. In this way, the phase velocity corresponding to each frequency can be obtained, thereby extracting the dispersion curve.

[0011] In any of the above schemes, it is preferred that the detection method be verified at an airport, with the arrangement being three linear measuring lines in the vertical connecting taxiway area, one measuring line being arranged at the center line of the connecting taxiway and two measuring lines being arranged at the edge lines of the connecting taxiway; each of the three measuring lines is equipped with 45 detectors.

[0012] In summary, the airport pavement structure detection method of the present invention has the following advantages: the active source surface wave detection technology using FWD as the seismic source can detect the location of the airport at a depth of 30m; the layered structure of the strata can be seen through the inverted shear wave velocity structure imaging; and further research and analysis can be carried out through shear wave velocity to analyze the causes of defects. Attached Figure Description

[0013] Figure 1 This is a schematic diagram illustrating the working principle of the airport pavement structure inspection method according to the present invention.

[0014] Figure 2 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows an experimental test in the preferred embodiment.

[0015] Figure 3 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows excitation signals from different seismic sources in the preferred embodiment.

[0016] Figure 4 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the spectrum of different seismic sources in the preferred embodiment.

[0017] Figure 5 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the inversion wave velocity of different seismic sources in the preferred embodiment.

[0018] Figure 6 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the excitation signals of different hammer loads in the preferred embodiment.

[0019] Figure 7 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the spectrum of different hammer impact loads in the preferred embodiment.

[0020] Figure 8 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the inversion wave velocity under different hammer loads in the preferred embodiment.

[0021] Figure 9 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the excitation signal for different hammer blow counts in the preferred embodiment.

[0022] Figure 10 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the spectrum of different hammer blow counts in the preferred embodiment.

[0023] Figure 11 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows excitation signals with different minimum offset distances in the preferred embodiment.

[0024] Figure 12 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the spectrum of different minimum offset distances in the preferred embodiment.

[0025] Figure 13 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the excitation signals for different channel spacings in the preferred embodiment.

[0026] Figure 14 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the spectrum of different channel spacings in the preferred embodiment.

[0027] Figure 15 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the inversion wave velocity at different channel spacings in the preferred embodiment.

[0028] Figure 16 For the airport pavement structure inspection method according to the present invention Figure 1 A schematic diagram of the subsidence area of ​​the vertical connecting tunnel in the preferred embodiment shown.

[0029] Figure 17 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows different survey line arrangements in the preferred embodiment.

[0030] Figure 18 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the imaging results of different survey lines in the preferred embodiment.

[0031] Figure 19 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the shear wave velocity at a distance of 40m from different measuring lines in the preferred embodiment.

[0032] Figure 20 For the airport pavement structure inspection method according to the present invention Figure 1 The diagram shows the shear wave velocity at different distances from the measuring line 3 in the preferred embodiment. Detailed Implementation

[0033] The following description is merely exemplary and not intended to limit this disclosure, its application, or its uses. The specific embodiments of the airport pavement structure inspection method of the present invention will be further described below with reference to the accompanying drawings.

[0034] like Figure 1 The diagram shown illustrates a preferred embodiment of the airport pavement structure inspection method according to the present invention. The present invention provides an airport pavement structure inspection method employing active source surface wave detection technology, comprising: The parameter acquisition experiment involves collecting vibration signals through a seismic source and extracting the surface wave dispersion curve. Based on the propagation characteristics of surface waves in the underground medium, the underground shear wave velocity structure is inverted (the detection method of this invention uses a genetic algorithm for inversion, taking the observed data as the actual extracted surface wave dispersion curve and the theoretical data as the theoretical surface wave dispersion curve obtained by forward modeling). The location of the disease can be determined based on the shear wave velocity structure. The inverted wave velocity value is equivalent to the shear wave velocity value and can also be used for subsequent disease cause analysis.

[0035] The test results obtained using the detection method of this invention are consistent with the current condition of the airport pavement structure, proving the feasibility of active source surface wave detection technology in airport pavement structure detection.

[0036] In this embodiment, the extraction of the surface wave dispersion curve employs a phase-shifting method, which includes: The acquired time-domain surface wave signal is transformed into a frequency-domain surface wave signal through Fourier transform, and the influence of amplitude spectrum is eliminated by normalization to obtain the normalized spectrum. For a given frequency, by scanning within the possible phase velocity range and summing each channel, the phase velocity spectrum corresponding to that frequency can be obtained. When the scanning phase velocity equals the true phase velocity, the summation result reaches its maximum value. The phase velocity at this point corresponds to the phase velocity at that frequency. In this way, the phase velocity corresponding to each frequency can be obtained, thereby extracting the dispersion curve.

[0037] See Figure 2 The diagram shows an experimental test schematic of the airport pavement structure detection method according to the present invention.

[0038] In this embodiment, the test structure selected for the parameter acquisition experiment is a cement concrete roadbed structure; the SmartSolo IGU-16HR single-component detector is used as the receiving instrument for the parameter acquisition experiment; the seismic source for the parameter acquisition experiment is an FWD (falling weight deflectometer) and a 1kg hammer, with a 2cm thick metal pad used when the hammer strikes.

[0039] The detector used in the parameter acquisition experiment was a SmartSolo IGU-16HR single-component detector with a natural frequency of 5Hz and a sampling interval of 1ms. The falling weight deflectometer (FWD) used was an Imaya Shinken fully automatic falling weight deflectometer.

[0040] In this embodiment, the parameters selected for the acquisition experiment include the seismic source, hammer load, number of hammer blows, minimum offset, and track spacing. The hammer load is set to 100-160 kN, the number of hammer blows per shot point is considered to be 1-7, the minimum offset is 5-10 m, and the track spacing is 0.2-2 m. (See Table 1) Table 1 Experimental Test Scheme The next step is to analyze the parameter selection for the detection method of this invention.

[0041] like Figures 3-5 As shown, the analysis of the earthquake source: The seismic source is a key factor affecting signal strength and bandwidth, and the magnitude and stability of its energy determine the depth and accuracy of the detection. Figure 3 The signals are generated by two types of seismic sources: FWD and small hammer. As can be seen from the figure, the signal collected by FWD is more stable and less affected by external noise. Figure 4 These are the spectrum diagrams obtained from two different seismic sources. The effective frequency band range of FWD excitation is 5-30Hz, while the effective frequency band range of hammer excitation is 40-100Hz. The frequency of FWD excitation is mainly in the low-frequency part, while the frequency of hammer excitation is in the high-frequency part. Figure 5This is a comparison chart of the shear wave velocities inverted from two different seismic sources and the shear wave velocities provided by geological exploration data. In the shallow region of 1-3m, the wave velocities inverted by the small hammer are close to those provided by the geological exploration data. In the deep region of 3-10m, the wave velocities inverted by the FWD are close to those provided by the geological exploration data. The small hammer inversion only reaches 7m in the deep region, and the inversion results differ greatly from those of the geological exploration data.

[0042] Therefore, small hammers have low impact energy, high excitation frequency, and shallow detection depth, while FWD hammers have high and stable impact energy, low excitation frequency, and deep detection depth.

[0043] like Figure 6-8 As shown, the analysis of the hammer impact load is as follows: Hammer load is a factor related to the hammering energy of the FWD equipment, which affects signal reception and detection depth. Figure 6 These are signals excited by hammering under three different loads. Within the same reception time, the signal excited by a 160kN load is more stable and receives more signals. Figure 7 The spectrum diagrams are obtained from three loads. The effective frequency bands of the loads 100kN and 130kN are in the range of 5-25Hz, and the excitation frequency bands are unstable, which is not conducive to the extraction of the dispersion curve. The effective frequency band of the load 160kN is in the range of 5-30Hz, and the excitation frequency bands are more stable. Figure 8 This is a comparison chart of the shear wave velocities inverted by three loads and those provided by geological exploration data. As can be seen from the chart, the shear wave velocities inverted by hammer loads of 100 and 130 kN in the shallow region of 1-4 m are closer to the shear wave velocities provided by geological exploration data; the shear wave velocity inverted by hammer load of 160 kN in the deep region of 4-10 m is closer to the shear wave velocity provided by geological exploration data, and the inversion effect of hammer loads of 100 and 130 kN is poor in the deep region.

[0044] Therefore, a smaller hammer load setting results in a shallower detection depth, while a larger setting results in a deeper detection depth.

[0045] like Figure 9 , Figure 10 As shown, the analysis of the number of hammer blows: The number of hammer strikes refers to the cumulative number of hammer strikes at a single shot point, and is a key factor related to signal strength and spectral resolution. Figure 9 The signal is generated by hammering with different numbers of times. Compared to hammering once, the signal is strengthened when hammering three times. However, as the number of hammering times increases from three to seven, the signal does not continue to strengthen. Therefore, hammering three times is the optimal way to increase signal strength and reduce random interference. Figure 10These are spectrum diagrams obtained from different numbers of hammer blows. The effective frequency band range for one hammer blow is 5-30Hz, and the effective frequency band range for three hammer blows is 5-25Hz. The bandwidth of the one-hammer-driven excitation is poor in the 5-15Hz range, but performs well in the 15-30Hz range. The bandwidth of the three-hammer-driven excitation is high in the 5-15Hz range, but poor in the 15-25Hz range. Furthermore, as the number of hammer blows increases from 3 to 7, the spectral resolution does not improve.

[0046] Therefore, the spectral resolution obtained by hammering once in the high-frequency range is higher, and the spectral resolution obtained by hammering three times in the low-frequency range is higher. Increasing the number of hammerings beyond three times does not improve the resolution.

[0047] like Figure 11 , Figure 12 As shown, the analysis of the minimum offset distance is as follows: The setting of the minimum offset distance is closely related to whether a valid signal can be received. A reasonable minimum offset distance minimizes random interference in the received signal and makes the received signal more stable. Figure 11 The signals are excited at different minimum offset distances. As can be seen from the figure, the signal reception is relatively stable when the minimum offset distance is 5m. As the minimum offset distance increases to 6 or 7m, the signal is further strengthened. When the minimum offset distance continues to increase to 8m, the signal begins to weaken and no longer strengthens. Figure 12 These are spectrum diagrams obtained at different minimum offset distances. The effective frequency band for excitation at minimum offset distances of 5, 6, and 8 m is 5-25 Hz, while the effective frequency band for excitation at a minimum offset distance of 7 m is 5-28 Hz. At a minimum offset distance of 5 m, the bandwidth is narrow and the spectral resolution is low. When the minimum offset distance increases to 6 or 7 m, the spectral resolution improves, random interference decreases, and the frequency band stabilizes. When the minimum offset distance continues to increase to 8 m, random interference increases, and the spectral resolution decreases.

[0048] Therefore, when using FWD as the seismic source, the minimum offset distance should be set to 6-7m.

[0049] like Figures 13-15 As shown, the analysis of track spacing is as follows: The spacing between channels is a key factor in determining the length of the survey line and the number of detectors, affecting the detection depth and accuracy. Figure 13 The signals were excited by different channel spacings. When the channel spacing was 0.5m, there were 40 detectors and the signal reception was stable. When the channel spacing increased to 1m, there were 20 detectors and the signal did not increase. When the channel spacing increased to 2m, there were 10 detectors and the signal began to weaken, with random interference having a significant impact. Figure 14The graphs show the spectrum obtained with different channel spacings. When the channel spacing is 0.5m, the frequency band is stable and the spectral resolution is high. When the channel spacing increases to 1m, the spectral resolution decreases and random interference increases. When the channel spacing increases to 2m, the frequency band becomes segmented and random interference increases significantly, which is not conducive to the extraction of dispersion curves. Figure 15 This is a comparison chart of shear wave velocities retrieved at different trace spacings and those provided by geological exploration data. As can be seen from the chart, the shear wave velocities retrieved at trace spacings of 0.5m and 1m are close to those provided by geological exploration data, while the shear wave velocities retrieved at a trace spacing of 2m differ greatly from those provided by geological exploration data, and the retrieved depth does not reach 10m.

[0050] Therefore, when conducting tests, it is necessary to select a large number of detectors with a reasonable channel spacing to improve lateral resolution.

[0051] In this embodiment, the detection method was verified at an airport in North China. This airport is located in a mountainous area with extensive deep excavation and high-fill construction. The soil-to-rock ratio in the excavation area is 2:8, so the area beneath the airport's runway foundation is primarily composed of soil and rock layers with high shear wave velocities. According to the airport's exploration data, the shear wave velocity range of the underground structural layers is 150-2500 m / s. The site mainly consists of soil and rock layers such as silty clay, strongly weathered conglomerate, moderately weathered conglomerate, strongly weathered andesite, and moderately weathered andesite. In the area of ​​the airport's vertical connecting runway, subsidence occurred in the runway foundation structure, such as... Figure 16 The area shown in the red box is as follows.

[0052] This verification involved setting up three linear survey lines in the vertical connecting road area, such as... Figure 17 As shown, one survey line is arranged along the centerline of the connecting tunnel, and two survey lines are arranged along the edge lines of the connecting tunnel. Each of the three survey lines is equipped with 45 geophones. The natural frequency of the geophones is 5Hz, the channel spacing is 1m, and the time sampling interval is 1ms. The seismic source adopts the FWD hammer method, with a minimum offset distance of 6m, a hammer load of 160kN, and a single shot point is hammered 3 times.

[0053] In this embodiment, by collecting vibration signals and extracting surface wave dispersion curves, the underground shear wave velocity structure of the vertical connecting tunnel was obtained through inversion, as shown below. Figure 18 As shown in the figure, (a) is the imaging result of survey line 1, and (b) and (c) are the imaging results of survey lines 2 and 3, respectively. The figure shows that the shear wave velocity distribution is mainly divided into three layers: within a depth of 10m, the shear wave velocity is <950m / s, indicating a soil layer; from a depth of 10-25m, the shear wave velocity ranges from 950-1950m / s, indicating a strongly weathered rock layer; and from a depth of 25-40m, the shear wave velocity ranges from 1950-2450m / s, indicating a moderately weathered rock layer. This is consistent with the shear wave velocity range given in the airport exploration data. The shear wave velocity data at a distance of 40m from each of the three survey lines are plotted as follows. Figure 19As shown, the shear wave velocities of the three survey lines within a depth range of 2-20m are basically consistent; after a depth of 20m, the shear wave velocities of survey lines 1 and 3 are much lower than those of survey line 2, resulting in uneven strength of the lower structure of the vertical connecting tunnel, which is an indirect cause of the subsidence of the vertical connecting tunnel foundation structure. From Figure 18 As can be seen in the red box area, the low-velocity region at a depth of 15m in Figure (c) shows a downward intrusion trend. Shear wave velocity data at distances of 10m and 40m in Figure (c) are compared for example... Figure 20 As shown, the shear wave velocity values ​​remain consistent at different distances from 2 to 15 m depth; at depths of 15 to 30 m, the shear wave velocity values ​​begin to decrease at a distance of 3 to 40 m from the survey line, corresponding to the downward intrusion of the low wave velocity zone, which leads to damage to the structural strength of the vertical connecting roadway subgrade and subsidence, consistent with the current pavement condition reported by the airport. This phenomenon may be caused by uneven filling during the excavation and filling process.

[0054] In summary, small hammers have low impact energy, high excitation frequency, and shallow detection depth, while FWD (Active Surface Wave) hammers have high and stable impact energy, low excitation frequency, and deep detection depth. Smaller hammer load settings result in shallower detection depths, while larger settings result in deeper detection depths. A single hammer strike in the high-frequency range yields higher spectral resolution, while three hammer strikes in the low-frequency range yield higher spectral resolution; increasing the number of strikes beyond three does not improve resolution. When using FWD as the seismic source, a minimum offset distance of 6-7 meters is optimal. During testing, selecting a large number of detectors with appropriate track spacing is crucial to improve lateral resolution. The detection method of this invention, employing active surface wave detection technology, is feasible for application in airport pavement structures, and its application can improve the accuracy of airport pavement structure detection.

[0055] Those skilled in the art will readily understand that the airport pavement structure inspection method of the present invention includes any combination of the parts described in this specification. Due to space limitations and for the sake of brevity, these combinations are not described in detail here; however, after reading this specification, the scope of the present invention, constituted by any combination of the parts described herein, is self-evident.

Claims

1. A method for inspecting airport pavement structures, characterized in that, This testing method employs active source surface wave detection technology, which includes: The parameter acquisition experiment involves collecting vibration signals through a seismic source and extracting the surface wave dispersion curve. The underground shear wave velocity structure is inverted based on the propagation characteristics of the surface wave in the underground medium. The location of the disease can be determined based on the shear wave velocity structure. The inverted wave velocity value is equivalent to the shear wave velocity value and can also be used for subsequent disease cause analysis.

2. The airport pavement structure inspection method as described in claim 1, characterized in that, The test structure used in the parameter acquisition experiment was a cement concrete roadbed structure; the SmartSolo IGU-16HR single-component detector was used as the receiving instrument; the source of the parameter acquisition experiment was an FWD and a 1kg hammer, with a 2cm thick metal pad used when the hammer was struck.

3. The airport pavement structure inspection method as described in claim 1 or 2, characterized in that, The parameters selected for the acquisition experiment include the seismic source, hammer load, number of hammer blows, minimum offset distance, and track spacing. The hammer load is set to 100-160kN, the number of hammer blows per shot point is considered to be 1-7 times, the minimum offset distance is 5-10m, and the track spacing is 0.2-2m.

4. The airport pavement structure inspection method as described in claim 1, characterized in that, The surface wave dispersion curve is extracted using the phase-shifting method, which includes: The acquired time-domain surface wave signal is transformed into a frequency-domain surface wave signal through Fourier transform, and the influence of amplitude spectrum is eliminated by normalization to obtain the normalized spectrum. For a given frequency, by scanning within the possible phase velocity range and summing each channel, the phase velocity spectrum corresponding to that frequency can be obtained. When the scanning phase velocity equals the true phase velocity, the summation result reaches its maximum value, and the phase velocity at this time corresponds to the phase velocity at that frequency. By obtaining the phase velocity corresponding to each frequency, the dispersion curve can be extracted.

5. The airport pavement structure inspection method as described in claim 1 or 2, characterized in that: The detection method was validated at an airport, with three linear survey lines arranged in the vertical taxiway area, one survey line arranged at the center line of the taxiway and two survey lines arranged at the edge lines of the taxiway; each of the three survey lines was equipped with 45 detectors.

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