Device and system for detecting bearing capacity of expressway

Abstract

The invention discloses a device and system for detecting the bearing capacity of an expressway, and belongs to the field of road construction, the device comprises an equipment control module, a detection module, a calculation module, a transmission module and a prediction module, the detection module comprises an environment detection module and a performance test module, the environment detection module comprises a drying detection unit, a humidity detection unit and a cold detection unit which correspond to three different environment conditions respectively, and the performance test module comprises a pavement performance test unit, a structure test unit and a material performance test unit. The system can automatically select a proper detection mode according to different environmental conditions.

Description

Technical Field

[0001] The present invention belongs to the field of road construction, and more particularly, to a detection device and system for the bearing capacity of expressways. Background Art

[0002] Expressways adapt to the development of industrialization and urbanization. Cities are places where industries and populations gather, and the growth of cars in cities is much faster than that in rural areas, making them the centers of car concentration. The construction of expressways often starts from urban ring roads, radial roads, and busy traffic sections, and gradually becomes the urban traffic backbone with expressways as the mainstay;

[0003] The development of automotive technology poses objective requirements for the construction of expressways. Cars have become an important means of transportation in human society. Infrastructure such as expressways can adapt to the two major development trends of lightweight and heavy-duty vehicles, and at the same time meet the requirements of high speed for passenger cars and large load capacity for freight cars;

[0004] Thus, expressways are an inevitable product of modern urban construction and development.

[0005] At the same time, the design of expressways needs to meet a series of technical standards, including road geometric design, bridge and tunnel design, subgrade and pavement design, etc. These standards stipulate the design requirements for the dimensions, geometric shapes, slopes, curve radii, etc. of various parts of expressways. The subgrade and pavement design of expressways is the basis for ensuring the firmness, flatness, and durability of the road structure. The design requirements for subgrade and pavement include the selection of subgrade soil, the stability design of embankments and slopes, the selection of pavement materials, and the thickness design, etc. Expressways are an important part of modern transportation infrastructure, featuring fast speed, convenience, safety, and high efficiency.

[0006] Therefore, during the construction of expressways, the detection of their road surfaces is essential, which needs to comply with the "Code for Field Testing of Highway Subgrade and Pavement", involving static load, dynamic load, crack detection, etc., but most of them are conventional detections. At present, in order to meet the long-term usage requirements, it is also necessary to conduct classified detections for different environments and predict the usage conditions in different environments, so as to ensure the subsequent road surface maintenance and other operations. Summary of the Invention

[0007] In view of the above defects, the present invention provides a detection system for the bearing capacity of expressways, which is characterized by including:

[0008] An equipment control module, used to control detection equipment with different bearing capacities, and through an integrated adaptive dynamic adjustment mechanism, automatically select and adjust the detection equipment and parameters according to the real-time data provided by the detection module;

[0009] The detection module includes an environment detection module and a performance testing module. The environment detection module is used to detect the current environment of the highway and transmit the corresponding environmental information to the equipment control module. The environment detection module includes a dry detection unit, a humid detection unit, and a cold detection unit, corresponding to three different environmental conditions respectively. The performance testing module includes a road surface performance testing unit, a structure testing unit, and a material performance testing unit;

[0010] The calculation module is used to calculate the bearing capacity detected by the detection equipment, and the calculation result is fed back to the equipment control module;

[0011] The transmission module is used to transmit the information of the detection module to the background terminal, and then the terminal processor in the background terminal transmits it to the equipment control module according to the preset information;

[0012] The prediction module, through finite element analysis software, based on the data information collected by the detection module, establishes a road surface and structure model, and through the integrated feedback learning mechanism, based on real-time monitoring data and historical data, optimizes the prediction model in real time, simulates the deformation of the highway under different loads and environmental conditions, and predicts the performance of the highway road surface and structure under specific climate changes or traffic loads.

[0013] Further, the environmental conditions triggered by the dry detection unit are: temperature > 15°C, humidity < 60%, and a static load test is carried out using a falling weight deflectometer, and the thickness and structure of the road surface layer are tested using a ground penetrating radar;

[0014] The environmental conditions triggered by the humid detection unit are: humidity ≥ 60% or precipitation weather, and after scanning with a ground penetrating radar, the strength of the internal structure of the highway is analyzed and detected using a sound detector;

[0015] The environmental conditions triggered by the cold detection unit are: temperature < 0°C, or ice and snow weather. In an environment with snow cover, a ground penetrating radar is used for scanning and detection, and in an environment without snow cover, a static load test is carried out using a falling weight deflectometer.

[0016] Further, the detection frequency of the dry detection unit using a falling weight deflectometer is:

[0017] One static load test is carried out every 100 meters, the falling weight is set as the standard, and 3 repeated tests are carried out, and the average value is taken;

[0018] The detection frequency of the dry detection unit using a ground penetrating radar is:

[0019] One ground penetrating radar scan is carried out every 200 meters.

[0020] Further, the detection frequency of the humid detection unit using a ground penetrating radar is:

[0021] Perform a geological radar scan every 100 meters, and use a high-frequency scan of 1.5 GHz.

[0022] The detection frequency of the acoustic detector used by the humidity detection unit is:

[0023] Perform an acoustic wave detection every 200 meters.

[0024] Furthermore, the monitoring frequency of the geological radar used by the cold detection unit is:

[0025] Perform a geological radar scan every 50 meters;

[0026] The monitoring frequency of the falling weight deflectometer used by the cold detection unit is:

[0027] Perform a scan with the falling weight deflectometer every 100 meters.

[0028] Furthermore, the road surface performance testing unit is used to test the friction coefficient of the road surface under different humidity conditions and measure the flatness of the road surface to ensure the comfort and safety of vehicle driving;

[0029] The structure testing unit is used to visually detect road surface cracks;

[0030] The material performance testing unit is used to test the weather resistance and durability of materials under different temperature and humidity conditions.

[0031] Furthermore, the prediction module includes a data acquisition unit for collecting various data detected by the detection module from road surface, structure and environmental monitoring;

[0032] A data processing and analysis unit for cleaning, fusing and analyzing the data collected by the data acquisition unit to identify key features;

[0033] A simulation calculation unit for performing finite element analysis based on the data of the data processing and analysis unit to simulate the deformation of highway road surface and structure under various conditions;

[0034] A visualization unit for presenting the simulation results output by the simulation calculation unit and real-time monitoring data in a graphical form.

[0035] The present invention also discloses a detection device for the bearing capacity of expressways, which is applied to the above-mentioned detection system for the bearing capacity of expressways. It includes a patrol vehicle equipped with a central processor integrating an equipment control module, a calculation module, and a prediction module, a ground penetrating radar, an acoustic wave detector, and a falling weight deflectometer for detecting expressways under different environments, a dynamic pavement performance tester and a drone for detecting different performances of expressways, and a wireless transceiver module for data transmission.

[0036] The present invention has the following beneficial effects compared with the prior art:

[0037] 1. Through the flexible design of the dry, wet, and cold detection units, the system can automatically select appropriate detection methods according to different environmental conditions. This feature makes the detection more targeted, enables operation under various climate conditions, and ensures the accuracy and reliability of the detection.

[0038] 2. According to different environmental conditions, the system sets appropriate detection frequencies (such as every 100 meters, every 50 meters, etc.). This improves the detection coverage rate, ensures timely problem discovery, and avoids potential safety hazards. By combining the detection frequency with environmental conditions, real-time monitoring of the road surface and structure status can be achieved, making maintenance decisions more scientific.

[0039] 3. The system integrates a variety of advanced devices such as a ground penetrating radar, an acoustic wave detector, a falling weight deflectometer, and a dynamic pavement performance tester, which can conduct comprehensive detections and reduce the number of devices and the complexity of management.

[0040] 4. The data processing and analysis unit cleans, fuses, and analyzes the collected data, can identify key features, and provides high-quality data support for subsequent simulation calculations. Through the simulation calculation unit, the system can perform finite element analysis based on real-time data, deeply understand the deformation of the road surface and structure under specific conditions, and provide a more scientific basis for prediction and decision-making.

[0041] 5. The prediction module of the system not only considers environmental factors but also combines historical data for multi-dimensional analysis, can early warn of potential risks of the road surface and structure, and ensure traffic safety. Specific Embodiments

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0043] Embodiment

[0044] In this embodiment, the expressway to be detected is a certain expressway in the central part of China. A detection system for the bearing capacity of the expressway is provided, which specifically includes the following modules:

[0045] The equipment control module is used to control the detection equipment with different bearing capacities;

[0046] The detection module includes an environment detection module and a performance test module. The environment detection module is used to detect the current environment of the expressway and transmit it to the equipment control module according to the corresponding environmental information. The environment detection module includes a dry detection unit, a humid detection unit, and a cold detection unit, which respectively correspond to three different environmental conditions;

[0047] Specifically, the environmental conditions triggered by the dry detection unit are: temperature > 15°C, humidity < 60%. A falling weight deflectometer is used for static load testing, and a ground penetrating radar is used for pavement layer thickness and structure testing. The detection frequency of the falling weight deflectometer used by the dry detection unit is 1 static load test per 100 meters. The falling weight is 9.8 kN, and 3 repeated tests are carried out. Take the average value (close to the standard of 9.8 kN). The measured pavement strain should be 1009895 (unit: micrometers). The detection frequency of the ground penetrating radar is 1 ground penetrating radar scan per 200 meters;

[0048] The environmental conditions triggered by the humid detection unit are: humidity ≥ 60% or precipitation weather. After scanning with a ground penetrating radar (frequency set to 1.5 GHz), analyze and use a sound detector to detect the strength of the internal structure of the expressway. The measured pavement layer thickness of the expressway is 20 cm, and the soil dielectric constant (based on GPR signal echo) is 4;

[0049] The environmental conditions triggered by the cold detection unit are: temperature < 0°C, or ice and snow weather. In an environment covered with snow, use a ground penetrating radar for scanning detection. At the same time, the ground penetrating radar can also be equipped with a heating wire or other heating mechanisms to work efficiently in a low-temperature environment. In an environment without snow cover, use a falling weight deflectometer for static load testing. Measure the acoustic propagation time: 0.5 ms. At the same time, based on the concrete strength of the measured pavement, the acoustic wave velocity of the measured pavement in this embodiment is about 3000 m / s;

[0050] It should be noted that the weather conditions can be obtained by accessing the weather system (such as the information of real-time weather forecasts).

[0051] The performance testing module includes a pavement performance testing unit (cooperating with a dynamic pavement performance tester to measure the friction coefficient and a falling weight deflectometer to measure the bearing capacity), a structure testing unit (cooperating with a ground penetrating radar to measure soil integrity, an acoustic wave detector to measure internal pavement defects, and an unmanned aerial vehicle to measure pavement cracks), and a material performance testing unit (cooperating with a falling weight deflectometer to measure the bearing capacity);

[0052] A calculation module is used to calculate in real time the bearing capacity and other performance indicators of the detection equipment (including the measured friction coefficient, elastic modulus, crack conditions, temperature and humidity, etc.). The calculation results can be fed back to the equipment control module to guide subsequent detections (that is, providing the data to the equipment control module, and through its adaptive dynamic adjustment mechanism, making corresponding adaptive adjustments to the detection range, frequency, etc.). Specifically:

[0053] The calculation of the data of the dry detection unit is as follows:

[0054] The calculation formula for the bearing capacity is:

[0055]

[0056] Among them, P = load (N) = 9.8 kN = 9800 N;

[0057]

[0058] The calculated bearing capacity of the expressway under test is:

[0059] That is, 100.3 MPa;

[0060] The calculation of the data of the wet detection unit is as follows:

[0061] There is a non-linear relationship between the dielectric constant of the soil and the water content (volumetric water content, VWC). The following formula is used for calculation:

[0062] ∈ r = ∈ r，dry +(VWC × K);

[0063] Among them, ∈ r is the dielectric constant of the soil, which is measured as 4, ∈ r，dry is the dielectric constant of the dry soil, taking the standard value of 3, and K is a constant related to the soil type, usually between 5 and 10. Taking 10, the water content can be deduced as follows:

[0064] 4 = 3 + VWC × 10, and it is obtained that VWC = 0.1, that is, the water content of the expressway pavement at this time is 10%;

[0065] The data of the cold detection unit is calculated as follows:

[0066] The calculation formula for the concrete strength fc is:

[0067]

[0068] After the information of the detection module is transmitted to the background terminal, the terminal processor in the background terminal transmits it to the device control module according to the preset information, where the preset information is the national standard for highway pavements. The following conclusions are all obtained after comparing with the national standard, specifically:

[0069] Under dry conditions, according to the national standard, the qualified road bearing capacity should generally be above 80 MPa, and the calculated pavement bearing capacity under this condition is 100.3 MPa. Therefore, the pavement condition is good and can withstand the design vehicle load, and the conclusion "can be used normally without immediate maintenance" is fed back to the background terminal;

[0070] Under humid conditions, when the moisture content is 10%, it generally does not cause serious impact on the pavement bearing capacity, but attention should be paid to the risk of possible moisture accumulation. The conclusion "no significant holes or cracks are found, but regular monitoring is required, especially during high precipitation periods" is fed back to the background terminal;

[0071] Under cold or extreme weather conditions, the concrete strength is about 0.667 MPa, which is lower than the minimum requirement of concrete under normal conditions (generally should be above 20 MPa). Insufficient strength may cause cracking and settlement of the pavement when heavy vehicles are driving, posing a safety hazard. It is concluded that the concrete may have freeze-thaw damage or other structural damages, and the conclusion "immediately conduct further structural inspections, and reinforce or resurface if necessary. At the same time, ensure preventive measures are taken under cold conditions, such as appropriate snow removal and anti-freezing treatments" is fed back to the background terminal.

[0072] At the same time, the background terminal can generate a sample report form through the traditional report generation function and record and store it in the local hard disk:

[0073] Environment Calculation data Suggestion Dry environment Bearing capacity 100.3MP Normal use, no maintenance required Humid environment Moisture content 10% Use appropriately, monitoring is recommended Cold environment Concrete strength 0.667MPa Reinforcement is recommended

[0074] And the process for guiding subsequent inspections includes

[0075] The prediction module, through finite element analysis software such as ANSYS, based on the data information collected by the detection module, establishes a pavement and structure model, simulates the deformation of the highway under different load and environmental conditions, and predicts the performance of the highway pavement and structure under specific climate changes or traffic loads.

[0076] Specifically, the prediction module includes a data acquisition unit for collecting various data detected by the detection module from road surfaces, structures, and environmental monitoring, including the bearing capacity, moisture content, and concrete strength of the road surface;

[0077] A data processing and analysis unit that cleans, fuses, and analyzes the data collected by the data acquisition unit to identify key features. Data cleaning means removing outliers and noise, data fusion means integrating data from multiple detection devices into a unified database, and analysis means identifying key features to ensure high-quality data support for subsequent simulation calculations. For example:

[0078] The cleaned road surface load data is 98 kN, the processed temperature data is 24.5 °C, and the average road surface strain is 198 μm;

[0079] A simulation calculation unit that performs finite element analysis (FEA) based on the data from the data processing and analysis unit to simulate the deformation of highway road surfaces and structures under various conditions. Its calculation content includes load effects (simulating the stress and deformation effects of different traffic loads on the road surface), temperature deformation (simulating the expansion or contraction behavior of the road surface structure due to temperature changes), and obtaining deformation results (such as the maximum stress is 1.5 MPa and the maximum deformation is 0.003 m);

[0080] A visualization unit (applicable to the background terminal) that presents the simulation results output by the simulation calculation unit and the real-time monitoring data in a graphical form. The output content can be in the form of a schematic diagram of the road surface deformation after loading, a pressure-deformation relationship diagram, or a report file, etc., which will not be elaborated here.

[0081] The prediction module can combine with the existing Structural Health Monitoring System (SHM) for systematic highway monitoring and management, and combine with the Internet of Things to achieve more convenient management functions.

[0082] The hardware used based on the above modules is as follows: including a patrol vehicle, which is equipped with a central processing unit integrating an equipment control module, a calculation module, and a prediction module, a ground-penetrating radar, an acoustic wave detector, and a falling weight deflectometer for highway detection in different environments, a dynamic road performance tester and a drone for highway performance detection, and a wireless transceiver module for data transmission.

[0083] It should be noted that visual detection is achieved by using a drone (UAV) and a high-resolution camera. The drone takes high-resolution photos of the highway road surface to identify surface cracks and other visible defects, which belongs to the prior art and will not be elaborated here.

[0084] At the same time, the detection parameters of the above devices in this embodiment are as follows:

[0085] The detection frequency of the dry detection unit using the falling weight deflectometer is: 1 static load test is carried out every 100 meters, the falling weight is set as the standard and 3 repeated tests are carried out, and the average value is taken; the detection frequency of using the ground penetrating radar is: 1 ground penetrating radar scan is carried out every 200 meters.

[0086] The detection frequency of the wet detection unit using the ground penetrating radar is: 1 ground penetrating radar scan is carried out every 100 meters, and a 1.5 GHz high-frequency scan is adopted; the detection frequency of using the acoustic wave detector is: 1 acoustic wave detection is carried out every 200 meters.

[0087] The monitoring frequency of the cold detection unit using the ground penetrating radar is: 1 ground penetrating radar scan is carried out every 50 meters; the monitoring frequency of using the falling weight deflectometer is: 1 falling weight deflectometer scan is carried out every 100 meters.

[0088] In addition, the equipment control module also has an adaptive dynamic adjustment mechanism, which can automatically select and adjust the detection equipment and parameters according to real-time data. The prediction module also has a feedback learning mechanism. Based on real-time monitoring data and historical data, an incremental learning algorithm is adopted. The prediction module adjusts its internal model in real time according to the results after each detection. The adaptive dynamic adjustment mechanism and the feedback learning mechanism are programs integrated into the equipment control module and the prediction module respectively, and their algorithm logics are as follows:

[0089] The adaptive dynamic adjustment mechanism includes dynamic parameter adjustment, that is, initial detection parameters (such as frequency, sensitivity, etc.) are set for each detection device, and these parameters are continuously adjusted through real-time monitoring data (such as temperature, humidity, road surface conditions). For example, when it is detected that the humidity increases by more than 80%, the system will automatically increase the detection frequency of the road surface friction coefficient to ensure safety;

[0090] The feedback learning mechanism refers to adopting an incremental learning algorithm. The prediction module adjusts its internal model according to the results after each detection. For example, if historical data shows that the road surface bearing capacity decreases under a certain humidity condition, the system will update the detection strategy under similar future conditions with this information. Specifically as follows:

[0091] The corresponding data information obtained through the detection module:

[0092] Environmental monitoring data, including humidity (relative humidity %), temperature (degrees Celsius °C), precipitation (millimeters), wind speed (meters per second);

[0093] Road surface bearing capacity data: including friction coefficient (dimensionless, usually between 0 and 1), bearing capacity (bearing weight per unit area, commonly expressed in kg / m²), road surface conditions (such as cracks, spalling, etc.);

[0094] Integrate the data into a data set:

[0095] Time Humidity (%) Temperature (°C) Precipitation (mm) Coefficient of friction <![CDATA[Bearing capacity (kg / m 2 )]]> 8:00 85 10 0 0.45 150 12:00 75 15 0 0.50 160 14:00 90 12 5 0.40 140 18:00 95 8 10 0.35 130

[0096] Analyze the dataset:

[0097] P1. Correlation analysis:

[0098] Calculate the Pearson correlation coefficient between humidity, temperature and friction coefficient to determine the strength of the linear relationship between them;

[0099] P2. Regression analysis:

[0100] Use a linear regression or multiple regression model to establish a mathematical model between environmental factors such as humidity and temperature and friction coefficient:

[0101] Friction coefficient = a × humidity + b × temperature + c;

[0102] where a, b, and c are regression coefficients and can be obtained from the above historical dataset;

[0103] P3. Time series analysis:

[0104] Use time series analysis methods (such as ARIMA model) to predict future changes in friction coefficient;

[0105] Results obtained from the analysis:

[0106] Correlation results:

[0107] Suppose the calculated correlation coefficient between humidity and friction coefficient is -0.75, and the correlation coefficient between temperature and friction coefficient is +0.6. This indicates that humidity and friction coefficient are negatively correlated (when humidity increases, friction coefficient decreases), while temperature and friction coefficient are positively correlated (when temperature increases, friction coefficient increases);

[0108] Model results:

[0109] The regression equation obtained through regression analysis, for example:

[0110] Friction coefficient = -0.002 × humidity + 0.01 × temperature + 0.25;

[0111] This indicates that humidity has a significant impact on the friction coefficient, and the direction of the impact is negative.

[0112] Based on the above results, the following strategies can be formulated:

[0113] When the humidity is higher than a certain threshold (such as 80%), the frequency of friction coefficient detection is automatically increased (i.e., the instruction is sent to the central processor of the patrol vehicle to remind to increase the detection frequency of the falling weight deflectometer), so as to ensure timely understanding of the road surface conditions. It should be noted that the above data is a hypothetical situation. In practice, there will also be cases where when the temperature is higher than the preset threshold, the detection frequency of road surface deformation is automatically increased, and when the precipitation exceeds the threshold, the detection frequency of cracks is automatically increased, etc.

[0114] Its function is that as the usage time increases, the data in the data set becomes more and more, and the threshold in the strategy will become more and more accurate, so as to realize the function of automatically adapting the detection frequency according to real-time data.

[0115] It should be noted that the structure described in the present invention can be implemented in many different forms and is not limited to the embodiments. Any equivalent transformation made by those of ordinary skill in the art using the content of the specification of the present invention, or directly or indirectly applied in other related technical fields, such as the loading and unloading of other articles, is included in the protection scope of the present invention.

Claims

1. A detection system for the bearing capacity of a highway, characterized in that, Including: The device control module is used to control inspection devices with different load-bearing capacities, and through the integrated adaptive dynamic adjustment mechanism, automatically select and adjust the inspection devices and parameters according to the real-time data provided by the detection module; The detection module includes an environment detection module and a performance test module. The environment detection module is used to detect the current environment of the highway and transmit it to the device control module according to the corresponding environmental information. The environment detection module includes a dry detection unit, a humid detection unit, and a cold detection unit, corresponding to three different environmental conditions respectively. The performance test module includes a road surface performance test unit, a structure test unit, and a material performance test unit; The calculation module is used to calculate the bearing capacity detected by the inspection device, and the calculation result is fed back to the device control module; The transmission module is used to transmit the information of the detection module to the background terminal, and then the terminal processor in the background terminal transmits it to the device control module according to the preset information; The prediction module, based on the data information collected by the detection module through finite element analysis software, establishes a road surface and structure model, and through the integrated feedback learning mechanism, based on real-time monitoring data and historical data, optimizes the prediction model in real time, simulates the deformation of the highway under different loads and environmental conditions, and predicts the performance of the highway road surface and structure under specific climate changes or traffic loads.

2. The detection system for the bearing capacity of expressways according to claim 1, characterized in that: The environmental conditions triggered by the dry detection unit are: temperature > 15°C, humidity < 60%, use a falling weight deflectometer for static load testing and use a ground penetrating radar for road surface layer thickness and structure testing; The environmental conditions triggered by the humid detection unit are: humidity ≥ 60% or precipitation weather, use a ground penetrating radar to scan and analyze and use a sound detector to detect the strength of the internal structure of the highway; The environmental conditions triggered by the cold detection unit are: temperature < 0°C, or ice and snow weather. Use a ground penetrating radar to scan and detect in an environment with snow cover, and use a falling weight deflectometer for static load testing in an environment without snow cover.

3. The detection system for the bearing capacity of the highway according to claim 2, characterized in that: The detection frequency of the falling weight deflectometer used by the dry detection unit is: Conduct 1 static load test every 100 meters, set the falling weight as the standard and conduct 3 repeated tests, and take the average value; The detection frequency of the ground penetrating radar used by the dry detection unit is: Conduct 1 ground penetrating radar scan every 200 meters.

4. The detection system for the bearing capacity of a highway according to claim 2, wherein: The detection frequency of the ground penetrating radar used by the humid detection unit is: Conduct 1 ground penetrating radar scan every 100 meters, and use a 1.5 GHz high-frequency scan; The detection frequency of the acoustic detector used by the humid detection unit is: Conduct 1 acoustic detection every 200 meters.

5. The detection system for the bearing capacity of a highway according to claim 2, wherein: The monitoring frequency of the ground penetrating radar used by the cold detection unit is: Conduct 1 ground penetrating radar scan every 50 meters; The monitoring frequency of the falling weight deflectometer used by the cold detection unit is: Conduct 1 scan with a falling weight deflectometer every 100 meters.

6. The detection system for the bearing capacity of the highway according to claim 1, characterized in that: The road surface performance test unit is used to test the friction coefficient of the road surface under different humidity conditions and measure the flatness of the road surface; The structure test unit is used to visually detect road surface cracks; The material performance test unit is used to test the weather resistance and durability of the test material under different temperature and humidity conditions.

7. The detection system for the bearing capacity of a highway according to claim 1, characterized in that: The prediction module includes a data acquisition unit for collecting various data detected by the detection module from road surface, structure and environmental monitoring; a data processing and analysis unit for cleaning, fusing and analyzing the data collected by the data acquisition unit to identify key features; a simulation calculation unit for performing finite element analysis based on the data of the data processing and analysis unit to simulate the deformation of highway road surface and structure under various conditions; a visualization unit for presenting the simulation results output by the simulation calculation unit and real-time monitoring data in a graphical form.

8. A detection device for the bearing capacity of a highway, which is used for the detection system of the bearing capacity of a highway according to any one of claims 1-7, characterized in that: It includes a patrol vehicle equipped with a central processing unit integrating an equipment control module, a calculation module and a prediction module, geological radar, sonic detector and falling weight deflectometer for highway detection under different environments, dynamic pavement performance tester and unmanned aerial vehicle for highway different performance detection, and a wireless transceiver module for data transmission.

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