An airport pavement roughness detection method and system

Abstract

The application relates to the technical field of airport pavement engineering design, in particular to an airport pavement unevenness detection method and system, which quickly obtains pavement three-dimensional elevation point cloud data accurately reflecting the pavement characteristics of an airport runway through laser scanning technology, greatly improves the speed and density of data collection, simultaneously obtains pavement unevenness information according to the pavement three-dimensional elevation point cloud data and a pavement reference plane, divides the airport runway into strips according to a preset width, samples the pavement unevenness information in the strips, calculates unevenness representative values of each sampling area, obtains the unevenness of the strips according to the unevenness representative values of each sampling area and the pavement unevenness information, more accurately reflects the unevenness of the key area of interaction between an airplane and a runway, and improves the reliability of the unevenness calculation result; in addition, the construction of an airport runway pavement unevenness information database can realize the timely acquisition and update of the unevenness information.

Description

Technical Field

[0001] The present application relates to the technical field of airport pavement engineering design, and in particular to a method and system for detecting airport pavement unevenness. Background Art

[0002] As a vital infrastructure within an airfield, the structural and functional performance of a runway is directly related to the safety of runway operations. Key indicators such as roughness and longitudinal and transverse slopes are widely used when evaluating runway functional performance. Roughness specifically refers to the degree of deviation between the actual elevation of an airport pavement and its ideal plane. When the pavement has poor roughness, it triggers a violent dynamic response in the aircraft-runway coupling system, which in turn generates excessive tensile stress in the pavement's shallow structure, accelerating the formation and development of cracks, exacerbating damage to the pavement structure, and seriously affecting driving comfort. Therefore, when studying the interaction mechanism between aircraft and runways, the impact of roughness must be fully considered.

[0003] Patent application CN108170912A discloses a method for evaluating airport runway roughness by calculating the roughness evaluation index (ARRI). However, in practice, the uncertainty of an aircraft's landing trajectory due to wind speed and pilot skill during landing necessitates the acquisition of extensive runway elevation data to detect pavement roughness. Traditional detection methods rely primarily on non-contact sensors (such as laser or ultrasonic sensors) mounted on vehicles for data collection. This method is not only inefficient but also relatively limited in the amount of data available, making it difficult to meet the requirements for large-scale pavement roughness detection. Furthermore, the measurement accuracy of non-contact sensors is highly susceptible to sensor sensitivity and vehicle speed. This dependency not only results in low reliability of the roughness detection results but also potentially compromises the accurate quantification of the aircraft's vibration response during landing, deceleration, and taxiing. Summary of the Invention

[0004] Based on this, it is necessary to provide an airport runway surface roughness detection method and system to address the current problems of low efficiency and low reliability of airport runway roughness detection.

[0005] In a first aspect, the present application provides a method for detecting airport pavement roughness. The method comprises:

[0006] Step S1, obtaining the three-dimensional elevation point cloud data and the pavement reference plane of the airport runway;

[0007] Step S2, obtaining road surface roughness information based on the road surface three-dimensional elevation point cloud data and the road surface reference plane;

[0008] Step S3, dividing the airport runway into strips according to a preset width;

[0009] Step S4, sampling the road surface roughness information within the strip and calculating the roughness representative value of each sampling area;

[0010] Step S5, obtaining the roughness of the strip according to the roughness representative value of each sampling area;

[0011] Step S6: constructing an airport runway pavement roughness information database based on the roughness of each strip.

[0012] Furthermore, the calculation formula of the roughness representative value is:

[0013]

[0014] Where, Indicates the The representative value of roughness of the sampling area, Indicates the The number of roughness values ​​contained in the sampling area, Indicates the Within the sampling area The roughness value, Indicates the The average roughness of the sampling area;

[0015] The calculation formula of the strip unevenness is:

[0016]

[0017] Where, Indicates the unevenness of the strip, Indicates the number of sampling areas contained in the strip.

[0018] Furthermore, the step S1 includes:

[0019] Step S11, dividing the airport runway into sections and obtaining the original three-dimensional elevation point cloud data of each section of the runway;

[0020] Step S12: construct a three-dimensional coordinate transformation model; the expression of the three-dimensional coordinate transformation model is:

[0021]

[0022] Where l is the scale factor of coordinate transformation, is the rotation matrix for coordinate transformation, is the translation vector, is the observation vector in the target coordinate system, is the observation vector in the original coordinate system;

[0023] Step S13, after described three-dimensional coordinate conversion model is carried out linearization process, adopts indirect adjustment method to solve and obtains coordinate conversion parameter; Described coordinate conversion parameter comprises translation parameter, rotation parameter and scale parameter;

[0024] Step S14 , splicing the original three-dimensional elevation point cloud data of each section of the runway surface based on the coordinate conversion parameters to obtain the three-dimensional elevation point cloud data of the runway surface of the entire runway.

[0025] Furthermore, the step S1 includes:

[0026] Step S15: performing registration, denoising and downsampling on the road surface three-dimensional elevation point cloud data.

[0027] Furthermore, the specific steps of downsampling include:

[0028] Step S151, dividing the road surface three-dimensional elevation point cloud data into neighborhoods and calculating neighborhood points in each neighborhood;

[0029] Step S152, calculating the normal angle between each point in the neighborhood and the neighboring point;

[0030] Step S153, calculating the curvature of the neighborhood according to the normal angle value;

[0031] Step S154, dividing the neighborhood into regions with obvious features and regions with unclear features based on the curvature of each neighborhood;

[0032] Step S155, uniformly sampling the region with obvious features according to a first preset sampling number;

[0033] Step S156: uniformly sample the region with unclear features according to a second preset sampling number.

[0034] Furthermore, the first preset sampling number is U*(1-V), and the second preset sampling number is U*V, where U is the target sampling number and V is the uniform sampling parameter.

[0035] Furthermore, the method further comprises:

[0036] Step S7, obtaining the pavement roughness information on the aircraft wheel track line from the airport runway pavement roughness information database based on coordinate matching;

[0037] Step S8, solving the precise dynamic load when the aircraft and the pavement interact based on the pavement roughness information and the aircraft-pavement structure coupled dynamic balance equation; the aircraft-pavement structure coupled dynamic balance equation is expressed as:

[0038]

[0039] Where, is the sprung mass, is the unsprung mass, is the main landing gear suspension stiffness coefficient, is the main landing gear wheel tire stiffness coefficient, is the main landing gear suspension damping coefficient, is the main landing gear wheel tire damping coefficient, is the vertical displacement of the sprung mass, is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass, is the vertical displacement of the unsprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, is the lift force exerted on the aircraft when it is taxiing on the runway. is the acceleration due to gravity; For road surface The unevenness of the is the first derivative of the roughness, is the number of generalized coordinates, is the generalized mass of the nth-order vibration mode of the pavement structure, is the nth-order generalized coordinate corresponding to the pavement structure, is the first derivative of the nth-order generalized coordinate corresponding to the pavement structure, is the second derivative of the nth-order generalized coordinate corresponding to the road surface structure, is the damping ratio of the nth-order vibration mode of the pavement structure, is the natural frequency of the nth-order vibration mode of the pavement structure, For road surface The vibration mode at .

[0040] Furthermore, the step of constructing the aircraft-pavement structure coupled dynamic balance equation includes:

[0041] Step S810: Establish a two-degree-of-freedom aircraft vertical vibration model differential equation; the expression of the two-degree-of-freedom aircraft vertical vibration model differential equation is:

[0042]

[0043] Where, is the road surface roughness excitation at the landing gear tire contact point, is the first-order derivative of the road surface roughness excitation; the expression of the road surface roughness excitation is:

[0044]

[0045] Where, For road surface The incentives of For road surface The unevenness of the For road surface The vertical displacement at ;

[0046] Step S811: constructing a discretized pavement structure vibration differential equation by using the mode superposition method; the expression of the discretized pavement structure vibration differential equation is:

[0047]

[0048] Where, is the mass matrix of the layered pavement structure model, is the damping matrix of the layered pavement structure model, is the stiffness matrix of the layered pavement structure model, is the acceleration vector of the layered pavement structure model, is the velocity vector of the layered pavement structure model, is the displacement vector of the layered pavement structure model, is the excitation vector acting on the pavement structure;

[0049] Step S812: construct a finite element model, perform modal analysis on the discretized pavement structure vibration differential equation, and obtain the modal equation of the n-th order vibration mode of the pavement structure; the modal equation of the n-th order vibration mode of the pavement structure is expressed as:

[0050]

[0051] Where, is the generalized force of the n-th vibration mode of the pavement structure; the expression of the generalized force of the n-th vibration mode of the pavement structure is:

[0052]

[0053] Step S813: Obtain the aircraft-pavement structure coupled dynamic equilibrium equation based on the two-degree-of-freedom aircraft vertical vibration model differential equation and the modal equation of the nth-order vibration mode of the pavement structure.

[0054] Furthermore, the step S8 further includes:

[0055] Step S820, obtain the initial mass matrix [M0], initial stiffness matrix [K0], initial damping matrix [C0], initial displacement vector of the coupling system and the initial velocity vector ;

[0056] Step S821, according to the initial mass matrix [M0], initial stiffness matrix [K0], initial damping matrix [C0], initial displacement vector , initial velocity vector The load array is determined by the aircraft-pavement structure coupled dynamic equilibrium equation , damping array and the array of spring forces ;

[0057] Step S822, according to the formula

[0058]

[0059] Calculate the initial acceleration vector Where [M0] -1 is the inverse matrix of the initial mass matrix of the coupled system;

[0060] Step S823, calculating the integral constant according to the preset time step Δt, the preset parameter δ and the preset parameter γ; the integral constant includes a0=1 / (δΔt 2 ), a1=γ / (δΔt), a2=1 / (δΔt), a3=1 / 2δ-1, a4=γ / δ-1, a5=Δt / 2*(γ / δ-2), a6=Δt*(1-γ), a7=γΔt;

[0061] Step S824: Establish the effective stiffness matrix of the coupling system :

[0062]

[0063] Step S825: Calculate the equivalent load increment within a preset time step. , displacement increment , speed increment and acceleration increment ;in,

[0064] The equivalent load increment The calculation formula is:

[0065]

[0066] Where, is the original load increment;

[0067] The displacement increment The calculation formula is:

[0068]

[0069] Where, is the inverse matrix of the effective stiffness matrix;

[0070] The speed increment The calculation formula is:

[0071]

[0072] The acceleration increment The calculation formula is:

[0073]

[0074] Step S826, calculate the displacement vector of the aircraft-runway interaction dynamic system at the end of a preset time step , velocity vector and the acceleration vector ;in,

[0075] The displacement vector The calculation formula is:

[0076]

[0077] The velocity vector The calculation formula is:

[0078]

[0079] The acceleration vector The calculation formula is:

[0080]

[0081] In step S827, the displacement vector, velocity vector, and acceleration vector obtained in step S826 are used as initial values, and steps S820 to S826 are repeatedly executed until a preset loop end condition is met.

[0082] In a second aspect, the present application also provides an airport pavement roughness detection system. The system comprises:

[0083] A pavement roughness information acquisition module is used to acquire the three-dimensional elevation point cloud data and the pavement reference plane of the airport runway; and obtain the pavement roughness information based on the three-dimensional elevation point cloud data and the pavement reference plane;

[0084] The runway strip roughness calculation module is used to divide the airport runway into strips according to a preset width; sample the pavement roughness information within the strips and calculate the roughness representative value of each sampling area; and obtain the roughness of the strip based on the roughness representative value of each sampling area and the pavement roughness information;

[0085] The airport runway pavement roughness information library construction and update module is used to construct an airport runway pavement roughness information library based on the roughness of each strip, and iteratively update the airport runway pavement roughness information library based on the pavement three-dimensional elevation point cloud data at preset time intervals.

[0086] The above-mentioned airport pavement roughness detection method and system uses laser scanning technology to acquire three-dimensional elevation point cloud data of the airport runway pavement, significantly improving the speed and density of data acquisition. Furthermore, the system obtains pavement roughness information based on the three-dimensional elevation point cloud data and a pavement reference plane. The runway is divided into strips of preset widths, and the pavement roughness information within the strips is sampled. The representative roughness value of each sampled area is calculated based on the representative roughness value and the pavement roughness information. A runway pavement roughness information database is constructed based on the roughness of each strip. Taking into account the actual trajectory distribution of aircraft landing, coordinate matching is used to more accurately reflect the roughness of key areas where the aircraft interacts with the runway, improving the reliability of the roughness calculation results and providing a reliable basis for their application. Furthermore, the timely acquisition and updating of pavement roughness information reflects the dynamic changes in pavement conditions, providing a reliable scientific basis for airport pavement operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 1 is a flow chart of a method for detecting airport pavement roughness in one embodiment;

[0088] Figure 2 FIG1 is a schematic diagram showing a comparison of three-dimensional elevation point cloud data of a runway of an airport before and after registration, denoising, and downsampling in one embodiment;

[0089] Figure 3 A distribution diagram of the elevation difference along the entire length of the runway obtained from two scans six years apart in one embodiment;

[0090] Figure 4 A distribution diagram of IRI values ​​along the entire length of a runway is calculated in one embodiment;

[0091] Figure 5 Schematic diagram of the structure of a two-degree-of-freedom aircraft vertical vibration model in one embodiment;

[0092] Figure 6 A schematic diagram of the locations of field stations and target points for measurement in one embodiment;

[0093] Figure 7 A schematic diagram of airport runway pavement scanning accuracy in one embodiment;

[0094] Figure 8is a distribution diagram of the unevenness of a certain strip along the entire length of the runway in one embodiment;

[0095] Figure 9 A diagram showing the precise distribution of dynamic loads along the longitudinal direction of the runway when the aircraft interacts with the pavement in one embodiment;

[0096] Figure 10 Schematic diagram of the structure of an airport pavement roughness detection system in one embodiment. DETAILED DESCRIPTION

[0097] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0098] Example 1

[0099] like Figure 1 As shown, a method for detecting unevenness of an airport pavement is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0100] Step S1: Acquire the three-dimensional elevation point cloud data and the pavement reference plane of the airport runway.

[0101] The 3D elevation point cloud data for the runway surface is a collection of 3D coordinate points on the runway surface. Each data point contains its positional information in 3D space. This embodiment utilizes a laser scanning device to rapidly acquire 3D coordinate information for the runway surface. 3D laser scanning technology uses lasers as a measurement medium and avoids contact with the test object, improving data acquisition accuracy and preventing damage to the object being measured. Furthermore, during the scanning process, special attention should be paid to: Based on the runway dimensions and scanning accuracy requirements, the minimum number of stations should be set up to achieve maximum coverage to reduce the number of stitching operations required for the 3D elevation point cloud data. For key scanning areas, a pre-scan should be performed first, followed by a detailed scan with adjusted resolution and quality parameters to obtain higher-quality data. Ensure that the target prism sphere is within 15 meters of the 3D scanner, with the prism facing the total station and the sphere facing the 3D scanner, to ensure good signal reception and data acquisition. After completing each scan, the scan results should be immediately checked to confirm that the reference sphere, paper target, or prism sphere is within the measurement range. Only after confirmation can the instrument be moved to the next scan station. The movement of scanning instruments and target stations should follow the longitudinal advancement method along the runway and be carried out in an orderly and efficient manner. In addition, given the heavy instruments and equipment, the long runway and the short nighttime scanning window, mobile or fixed buried lifting equipment can be used for three-dimensional scanning. These devices can achieve road surface elevation scanning more quickly while avoiding the noise generated by vehicles and personnel during movement and relocation. With the help of high-precision Beidou satellite positioning technology, more efficient and accurate scanning and data transmission can be achieved. In addition, in order not to affect the takeoff and landing of aircraft, data optical cable transmission should be adopted. The scanned data will be transmitted to a certain range outside the road surface via optical cable, and then long-distance signal transmission will be carried out through the base station to ensure the security and stability of data transmission and reduce interference with aircraft signals.

[0102] Specifically, the data of each section of the airport runway collected by the laser scanning device is first used as the original 3D elevation point cloud data of the runway surface, and then the original 3D elevation point cloud data of the runway surface is spliced ​​to obtain the 3D elevation point cloud data of the entire runway surface. The specific steps of data splicing include:

[0103] Step S11: Divide the airport runway into sections and obtain the original three-dimensional elevation point cloud data of each section of the runway.

[0104] Specifically, to ensure scanning accuracy, the airport runway is first divided into sections, and then the original three-dimensional elevation point cloud data of each section of the runway is obtained.

[0105] Step S12: construct a three-dimensional coordinate transformation model as shown in formula (1):

[0106] (1)

[0107] Wherein, l is the scale factor of coordinate transformation, which can be flexibly set by those skilled in the art. is the rotation matrix for coordinate transformation, is the translation vector, is the observation vector in the target coordinate system, is the observation vector in the original coordinate system. The rotation matrix in formula (1) is an orthogonal matrix with only three independent rotation variables, so there are 6 restrictions, which are shown in formula (2):

[0108] (2)

[0109] Step S13: After linearizing the three-dimensional coordinate transformation model, the coordinate transformation parameters are obtained by using an indirect adjustment method.

[0110] Since the direct solution of equation (1) may be very complicated, the three-dimensional coordinate transformation model is first linearized, and then the indirect adjustment method is used to solve the coordinate transformation parameters. The coordinate transformation parameters include the translation parameters ( 、 、 ), rotation parameters ( 、 、 ) and scale parameters The indirect adjustment method is a method that constructs an error equation and uses the least squares principle to solve the parameters. For example, the error equation is expressed as shown in formula (3):

[0111] (3)

[0112] in, , , , , , , , , , , i=1,2,....n, n is the number of generalized coordinates, is the error vector, is the coordinate transformation parameter vector to be solved, It is the ratio of the distances between two common points in different coordinate systems. In particular, when there are many common points, the distance ratio corresponding to each common point can be calculated and the average value can be taken as the ratio of the distances between the common points in different coordinate systems.

[0113] Then, based on the error equation shown in Equation (3), the least squares principle can be used to find the optimal solution for the coordinate transformation parameters that meets the preset error conditions. It is understood that the preset error conditions can be flexibly set by those skilled in the art, provided that they meet the accuracy requirements of airport pavement engineering, and are not limited here.

[0114] In step S14, the original three-dimensional elevation point cloud data of each section of the runway are spliced ​​based on the coordinate conversion parameters to obtain the three-dimensional elevation point cloud data of the entire runway.

[0115] Finally, using the coordinate transformation parameters obtained in step S13, the original 3D elevation point cloud data for each section of the road surface is spliced ​​together to create a complete, continuous 3D elevation point cloud dataset. This process ensures seamless integration of data from different sections, reconstructing a 3D model that accurately reflects the overall shape of the airport runway.

[0116] Furthermore, in some embodiments, since there is a large amount of redundant data that does not belong to the road surface in the road surface three-dimensional elevation point cloud data, such as elevation information of road surface scanning personnel and equipment, step S1 may further include:

[0117] Step S15: register, denoise, and downsample the road surface three-dimensional elevation point cloud data.

[0118] Optionally, the road surface three-dimensional elevation point cloud data is aligned and denoised by constructing a local reference plane, and the more critical road surface three-dimensional elevation point cloud data is streamlined and sampled through sampling techniques such as normal vectors, curvature and compression rate. This embodiment comprehensively considers the sampling accuracy, time consumption and the degree of change to the original point cloud data, and adopts a geometric sampling method for downsampling. The geometric sampling method is based on the geometric features of the road surface three-dimensional elevation point cloud data, especially the curvature, and combines the normal vector and compression rate to achieve sampling. In the road surface three-dimensional elevation point cloud data, any point corresponds to a curved surface, and the magnitude of the curvature directly reflects the degree of curvature of the surface arc, and further indicates the density of the elevation feature points. Therefore, in areas where the curvature of the road surface three-dimensional elevation point cloud data is large, this method will increase the number of sampling points to ensure accurate expression of the data. The specific steps of downsampling include:

[0119] Step S151 : dividing the road surface three-dimensional elevation point cloud data into neighborhoods and calculating neighborhood points in each neighborhood.

[0120] Specifically, neighborhood points can be specified or determined by automatic selection methods. For example, evenly distributed neighborhood points can be automatically selected based on the density distribution of the road surface three-dimensional elevation point cloud data, or the neighborhood range of each point can be automatically determined based on a certain algorithm (such as the K-nearest neighbor algorithm).

[0121] Step S152, calculating the normal angle between each point in the neighborhood and the neighborhood point.

[0122] Among them, the normal is a vector that is perpendicular to the surface where the point is located, and the normal angle value reflects the geometric distribution of points in the neighborhood and the curvature of the surface.

[0123] Step S153: Calculate the curvature of the neighborhood according to the normal angle value.

[0124] Since curvature and angle are positively correlated, i.e., the greater the curvature, the greater the angle, the curvature can be approximated by calculating the angle. This method is much simpler than directly calculating the curvature, thus improving efficiency.

[0125] Step S154: dividing the neighborhood into regions with obvious features and regions with unclear features based on the curvature of each neighborhood.

[0126] Next, an angle or curvature threshold is set for each neighborhood. This threshold is used to distinguish between areas with distinct features and those with less distinct features within the neighborhood. When the curvature (or angle, due to the positive correlation between them) of a neighborhood point is greater than the threshold, the neighborhood is considered to be within a distinct feature area; otherwise, it is considered to be a less distinct feature area.

[0127] Step S155 : uniformly sampling the region with obvious features according to a first preset sampling number.

[0128] Step S156: uniformly sample the region with unclear features according to a second preset sampling number.

[0129] Among them, the first preset sampling number can be U*(1-V), and the second preset sampling number can be U*V, where U is the target sampling number, that is, the number of points in the point cloud data that you hope to obtain in the end; V is the uniform sampling, which is a parameter between 0 and 1, used to control the number of features retained during the sampling process. The values ​​of U and V can be set freely. For areas with obvious features, U*(1-V) is used as the sampling number for uniform sampling, which means that while retaining most of the features, the number of points is appropriately reduced. For areas with unclear features, U*V is used as the sampling number for uniform sampling. Since the value of V is usually small, the number of samples here will also be relatively small, thereby further reducing the amount of data.

[0130] Figure 2 The following figure shows the 3D elevation point cloud data of the road surface before and after registration, denoising and downsampling. Figure 2 (a) is the spliced ​​three-dimensional elevation point cloud data map of the road surface. Figure 2 (b) is the 3D elevation point cloud data of the road surface after registration, denoising and downsampling. Figure 2It can be seen that the three-dimensional elevation point cloud data of the road surface with a sampling rate of 1% can still accurately reflect the detailed information of the road surface, and can even more clearly see some local and contour information of the road surface, while also achieving the purpose of reducing the amount of calculation.

[0131] The pavement datum plane serves as a reference for assessing pavement roughness. Pavement design documents and construction logs typically record the pavement datum plane's elevation information. By employing a coordinate system consistent with the pavement's 3D elevation point cloud data, this information can be efficiently extracted through programming.

[0132] Step S2: Obtaining road surface roughness information based on the road surface three-dimensional elevation point cloud data and the road surface reference plane.

[0133] Among them, the pavement roughness information reflects the degree of deviation between the actual elevation of the airport pavement and the ideal plane. Therefore, by subtracting the pavement reference plane recorded in the pavement design file and the pavement construction log from the pavement three-dimensional elevation point cloud data, the pavement roughness information can be obtained. Specifically, the coordinate information of the pavement three-dimensional elevation point cloud data is used to index in the reference plane data to find the theoretical elevation value on the reference plane corresponding to each point cloud data. For each pavement three-dimensional elevation point, the difference between its actual elevation and the theoretical elevation of the corresponding point on the reference plane is calculated. This difference is the roughness of the point. The roughness information of all points is then summarized to form the roughness information of the pavement. This information usually exists in the form of a data set, which contains the roughness value of each point and its position information.

[0134] Step S3: Divide the airport runway into strips according to preset widths.

[0135] Among them, the preset width can be adjusted according to actual needs to more effectively manage and analyze a large amount of road surface roughness information.

[0136] Step S4: sampling the road surface roughness information within the strip, and calculating the roughness representative value of each sampling area.

[0137] Within each strip, smaller sampling areas are further divided and the roughness information in these areas is sampled. Then, the roughness representative value (such as average, maximum value, standard deviation, etc.) of each sampling area is calculated to simplify the data and capture key features.

[0138] Step S5: Obtain the roughness of the strip according to the roughness representative value of each sampling area.

[0139] The roughness of each strip is distributed longitudinally along the length of the runway. Specifically, the roughness of the entire strip is calculated by combining the representative roughness values ​​of all sampling areas using a statistical method (such as weighted average) to comprehensively evaluate the roughness of the entire strip. In a preferred embodiment, the roughness of the strip can be calculated using the formulas shown in equations (4) and (5):

[0140] (4)

[0141] (5)

[0142] Where, Indicates the unevenness of the strip, specifically the unevenness of a certain length range of the strip. Indicates the The representative value of roughness of the sampling area, Indicates the The number of roughness values ​​contained in the sampling area, Indicates the Within the sampling area The roughness value, Indicates the The average roughness of the sampling area is Indicates the number of sampling areas contained in the strip, specifically the number of sampling areas contained within a certain length range of the strip.

[0143] Step S6: constructing an airport runway pavement roughness information database based on the roughness of each strip.

[0144] Finally, the roughness information from all strips is integrated into a database, which can be used for further runway pavement analysis, reporting, and decision support. For example, during aircraft landing, deceleration, and taxiing, the pavement roughness data for the landing gear wheel track strips can be retrieved from the airport runway pavement roughness database through coordinate matching. This enables precise coupled response analysis of the aircraft and pavement vibration models, providing accurate load form and magnitude information for exploring the dynamic response of the pavement structure under aircraft loads. Furthermore, by accurately determining the relative positional relationship between the aircraft and the pavement, the pavement roughness data within different strips can be flexibly switched and substituted, fully accounting for the impact of aircraft lateral slip on the pavement's lateral coverage during actual taxiing. Furthermore, by further combining the mechanism of aircraft dynamic loads on pavement damage with the airport's hydrogeological monitoring data, it is possible to accurately predict the pavement's service life, providing solid and reliable scientific support for pavement operation and maintenance.

[0145] Furthermore, the roughness data stored in the runway pavement roughness database can be updated in real time based on the scanning plan established by airport operations. Periodic 3D laser scanning of the runway generates 3D elevation point cloud data during operation. Comparing and analyzing this data with previous data allows precise tracking of changes in pavement elevation over time. This dynamic update mechanism ensures that the roughness data stored in the database always reflects the current pavement condition. Furthermore, the efficient use of mobile or fixed buried and elevated scanning instruments and scanning stations allows for rapid capture of even the smallest changes in pavement elevation, enabling timely updates of the 3D elevation point cloud data. This not only improves data processing timeliness but also provides a more accurate basis for subsequent maintenance decisions. After maintenance work is completed, the 3D laser scanning process is re-initiated to verify the results and update the runway pavement roughness database. This closed-loop management process ensures scientific and effective pavement maintenance, contributing to the goal of long pavement life. At the same time, airport operation and maintenance personnel can flexibly adjust the time interval between two pavement elevation scans based on the airport's operational needs and sensitivity analysis results to balance the relationship between data update frequency and operation and maintenance costs.

[0146] Example 2

[0147] In traditional studies of aircraft vertical vibration models to analyze the effects of aircraft on airport runway pavement, the pavement is typically idealized as a plane. However, when analyzing the coupled dynamic response of the aircraft and runway, the unevenness of the pavement can cause additional dynamic loads on the aircraft, affecting the aircraft's taxiing stability and exacerbating the degradation of the pavement's performance. To quantitatively describe the effect of pavement roughness on aircraft dynamic loads, current analysis methods define an unevenness influence coefficient, as shown in Equation (6):

[0148] (6)

[0149] Where, is the aircraft dynamic load without considering the pavement roughness, in kN. is the aircraft dynamic load when considering the pavement roughness, the unit is kN, It is a dimensionless unit.

[0150] The IRI (International Roughness Index) is then calculated using the conversion formula shown in Equation (7), which roughly considers the impact of the pavement surface condition on the aircraft vibration response.

[0151] (7)

[0152] Where, is the aircraft taxiing speed, in m / s; is the aircraft touchdown speed, in m / s.

[0153] Specifically, in one embodiment, a 3D laser scan was performed on the runway of an airport twice in 2013 and 2019, respectively, to obtain 3D elevation point cloud data of the runway surface before and after 6 years of operation. The southern end of the runway is the main take-off and landing end, so the 3600 m point at the north end of the runway centerline was used as the elevation constant point to convert the 3D elevation point cloud data of the runway surface. The 3D elevation point cloud data of the runway surface at the corresponding position in 2013 was subtracted from the 3D elevation point cloud data of the runway surface at the centerline in 2019, resulting in the following: Figure 3 The elevation difference between the two scans is shown along the entire length of the runway. Figure 4 The figure shows the distribution of the runway surface roughness along the entire length of the runway. It is worth noting that the runway surface roughness here is the calculated IRI value. Figure 3 It can be seen that the main take-off and landing end at the south end of the runway has basically settled under the influence of the operating aircraft load and the external environment. The maximum settlement is 61 mm, but the settlement is relatively uniform, with an average of 41 mm. Figure 4 It can be seen that the IRI of the main take-off and landing end of the runway is relatively small, only 0.734 m / km, which is less than the IRI of the entire length. This phenomenon shows that the construction quality and geological conditions of the southern end of the runway are relatively good, allowing the roadbed to settle evenly under the influence of aircraft loads and the environment. In addition, settlement also occurred in the middle of the runway, but the settlement amplitude (57 mm) was smaller than that of the main take-off and landing end (61 mm), indicating that the impact load during aircraft landing will cause the runway to settle significantly. In addition, the calculated IRI value of the middle of the runway is 0.791 m / km, which is not much different from the calculated IRI value of the southern end of the runway, but from Figure 3 However, it can be seen that the settlement in the middle of the runway is not uniform. There is both bulge and settlement at the secondary take-off and landing end at the north end of the runway, but settlement is the main factor. Among them, the maximum settlement is 54 mm, the maximum bulge is 27 mm, and the calculated IRI value is 0.829m / km, which is not much different from the IRI calculated for the entire length of the runway and the IRI values ​​calculated for the southern and middle ends, but the local pavement settlement is more complicated. At the same time, the airport is located in the southeast coast with relatively abundant precipitation, which also shows that the effect of aircraft loads on the runway pavement settlement is dominant, and the effect of the external environment is second. In addition, the settlement of the main take-off and landing end is relatively uniform, and there is both bulge and settlement at the secondary take-off and landing end, which shows that the compaction of the roadbed soil caused by repeated aircraft take-off and landing loads has played a positive role in the roadbed's resistance to natural fatigue. Usually, in order to reduce the cost of runway projects, the middle area of ​​the pavement will adopt a thinning design. The roadbed is greatly affected by aircraft loads, and settlement will also increase, but from Figure 3 It can also be seen that after years of operation, the middle of the runway is not the area with the largest settlement, which also indirectly confirms the rationality of adopting the thinning design in the middle of the pavement.

[0154] From the above analysis, it can be seen that IRI can only reflect the unevenness of the pavement within a large range, and cannot reflect the local unevenness of the pavement. Therefore, the reliability of the aircraft dynamic load calculated according to equations (6)-(7) after considering the pavement unevenness is not high. In order to further optimize the calculation of the aircraft dynamic load, this embodiment further optimizes the solution of the aircraft dynamic load based on the airport runway pavement unevenness information database calculated in Example 1. Specifically, the following steps are included:

[0155] In step S7, based on coordinate matching, the pavement roughness information on the aircraft's wheel track is obtained from the airport runway roughness database. Specifically, by comparing and matching the coordinates on the aircraft's wheel track with the coordinates in the pavement roughness database, the pavement roughness data for the area covered by the wheel track is directly indexed and extracted to obtain the corresponding pavement roughness.

[0156] Step S9: solving the precise dynamic load when the aircraft and the pavement interact based on the pavement roughness information and the aircraft-pavement structure coupled dynamic equilibrium equation.

[0157] An aircraft taxiing on a pavement is stimulated by the pavement's surface irregularities and vibrates, generating dynamic loads on the pavement structure, which in turn triggers a vibration response. The pavement's vibration, in turn, affects the aircraft's vibration, forming an interactive coupled dynamic system with the pavement structure. Therefore, this embodiment constructs the aircraft-pavement structure coupled dynamic balance equation shown in Equation (8):

[0158] (8)

[0159] Where, is the sprung mass, is the unsprung mass, is the main landing gear suspension stiffness coefficient, is the main landing gear wheel tire stiffness coefficient, is the main landing gear suspension damping coefficient, is the main landing gear wheel tire damping coefficient, is the vertical displacement of the sprung mass, is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass, is the vertical displacement of the unsprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, is the lift force exerted on the aircraft when it is taxiing on the runway. is the acceleration due to gravity; For road surface The roughness at the location is the roughness data obtained from the airport runway surface roughness information database. is the first-order derivative of the roughness, is the number of generalized coordinates, is the generalized mass of the nth-order vibration mode of the pavement structure, is the nth-order generalized coordinate corresponding to the pavement structure, is the first derivative of the nth-order generalized coordinate corresponding to the pavement structure, is the second derivative of the nth-order generalized coordinate corresponding to the road surface structure, is the damping ratio of the nth-order vibration mode of the pavement structure, is the natural frequency of the nth-order vibration mode of the pavement structure, For road surface The vibration mode at .

[0160] The specific steps for constructing the aircraft-pavement structure coupled dynamic balance equation include:

[0161] Step S810: Establish a two-degree-of-freedom aircraft vertical vibration model differential equation.

[0162] The two-degree-of-freedom aircraft vertical vibration model is a simplified aircraft dynamic analysis model. Although it is relatively simple in form, it still demonstrates high effectiveness in the application scenario of this embodiment compared to other more complex models. The two-degree-of-freedom aircraft vertical vibration model only considers the vibration of the aircraft in the vertical direction, that is, the vertical vibration of the sprung mass and the unsprung mass, while ignoring the rotation of the aircraft around the axes. Therefore, the aircraft is simplified into a two-mass system connected by a spring and a damper. Its calculation model is as follows: Figure 5 shown. Figure 5 middle The sprung mass, i.e., the mass of the fuselage, is equal to the total mass of the aircraft multiplied by the mass distribution coefficient of the main landing gear and divided by the number of landing gears. It is treated as a completely rigid body and the unit is kg. Indicates the unsprung mass, that is, the mass of a single main landing gear, in kg; The main landing gear suspension stiffness coefficient, in N / m; The main landing gear wheel tire stiffness coefficient, in N / m; Main landing gear suspension damping coefficient, in ; The main landing gear wheel tire damping coefficient, in units of ; is the vertical displacement of the sprung mass (center of mass of the fuselage), in m; is the unsprung (landing gear center of mass) vertical displacement, in m; is the excitation received by the landing gear tire at the contact point, in m; is the lift force exerted on the aircraft when taxiing on the runway. Its calculation formula is shown in formula (9) and the unit is kN. It should be noted that the main landing gear here refers to the left main landing gear, and the corresponding roughness is also the runway roughness corresponding to the left landing gear wheel track.

[0163] (9)

[0164] Where, is the air resistance coefficient, which ranges from 0.5 to 0.9 and is 0.6 in this embodiment; is the air density, which is 1.293 kg / m 3 ; is the relative speed between the aircraft and the air. For example, when the aircraft is taxiing at 80 km / h, is 22.22 m / s; is the frontal area of ​​the wing.

[0165] The sprung mass and unsprung mass After isolating it for force analysis, the differential equation of the two-degree-of-freedom aircraft vertical vibration model was established as shown in Equation (10):

[0166] (10)

[0167] Where, is the road surface roughness excitation at the landing gear tire center of mass contact point, is the first-order derivative of the road surface irregularity excitation.

[0168] In order to facilitate the solution of the equation, the differential equation shown in formula (10) is organized into a matrix form as shown in formula (11):

[0169] (11)

[0170] Where, is the mass matrix of the two-degree-of-freedom aircraft vertical vibration model, is the damping coefficient matrix of the two-degree-of-freedom aircraft vertical vibration model, is the model stiffness coefficient matrix of the two-degree-of-freedom aircraft vertical vibration model, is the displacement column vector of the two-degree-of-freedom aircraft vertical vibration model, is the excitation column vector of the two-degree-of-freedom aircraft vertical vibration model.

[0171] Therefore, when the aircraft travels on an uneven airport pavement, the additional dynamic load exerted by the landing gear on the pavement surface is shown in formula (12):

[0172] (12)

[0173] Where, It represents the additional dynamic load exerted by the aircraft landing gear on the pavement surface.

[0174] Step S811: constructing a discretized pavement structure vibration differential equation by using the mode superposition method.

[0175] Specifically, the airport runway pavement is first treated as a layered pavement structure model to establish a vibration equation describing the pavement structure vibration. This vibration equation is then discretized using the mode superposition method. The boundary conditions for the layered pavement structure model are that the soil base is fixed, the normal displacement of the transverse end surface is zero, and the normal displacement of the longitudinal end surface is zero. The expression of the discretized pavement structure vibration differential equation is shown in Equation (13):

[0176] (13)

[0177] Where, is the mass matrix of the layered pavement structure model, is the damping matrix of the layered pavement structure model, is the stiffness matrix of the layered pavement structure model, is the acceleration vector of the layered pavement structure model, is the velocity vector of the layered pavement structure model, is the displacement vector of the layered pavement structure model, is the excitation vector acting on the pavement structure.

[0178] Step S812: construct a finite element model, perform modal analysis on the discretized pavement structure vibration differential equation, and obtain the modal equation of the nth-order vibration mode of the pavement structure.

[0179] Specifically, a finite element model is first constructed to represent the discretized pavement structure. Then, a modal analysis is performed on this finite element model to obtain the modal equation of the nth-order vibration mode of the pavement structure. Among them, the important property of the orthogonality of the vibration mode is utilized in the modal analysis process. Through the orthogonality of the vibration mode, multiple interrelated vibration equations can be converted into independent generalized differential equations. These generalized differential equations describe the dynamic behavior of the system under specific vibration modes. Finally, by solving these generalized differential equations, the natural frequencies, damping ratios and vibration mode vectors of each order under free vibration can be obtained, providing initial parameters for the subsequent solution of the aircraft-pavement structure coupled dynamic equilibrium equation.

[0180] In particular, the specific steps of converting multiple interrelated vibration equations into independent generalized differential equations by using the orthogonality of vibration modes can include: multiplying both sides of the differential equation shown in formula (13) by the nth vibration mode vector The transpose of , we can get:

[0181] (14)

[0182] Then based on the orthogonality condition of the pavement structure dynamic vibration mode vector, we can get:

[0183] (15)

[0184] Where, represents the generalized mass of the nth-order vibration mode of the pavement structure, represents the generalized damping of the nth-order vibration mode of the pavement structure, Represents the generalized stiffness of the nth-order vibration mode of the pavement structure.

[0185] According to equations (14) and (15), the modal equation of the nth-order vibration mode of the pavement structure can be obtained as follows:

[0186] (16)

[0187] Where, is the generalized force of the nth-order vibration mode of the pavement structure.

[0188] This embodiment considers the coupled vibration of the aircraft and pavement structure when establishing the differential equations for the two-degree-of-freedom aircraft vertical vibration model. Therefore, the actual excitation of the pavement on the aircraft is the pavement roughness excitation coupled with the pavement structure vibration. Assuming that there is point contact between the aircraft landing gear wheels and the pavement surface, the actual pavement roughness excitation to the wheels is expressed as Equation (17):

[0189] (17)

[0190] Where, For road surface The incentives of For road surface The unevenness of the The pavement surface under the action of aircraft dynamic load The vertical displacement at is expressed as follows:

[0191] (18)

[0192] On the airport runway pavement, the mode vector The value of will change with the vertical x position, but will remain unchanged in the horizontal y direction. It represents the value of the vibration mode vector at any point on the road surface (determined by the x-coordinate).

[0193] Therefore, according to Equations (17) and (18), the expression of the generalized force of the nth-order vibration mode of the pavement structure can be obtained as shown in Equation (19):

[0194] (19)

[0195] Step S813: The aircraft-pavement structure coupled dynamic equilibrium equation is derived based on the two-degree-of-freedom aircraft vertical vibration model differential equation and the modal equation for the nth-order vibration mode of the pavement structure. This equation couples the vertical vibration displacement of the runway surface at the contact point between the aircraft wheel and the pavement with the runway surface roughness.

[0196] Finally, by combining Equations (11), (16), and (19), we can obtain the aircraft-pavement structure coupled dynamic equilibrium equation shown in Equation (8).

[0197] As can be seen from Equation (8), there are a total of n+2 unknown variables, where n is the number of generalized coordinates of the rigid pavement structure. In this embodiment, only the first 12 control modes are used for modal superposition during analysis and calculation. By moving some unknowns on the right side of the equations to the left side and sorting them out, the standard form of the two-degree-of-freedom aircraft-pavement structure coupled dynamic equilibrium equation can be obtained, as shown in Equation (20):

[0198] (20)

[0199] Where, is the total mass matrix of the coupled system, , is the overall damping matrix of the coupled system,

[0200] , is the overall stiffness matrix of the coupled system,

[0201] , is the overall load array of the coupled system, , is the total displacement array of the coupled system, .

[0202] Afterwards, the incremental Newmark-β method is used to solve the equations shown in Equation (20). The specific solution steps are as follows:

[0203] (1) Calculation of the initial time t0:

[0204] Step S820, obtain the initial mass matrix [M0], initial stiffness matrix [K0], initial damping matrix [C0], initial displacement vector of the coupling system and the initial velocity vector .

[0205] Step S821, according to the initial mass matrix [M0], initial stiffness matrix [K0], initial damping matrix [C0], initial displacement vector , initial velocity vector Determine the load array based on the aircraft-pavement structure coupled dynamic equilibrium equation , damping array and the array of spring forces .

[0206] Step S822, according to the formula

[0207] (twenty one)

[0208] Calculate the initial acceleration vector Where [M0] -1 is the inverse matrix of the initial mass matrix of the coupled system.

[0209] Step S823 : calculating the integral constant according to the preset time step Δt, the preset parameter δ, and the preset parameter γ.

[0210] Among them, the integral constants include a0=1 / (δΔt 2 ), a1=γ / (δΔt), a2=1 / (δΔt), a3=1 / 2δ-1, a4=γ / δ-1, a5=Δt / 2*(γ / δ-2), a6=Δt*(1-γ), a7=γΔt.

[0211] Step S824: Establish the effective stiffness matrix of the coupling system :

[0212] (twenty two)

[0213] The effective stiffness matrix is ​​the stiffness matrix after taking into account the damping and inertia effects, which is used in the subsequent calculation of displacement increments. In addition, to facilitate the solution, the effective stiffness matrix can be triangularly decomposed, converting the complex effective stiffness matrix into two relatively simple triangular matrices, greatly simplifying the calculation process.

[0214] (2) Calculate a preset time step The increment of each item within.

[0215] Step S825: Calculate the equivalent load increment within a preset time step. , displacement increment , speed increment and acceleration increment .

[0216] Among them, the equivalent load increment The calculation formula is:

[0217] (twenty three)

[0218] Where, is the original load increment.

[0219] Displacement increment The calculation formula is:

[0220] (twenty four)

[0221] Where, is the inverse matrix of the effective stiffness matrix.

[0222] Speed ​​increment The calculation formula is:

[0223] (25)

[0224] Acceleration increment The calculation formula is:

[0225] (26)

[0226] Step S826, calculate the displacement vector of the aircraft-runway interaction dynamic system at the end of a preset time step , velocity vector and the acceleration vector .

[0227] Among them, the displacement vector The calculation formula is:

[0228] (27)

[0229] Velocity vector The calculation formula is:

[0230] (28)

[0231] acceleration vector The calculation formula is:

[0232] (29)

[0233] In step S827, the displacement vector, velocity vector, and acceleration vector obtained in step S826 are used as initial values, and steps S820 to S826 are repeatedly executed until a preset loop end condition is met.

[0234] Specifically, the displacement at the current moment calculated based on equation (26) is used as the initial displacement at the next moment, the velocity at the current moment calculated based on equation (28) is used as the initial velocity at the next moment, and the acceleration at the current moment calculated based on equation (29) is used as the initial acceleration at the next moment. The preset loop end condition can be the time of interaction between the aircraft and the pavement structure. For example, due to the requirements of calculation accuracy and speed, only the first k-order vibration mode superposition can be calculated. For example, in the analysis and calculation, only the first 12-order vibration mode superpositions that play a controlling role are taken. After the loop is completed, the equivalent load increment at each moment can be obtained, and then the aircraft's precise dynamic load can be obtained based on the initial load array and the equivalent load increment at each moment. It is worth noting that the aircraft's precise dynamic load is an aircraft dynamic load that takes into account the pavement roughness and aircraft-pavement coupled vibration. Compared with the aircraft dynamic load calculated based on IRI in the prior art, it is more accurate and more reliable. Compared with IRI, it can know the impact of local pavement surface roughness on the aircraft's vibration response.

[0235] The airport pavement roughness detection method of this embodiment establishes an aircraft-pavement structure coupled dynamic equation and solves the equation using the incremental Newmark-β method. This equation can obtain accurate aircraft dynamic loads that take into account pavement roughness and aircraft-pavement coupled vibrations. This improves the accuracy and reliability of aircraft dynamic load calculations and provides more scientific data for airport operations and maintenance.

[0236] In order to better understand the airport pavement roughness detection method in Example 1 and Example 2, an example including specific data is provided below for explanation.

[0237] Example 3

[0238] In this example, two long-range 3D laser scanners, ScanStation P50, were used. The scanning distance was increased to over 1 km, with an accuracy of 3mm + 10ppm (570m / km) and a scanning speed of 1 million points / second. To improve the accuracy of the elevation measurement data, the operators divided the 3,600m runway into six sections, with two instruments responsible for Section A and Section B, respectively. Section A had milestones from 0 to 360m, and Section B had milestones from 360 to 720m. The fieldwork arrangements were as follows: Figure 6 As shown, Figure 6 The middle triangle represents the measurement site, and the circle represents the target. Figure 7 shown. Figure 7 (a) is the actual airport runway surface, Figure 7(b) shows the scanned point cloud data. The instruments and equipment required for the scan are listed in Table 1. Furthermore, since the airport runway is 3600 meters long, the data obtained from the 3D LiDAR scan is based on the target station's coordinate system, conforming to the Cartesian coordinate system. A total of 94 targets were used for elevation measurement of the runway. Furthermore, the maximum splicing error based on the 3D coordinate transformation model shown in Equation (1) is only 0.0005 meters, meeting the accuracy requirements of airport pavement engineering.

[0239] Table 1

[0240]

[0241] After the original 3D elevation point cloud data of the road surface was obtained by splicing, it was aligned and denoised by constructing a local reference plane, resulting in a total of 766,898,424 3D elevation point cloud data of the road surface. Afterwards, the 3D elevation point cloud data of the road surface was simplified by sampling techniques such as normal vector, curvature and compression rate, resulting in 3,912,084 simplified point cloud data. Figure 2 It can be seen that the runway point cloud image after registration, denoising and downsampling can better reflect the contour and detailed features of the runway surface, and the computational complexity is greatly reduced.

[0242] For a runway 3600m long and 45m wide, and based on the tire width of the Category C aircraft primarily operating at the airport, this embodiment divides the runway into strips of 0.5m width, for a total of 90 strips. With the runway length as the X-axis and the width as the Y-axis, within each strip, samples are taken at 0.5m intervals along the X-axis and 0.1m intervals along the Y-axis. Figure 8 The figure shows the distribution of pavement roughness along the entire length of the runway in a certain strip.

[0243] Furthermore, this example selects a Class C B737-800 aircraft commonly used at my country's trunk airports as the research object. It has a fuselage length of 39.5 m, a wingspan of 34.4 m, a maximum takeoff weight of 790.04 kN, a maximum landing weight of 663.80 kN, and a maximum taxiing weight of 792.60 kN (Design Specification for Cement Concrete Pavements at Civil Airports MH-T 5004-2024). The structural and geometric parameters related to its dynamic model are shown in Table 2. The parameters listed in Table 2 are all parameters included in the two-degree-of-freedom full-machine model. When calculating the vibration response of the aircraft model with other degrees of freedom, except for the body mass m, the parameters are not included in the calculation. s Other model parameters are included in the table except those that need to be calculated separately upon request.

[0244] Table 2

[0245]

[0246] Specifically, this embodiment uses a two-degree-of-freedom aircraft model for analysis and calculation. It can be seen from Table 2 that Figure 5 The values ​​of the parameters are: k s =k s2 =4800, c s =cs2=20,m t =mt2=74.55,k t =k t2 =9600, c t =c t2 =24,q t =q2=1.47.

[0247] The pavement structure model of this embodiment is an airport rigid pavement structure model, and its parameters are shown in Table 3.

[0248] Table 3

[0249]

[0250] By bringing the aircraft-related parameters obtained from Table 2 and the road-related parameters obtained from Table 3, as well as the road surface roughness of each strip obtained in this embodiment into the above-mentioned solution process, the following equation can be obtained: Figure 9 Figure 2 shows the aircraft dynamic loads taking into account pavement roughness and aircraft-pavement coupled vibrations.

[0251] Example 3

[0252] like Figure 10 As shown, this embodiment provides an airport pavement roughness detection system, including:

[0253] The pavement roughness information acquisition module is used to obtain the pavement three-dimensional elevation point cloud data and the pavement reference plane of the airport runway; and obtain the pavement roughness information based on the pavement three-dimensional elevation point cloud data and the pavement reference plane.

[0254] The runway strip roughness calculation module is used to divide the airport runway into strips according to preset widths; sample the pavement roughness information within the strips and calculate the roughness representative value of each sampling area; and obtain the strip roughness based on the roughness representative value of each sampling area and the pavement roughness information.

[0255] The airport runway pavement roughness information database construction and update module is used to construct the airport runway pavement roughness information database based on the roughness of each strip, and to timely iterate and update the airport runway pavement roughness information database based on the pavement three-dimensional elevation point cloud data at preset time intervals.

[0256] Furthermore, the pavement roughness information acquisition module also includes a pavement 3D elevation point cloud data splicing unit and a pavement 3D elevation point cloud data preprocessing unit. The pavement 3D elevation point cloud data splicing unit is used to splice the acquired pavement 3D elevation point cloud original data of each section of the airport runway. By constructing a 3D coordinate transformation model as shown in Equation (1), the 3D coordinate transformation model is linearized and then solved using an indirect adjustment method to obtain the coordinate transformation parameters; the coordinate transformation parameters include translation parameters, rotation parameters, and scale parameters; based on the coordinate transformation parameters, the pavement 3D elevation point cloud original data of each section is spliced ​​to obtain the pavement 3D elevation point cloud data. The pavement 3D elevation point cloud data preprocessing unit is used to perform registration, denoising, and downsampling on the pavement 3D elevation point cloud data.

[0257] Furthermore, the airport pavement roughness detection system also includes an aircraft dynamic load calculation module. The aircraft dynamic load calculation module is used to obtain the pavement roughness information on the aircraft wheel track from the airport runway pavement roughness information database. Based on the pavement roughness information and the aircraft-pavement structure coupled dynamic balance equation shown in Equation (8), the accurate dynamic load when the aircraft and pavement interact is obtained.

[0258] Furthermore, the airport pavement roughness detection system also includes an airport pavement operation and maintenance suggestion module. This module is used to generate airport pavement operation and maintenance suggestions for reference by airport operation and maintenance personnel based on the pavement roughness information stored in the airport runway pavement roughness information database, combined with the airport's geographical environment, operation and maintenance objectives, and other factors.

[0259] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0260] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for detecting unevenness of an airport pavement, characterized in that: The method comprises: Step S1, obtaining the three-dimensional elevation point cloud data and the pavement reference plane of the airport runway; said step S1 includes: Step S11, dividing the airport runway into sections and obtaining the original three-dimensional elevation point cloud data of each section of the runway; Step S12: construct a three-dimensional coordinate transformation model; the expression of the three-dimensional coordinate transformation model is: ； Where l is the scale factor of coordinate transformation, is the rotation matrix for coordinate transformation, is the translation vector, is the observation vector in the target coordinate system, is the observation vector in the original coordinate system; Step S13, after described three-dimensional coordinate conversion model is carried out linearization process, adopts indirect adjustment method to solve and obtains coordinate conversion parameter; Described coordinate conversion parameter comprises translation parameter, rotation parameter and scale parameter; Step S14, splicing the original three-dimensional elevation point cloud data of each section of the runway based on the coordinate conversion parameters to obtain the three-dimensional elevation point cloud data of the entire runway; Step S2: Obtaining road surface roughness information based on the road surface three-dimensional elevation point cloud data and the road surface reference plane; the calculation formula for the roughness representative value is: ； Where, Indicates the The representative value of roughness of the sampling area, Indicates the The number of roughness values ​​contained in the sampling area, Indicates the Within the sampling area The roughness value, Indicates the The average roughness of the sampling area; The calculation formula of the strip unevenness is: ； Where, Indicates the unevenness of the strip, Indicates the number of sampling areas contained in the strip; Step S3, dividing the airport runway into strips according to a preset width; Step S4, sampling the road surface roughness information within the strip and calculating the roughness representative value of each sampling area; Step S5, obtaining the roughness of the strip according to the roughness representative value of each sampling area; Step S6, constructing an airport runway pavement roughness information database based on the roughness of each strip; Step S7, obtaining the pavement roughness information on the aircraft wheel track line from the airport runway pavement roughness information database based on coordinate matching; Step S8, solving the precise dynamic load when the aircraft and the pavement interact based on the pavement roughness information and the aircraft-pavement structure coupled dynamic balance equation; the aircraft-pavement structure coupled dynamic balance equation is expressed as: ； Where, is the sprung mass, is the unsprung mass, is the main landing gear suspension stiffness coefficient, is the main landing gear wheel tire stiffness coefficient, is the main landing gear suspension damping coefficient, is the main landing gear wheel tire damping coefficient, is the vertical displacement of the sprung mass, is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass, is the vertical displacement of the unsprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, is the lift force exerted on the aircraft when it is taxiing on the runway. is the acceleration due to gravity; For road surface The unevenness of the is the first derivative of the roughness, is the number of generalized coordinates, is the generalized mass of the nth-order vibration mode of the pavement structure, is the nth-order generalized coordinate corresponding to the pavement structure, is the first derivative of the nth-order generalized coordinate corresponding to the pavement structure, is the second derivative of the nth-order generalized coordinate corresponding to the road surface structure, is the damping ratio of the nth-order vibration mode of the pavement structure, is the natural frequency of the nth-order vibration mode of the pavement structure, For road surface The vibration mode at ; The steps of constructing the aircraft-pavement structure coupled dynamic balance equation include: Step S810: Establish a two-degree-of-freedom aircraft vertical vibration model differential equation; the expression of the two-degree-of-freedom aircraft vertical vibration model differential equation is: ； Where, is the road surface roughness excitation at the landing gear tire contact point, is the first-order derivative of the road surface roughness excitation; the expression of the road surface roughness excitation is: ； Where, For road surface The incentives of For road surface The unevenness of the For road surface The vertical displacement at ; Step S811: constructing a discretized pavement structure vibration differential equation by using the mode superposition method; the expression of the discretized pavement structure vibration differential equation is: ； Where, is the mass matrix of the layered pavement structure model, is the damping matrix of the layered pavement structure model, is the stiffness matrix of the layered pavement structure model, is the acceleration vector of the layered pavement structure model, is the velocity vector of the layered pavement structure model, is the displacement vector of the layered pavement structure model, is the excitation vector acting on the pavement structure; Step S812: construct a finite element model, perform modal analysis on the discretized pavement structure vibration differential equation, and obtain the modal equation of the n-th order vibration mode of the pavement structure; the modal equation of the n-th order vibration mode of the pavement structure is expressed as: ； Where, is the generalized force of the n-th vibration mode of the pavement structure; the expression of the generalized force of the n-th vibration mode of the pavement structure is: ； Step S813: Obtain the aircraft-pavement structure coupled dynamic equilibrium equation based on the two-degree-of-freedom aircraft vertical vibration model differential equation and the modal equation of the nth-order vibration mode of the pavement structure.

2. The method for detecting airport pavement roughness according to claim 1, characterized in that: The step S1 comprises: Step S15: performing registration, denoising and downsampling on the road surface three-dimensional elevation point cloud data.

3. The method for detecting airport pavement roughness according to claim 2, characterized in that: The specific steps of downsampling include: Step S151, dividing the road surface three-dimensional elevation point cloud data into neighborhoods and calculating neighborhood points in each neighborhood; Step S152, calculating the normal angle between each point in the neighborhood and the neighboring point; Step S153, calculating the curvature of the neighborhood according to the normal angle value; Step S154, dividing the neighborhood into regions with obvious features and regions with unclear features based on the curvature of each neighborhood; Step S155, uniformly sampling the region with obvious features according to a first preset sampling number; Step S156: uniformly sample the region with unclear features according to a second preset sampling number.

4. The method for detecting airport pavement roughness according to claim 3, characterized in that: The first preset sampling number is U*(1-V), and the second preset sampling number is U*V, where U is the target sampling number and V is the uniform sampling property.

5. The method for detecting unevenness of an airport pavement according to claim 1, wherein: The step S8 further includes: Step S820, obtain the initial mass matrix [M0], initial stiffness matrix [K0], initial damping matrix [C0], initial displacement vector of the coupling system and the initial velocity vector ; Step S821, according to the initial mass matrix [M0], initial stiffness matrix [K0], initial damping matrix [C0], initial displacement vector , initial velocity vector The load array is determined by the aircraft-pavement structure coupled dynamic equilibrium equation , damping array and the array of spring forces ; Step S822, according to the formula ； Calculate the initial acceleration vector Where [M0] -1 is the inverse matrix of the initial mass matrix of the coupled system; Step S823, calculating the integral constant according to the preset time step Δt, the preset parameter δ and the preset parameter γ; the integral constant includes a0=1 / (δΔt 2 ), a1=γ / (δΔt), a2=1 / (δΔt), a3=1 / 2δ-1, a4=γ / δ-1, a5=Δt / 2*(γ / δ-2), a6=Δt*(1-γ), a7=γΔt; Step S824: Establish the effective stiffness matrix of the coupling system : ； Step S825: Calculate the equivalent load increment within a preset time step. , displacement increment , speed increment and acceleration increment ;in, The equivalent load increment The calculation formula is: ； Where, is the original load increment; The displacement increment The calculation formula is: ； Where, is the inverse matrix of the effective stiffness matrix; The speed increment The calculation formula is: ； The acceleration increment The calculation formula is: ； Step S826, calculate the displacement vector of the aircraft-runway interaction dynamic system at the end of a preset time step , velocity vector and the acceleration vector ;in, The displacement vector The calculation formula is: ； The velocity vector The calculation formula is: ； The acceleration vector The calculation formula is: ； In step S827, the displacement vector, velocity vector, and acceleration vector obtained in step S826 are used as initial values, and steps S820 to S826 are repeatedly executed until a preset loop end condition is met.

6. An airport pavement roughness detection system, characterized in that: The system comprises: The pavement roughness information acquisition module is used to obtain the three-dimensional elevation point cloud data and pavement reference plane of the airport runway; specifically: Divide the airport runway into sections and obtain the original 3D elevation point cloud data of each section of the runway; Construct a three-dimensional coordinate transformation model; the expression of the three-dimensional coordinate transformation model is: ； Where l is the scale factor of coordinate transformation, is the rotation matrix for coordinate transformation, is the translation vector, is the observation vector in the target coordinate system, is the observation vector in the original coordinate system; After linearizing the three-dimensional coordinate transformation model, an indirect adjustment method is used to obtain coordinate transformation parameters, which include translation parameters, rotation parameters, and scale parameters. splicing the original three-dimensional elevation point cloud data of each section of the runway based on the coordinate conversion parameters to obtain the three-dimensional elevation point cloud data of the entire runway; The road surface roughness information is obtained based on the road surface three-dimensional elevation point cloud data and the road surface reference plane; the calculation formula of the roughness representative value is: ； Where, Indicates the The representative value of roughness of the sampling area, Indicates the The number of roughness values ​​contained in the sampling area, Indicates the Within the sampling area The roughness value, Indicates the The average roughness of the sampling area; The calculation formula of the strip unevenness is: ； Where, Indicates the unevenness of the strip, Indicates the number of sampling areas contained in the strip; The runway strip roughness calculation module is used to divide the airport runway into strips according to a preset width; sample the pavement roughness information within the strips and calculate the roughness representative value of each sampling area; and obtain the roughness of the strip based on the roughness representative value of each sampling area and the pavement roughness information; An airport runway pavement roughness information library construction and update module is used to construct an airport runway pavement roughness information library based on the roughness of each strip, and iteratively update the airport runway pavement roughness information library based on the pavement three-dimensional elevation point cloud data at preset time intervals; obtain pavement roughness information on aircraft wheel tracks from the airport runway pavement roughness information library based on coordinate matching; and solve the precise dynamic load when the aircraft and pavement interact based on the pavement roughness information and the aircraft-pavement structure coupled dynamic balance equation; the expression of the aircraft-pavement structure coupled dynamic balance equation is: ； Where, is the sprung mass, is the unsprung mass, is the main landing gear suspension stiffness coefficient, is the main landing gear wheel tire stiffness coefficient, is the main landing gear suspension damping coefficient, is the main landing gear wheel tire damping coefficient, is the vertical displacement of the sprung mass, is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass, is the vertical displacement of the unsprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, is the lift force exerted on the aircraft when it is taxiing on the runway. is the acceleration due to gravity; For road surface The unevenness of the is the first derivative of the roughness, is the number of generalized coordinates, is the generalized mass of the nth-order vibration mode of the pavement structure, is the nth-order generalized coordinate corresponding to the pavement structure, is the first derivative of the nth-order generalized coordinate corresponding to the pavement structure, is the second derivative of the nth-order generalized coordinate corresponding to the road surface structure, is the damping ratio of the nth-order vibration mode of the pavement structure, is the natural frequency of the nth-order vibration mode of the pavement structure, For road surface The vibration mode at ; The steps of constructing the aircraft-pavement structure coupled dynamic balance equation include: A two-degree-of-freedom aircraft vertical vibration model differential equation is established; the expression of the two-degree-of-freedom aircraft vertical vibration model differential equation is: ； Where, is the road surface roughness excitation at the landing gear tire contact point, is the first-order derivative of the road surface roughness excitation; the expression of the road surface roughness excitation is: ； Where, For road surface The incentives of For road surface The unevenness of the For road surface The vertical displacement at ; The discretized pavement structure vibration differential equation is constructed by the mode superposition method; the expression of the discretized pavement structure vibration differential equation is: ； Where, is the mass matrix of the layered pavement structure model, is the damping matrix of the layered pavement structure model, is the stiffness matrix of the layered pavement structure model, is the acceleration vector of the layered pavement structure model, is the velocity vector of the layered pavement structure model, is the displacement vector of the layered pavement structure model, is the excitation vector acting on the pavement structure; A finite element model is constructed, and a modal analysis is performed on the discretized pavement structure vibration differential equation to obtain a modal equation of the n-th order vibration mode of the pavement structure. The modal equation of the n-th order vibration mode of the pavement structure is expressed as follows: ； Where, is the generalized force of the n-th vibration mode of the pavement structure; the expression of the generalized force of the n-th vibration mode of the pavement structure is: ； The aircraft-pavement structure coupled dynamic equilibrium equation is obtained according to the two-degree-of-freedom aircraft vertical vibration model differential equation and the modal equation of the nth-order vibration mode of the pavement structure.

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