A road hole monitoring and identifying method based on Gaussian function

Abstract

This invention discloses a method for detecting and identifying road cavities based on Gaussian functions, comprising: setting a preset number of road monitoring points on a target road; obtaining strain influence lines based on the road monitoring points, wherein the target road is a road with cavities; determining initial road cavity location segments based on the strain influence lines and a Gaussian function; selecting road monitoring target points according to the initial road cavity location segments; and obtaining road cavity location points based on the strain abrupt change points of the road monitoring target points. This invention is not only computationally simple and easy to operate, greatly improving detection efficiency, but also allows for quantitative evaluation of the detection results, providing a strong basis for subsequent road maintenance and repair.

Description

Technical Field

[0001] This invention belongs to the field of road detection technology, and in particular relates to a method for monitoring and identifying road voids based on Gaussian functions. Background Technology

[0002] In urban infrastructure construction, roads serve as a crucial transportation carrier, and their condition has a significant impact on urban operation and traffic safety. However, due to prolonged use, natural factors, and human factors, roads may develop cavities. These cavities may arise from factors such as long-term external pressure on the road surface, vibrations, groundwater or soil erosion. If not detected and addressed promptly, these cavities may expand further, leading to road subsidence and collapse, causing serious damage.

[0003] However, current methods for detecting road cavities mostly rely on manual inspections or traditional measuring equipment, such as manual inspections, radar detection, and acoustic detection. These methods all have limitations. For example, manual inspections cannot accurately detect cavities under the road surface, while radar detection and acoustic detection can be affected by factors such as road materials and traffic noise. These methods are not only inefficient and have long detection intervals, but they also cannot monitor road conditions in real time. The limitations of traditional methods become even more apparent when dealing with large-scale road networks. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a road cavity monitoring and identification method based on Gaussian functions, which can overcome the limitations of traditional cavity detection methods.

[0005] To achieve the above objectives, this invention proposes a road cavity monitoring and identification method based on Gaussian functions, comprising:

[0006] A predetermined number of road monitoring points are set up on the target road, and a strain influence line is obtained based on the road monitoring points, wherein the target road is a road with voids;

[0007] Based on the strain influence line and combined with the Gaussian function, the initial road cavity location segment is determined;

[0008] Based on the initial road cavity location segment, select road monitoring target points, and obtain road cavity location points based on the strain mutation points of the road monitoring target points.

[0009] Optionally, obtaining the strain influence line based on the road monitoring points includes:

[0010] A predetermined number of strain monitoring sensors are embedded inside the road. A moving load is applied to the road to obtain the strain influence lines of the corresponding road monitoring points.

[0011] Optionally, the process of obtaining the strain influence line of the road monitoring point includes:

[0012] Vertical strain monitoring sensors are embedded in the upper surface of the road subbase and are fixedly buried at the same intervals along the road direction. The moving load is made to travel at a constant speed in a straight line along the road direction to obtain the strain influence line of the road monitoring point.

[0013] Optionally, the road segments for locating road cavities include:

[0014] Extract the peak points of the strain influence line, and perform Gaussian function fitting on the peak points to obtain the fitting curve;

[0015] Based on the fitted curve, the road cavity location segment is obtained.

[0016] Optionally, the method for fitting the peak points with a Gaussian function is as follows:

[0017]

[0018] Among them, y i x represents the peak value of the strain influence line corresponding to the monitoring point. i The location is marked as the monitoring point, A is the peak value of the Gaussian curve, and x is the position of the monitoring point. c y0 represents the position of the peak value of the Gaussian curve, S represents the half-width information of the Gaussian curve, and y0 represents the peak value of the strain influence line when there are no road cavities.

[0019] Optionally, based on the initial road cavity location segment, a road monitoring target point is selected, and the road cavity location point is obtained based on the strain abrupt change point of the road monitoring target point, including:

[0020] Select the road monitoring point that is closest to the road cavity location section. The closest road monitoring point is the road monitoring target point. Move the load in a circular trajectory with the road monitoring target point as the center.

[0021] Record the strain abrupt change points of the road monitoring target points, and obtain the road cavity location points based on the strain abrupt change points and the center of the circle.

[0022] Optionally, during the process of moving the load in a circular trajectory with the road monitoring target point as the center, the moving load should maintain a constant speed and a consistent distance from the center.

[0023] Optionally, based on the strain abrupt change point and the center of the circle, the location points of road cavities can be obtained as follows:

[0024] Connect the strain abrupt change point and the center of the circle to obtain the first route;

[0025] The location point of the road cavity is obtained based on the intersection of the first route and the road cavity location section.

[0026] Compared with the prior art, the present invention has the following advantages and technical effects:

[0027] This invention enables real-time monitoring by setting a preset number of road monitoring points. It extracts the peak points of the strain influence lines from each monitoring point and fits them with a Gaussian function, simplifying the calculation. Using the Gaussian function, it determines the initial road cavity location segment, selects the nearest road monitoring point within that segment, and obtains the road cavity location point based on the strain abrupt change point of the target monitoring point, ensuring accurate positioning. Furthermore, by selecting the nearest road monitoring point within the target segment and using it as the center, a circular trajectory is drawn to move the load, recording the strain abrupt change point of the target monitoring point. This method is convenient to operate and highly efficient. Therefore, based on advanced sensing technology and data analysis methods, this invention can detect and locate road cavities in real-time, quickly, and accurately. This method is not only computationally simple and easy to operate, greatly improving detection efficiency, but it also allows for quantitative evaluation of the detection results, providing a strong basis for subsequent road maintenance and repair. Attached Figure Description

[0028] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0029] Figure 1 This is a flowchart illustrating the road cavity monitoring and identification method based on Gaussian functions according to an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the asphalt concrete pavement structure according to an embodiment of the present invention;

[0031] Figure 3 This is a top-down cross-sectional view of an embodiment of the present invention.

[0032] Figure 4 This is a schematic diagram of the working condition fitting curve according to an embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram illustrating the determination of road cavity location points under working conditions according to an embodiment of the present invention;

[0034] Figure 6 This is a graph showing the fitting curve results of the working conditions in an embodiment of the present invention. Detailed Implementation

[0035] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0037] This invention proposes a method for detecting and identifying road cavities based on Gaussian functions, such as... Figure 1 As shown, the specific steps include:

[0038] Step 1: Bury multiple strain monitoring sensors inside existing or newly built roads, apply moving loads to roads with cavities, and obtain strain influence lines for multiple road monitoring points;

[0039] Step 2: Take the peak points of the strain influence lines of each monitoring point, perform Gaussian function fitting, and obtain the fitting curve to preliminarily determine the road segment line where the road cavity is located;

[0040] Step 3: Select the road monitoring point that is closest to the initially determined road segment line. Use this monitoring point as the center to move the load in a circular trajectory and record the strain change point of this monitoring point. The line connecting this point and the center of the circle intersects the initially determined road segment line at one point to obtain the location point of the road cavity.

[0041] Furthermore, the process of obtaining the strain influence line of the road monitoring points includes:

[0042] Vertical strain sensors are embedded in the upper surface of the road subbase and are fixedly buried at the same intervals along the road direction. The moving load is made to travel at a constant speed in a straight line along the road direction to obtain the strain influence line of the monitoring point.

[0043] Furthermore, the peak value of the strain influence line at each monitoring point is taken and recorded as (x i y i ), x i For the location of the monitoring point, y i The peak value of the strain influence line corresponding to the monitoring point, i = 1, 2, 3...

[0044] Furthermore, for (x) i y i The data is fitted with a Gaussian function to establish a model:

[0045]

[0046] Parameters A and x that need to be estimated c S and y0 are the peak value, peak position, half-width information and value range of the Gaussian curve, respectively, and y0 is the peak value of the strain influence line when there are no road cavities.

[0047] Specifically, derive parameters A and x c The formula for calculating S is transformed by taking the natural logarithm of both sides of the equation.

[0048]

[0049] Let, ln(y i -y0)=z i ,

[0050] Transform into a quadratic polynomial fitting function:

[0051]

[0052] Among them, z i b0, b1 and b2 are all transition parameters.

[0053] By considering all recorded peak data, estimate parameters A and x. c And S, and expressed in matrix form as:

[0054]

[0055] This can be abbreviated as Z = XB;

[0056] Where Z is a matrix X is a matrix B is a matrix

[0057] According to the least squares principle, the generalized least squares solution constituting matrix B can be obtained, and the parameters A and x that need to be estimated can be calculated. c and S;

[0058] Furthermore, the fitted curve, x, is obtained. c This indicates the horizontal position of the road cavity, thus initially determining the location of the road section where the cavity is located.

[0059] Furthermore, during the process of moving the load along a circular trajectory with the monitoring point as the center, the moving load should maintain a uniform speed and a consistent distance from the center.

[0060] Example 1

[0061] This embodiment discloses an example of an asphalt concrete pavement structure, which, from top to bottom, consists of a surface layer, a base layer, strain sensors, a subbase, and a subgrade. Figure 2 As shown in Table 1, the material parameters of each structural layer are as follows. It should be noted that vertical strain sensors are embedded in the upper surface of the subbase, and are sequentially buried at 1m intervals along the road direction.

[0062] Table 1

[0063]

[0064] Operating conditions:

[0065] This embodiment simulates a road cavity with a radius of 0.2m and a burial depth of 1.3m. The horizontal distance of this cavity from the monitoring point axis is 1m, and the longitudinal distance from the first monitoring point is 4.3m. (See schematic diagram). Figure 3 .

[0066] The moving load travels on the road at a speed of 60 km / h, passing directly above a road cavity. The moving load is a single-wheel load with dimensions of 20 mm × 20 mm and a magnitude of 1000 kPa. Data is recorded at each monitoring point during the movement.

[0067] by Figure 3 Taking eight monitoring points as an example, from left to right, they are numbered 1-8. Taking the position of monitoring point 1 as the origin, (x i y i ), x i For the location of the monitoring point, y i Table 2 shows the corresponding data for the peak values ​​of the strain influence lines at the monitoring points (i = 1, 2, 3, ..., 8). y0 represents the peak value of the strain influence line when there are no road cavities, y0 = -6.03084 × 10⁻⁶. -6 .

[0068] Table 2

[0069]

[0070] Substituting the data into the formula, the matrix form is as follows:

[0071]

[0072] Finally, x can be obtained. c =3.37375, S=0.74096, A=-3.51124×10 -8 Substituting the values, we get the fitting formula:

[0073]

[0074] In summary, the peak position of the curve corresponds to the location of the road cavity section. Figure 4 As shown, the calculated result is 3.37375m, which is very close to the actual 3.4m, indicating that this method of monitoring and judgment is accurate. Subsequently, monitoring point No. 4, which is closest to the 3.37375m road segment line, was selected. A moving load was then uniformly moved in a circle with a radius of 2m centered on monitoring point No. 4. The points of sudden strain changes were recorded. The line connecting this point to the center of the circle intersects the initially determined road segment line at a single point, thus obtaining the location of the road cavity. Figure 5 As shown.

[0075] Example 2

[0076] This embodiment is a simplification based on Embodiment 1. The monitoring point interval is 2m, that is, five monitoring points are selected: No. 1, No. 3, No. 5, No. 7, and No. 9. Additionally, x9 = 8, y9 = -6.03084 × 10⁻⁶. -6

[0077] Substituting the data into the formula, the matrix form is as follows:

[0078]

[0079] Finally, x can be obtained. c =3.55943, S=0.90544, A=-2.78335×10 -8 Substituting the values, we get the fitting formula:

[0080]

[0081] In summary, the predicted value is 3.55943m. Compared with Example 1, the accuracy is slightly lower, but it is still very close to the actual value of 3.4m. Figure 6 As shown, the smaller the spacing between monitoring points, the more accurate the location of road cavities can be determined. Increasing the spacing can save costs while still maintaining good accuracy.

[0082] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring and identifying road voids based on Gaussian functions, characterized in that, include: A predetermined number of road monitoring points are set up on the target road, and a strain influence line is obtained based on the road monitoring points, wherein the target road is a road with voids; Based on the road monitoring points, the strain influence lines obtained include: A predetermined number of strain monitoring sensors are embedded inside the road. A moving load is applied to the road to obtain the strain influence lines of the corresponding road monitoring points. The process of obtaining the strain influence line of the road monitoring point includes: Vertical strain monitoring sensors are embedded in the upper surface of the road subbase and are fixedly buried at the same intervals along the road direction. The moving load is made to travel at a constant speed in a straight line along the road direction to obtain the strain influence line of the road monitoring point. Based on the strain influence line and combined with the Gaussian function, the initial road cavity location segment is determined; Based on the initial road cavity location section, select road monitoring target points, and obtain road cavity location points based on the strain change points of the road monitoring target points; Based on the initial road cavity location segment, road monitoring target points are selected, and the road cavity location points are obtained based on the strain abrupt change points of the road monitoring target points, including: Select the road monitoring point that is closest to the road cavity location section. The nearest road monitoring point is the road monitoring target point. Move the load in a circular trajectory with the road monitoring target point as the center. Record the strain abrupt change points of the road monitoring target points, and obtain the road cavity location points based on the strain abrupt change points and the center of the circle; During the process of moving the load in a circular trajectory with the road monitoring target point as the center, the moving load should maintain a constant speed and a consistent distance from the center of the circle.

2. The method for monitoring and identifying road voids based on Gaussian functions according to claim 1, characterized in that, The road sections where road cavities were located include: Extract the peak points of the strain influence line, and perform Gaussian function fitting on the peak points to obtain the fitting curve; Based on the fitted curve, the road cavity location segment is obtained.

3. The method for monitoring and identifying road voids based on Gaussian functions according to claim 2, characterized in that, The method for fitting the peak points with a Gaussian function is as follows: in, y i This corresponds to the peak value of the strain influence line at the monitoring point. x i For the location of the monitoring point, A The peak value of the Gaussian curve 、x c The position of the peak of the Gaussian curve 、S For Gaussian curve half-width information and y 0 The peak value of the strain influence line when there are no road cavities.

4. The method for monitoring and identifying road voids based on Gaussian functions according to claim 1, characterized in that, Based on the strain abrupt change point and the center of the circle, the location points of road cavities are obtained as follows: Connect the strain abrupt change point and the center of the circle to obtain the first route; The location point of the road cavity is obtained based on the intersection of the first route and the road cavity location section.

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