Highway load-bearing capacity testing device and system

Abstract

This invention discloses a testing device and system for the load-bearing capacity of highways, belonging to the field of road construction. It includes an equipment control module, a testing module, a calculation module, a transmission module, and a prediction module. The testing module includes an environmental testing module and a performance testing module. The environmental testing module includes a dry testing unit, a wet testing unit, and a cold testing unit, corresponding to three different environmental conditions. The performance testing module includes a pavement performance testing unit, a structure testing unit, and a material performance testing unit. Through the flexible design of the dry, wet, and cold testing units, the system can automatically select the appropriate testing method according to different environmental conditions.

Description

Technical Field

[0001] This invention belongs to the field of road construction, and more specifically, relates to a device and system for detecting the load-bearing capacity of highways. Background Technology

[0002] Highways adapt to the development of industrialization and urbanization. Cities are the concentration of industry and population, and their automobile growth is much faster than that of rural areas, making them automobile hubs. Highway construction often starts with urban ring roads, radial roads, and busy traffic sections, gradually becoming an urban transportation system with highways as the backbone.

[0003] The development of automotive technology has placed objective demands on highway construction. Automobiles have become an important means of transportation in human society, and highways and other infrastructure can support the two major development trends of lighter and heavier vehicles, while simultaneously meeting the needs of high-speed passenger vehicles and heavy-duty freight vehicles.

[0004] Thus, expressways have become an inevitable product of modern urban development.

[0005] Meanwhile, highway design must comply with a series of technical standards, including road geometry design, bridge and tunnel design, and subgrade and pavement design. These standards specify the design requirements for the dimensions, geometry, gradient, curve radius, and other aspects of various parts of the highway. The subgrade and pavement design is fundamental to ensuring the highway structure is robust, flat, and durable. Subgrade and pavement design requirements include the selection of subgrade soil, the stability design of embankments and slopes, and the selection and thickness design of pavement materials. Highways are an important component of modern transportation infrastructure, characterized by speed, convenience, safety, and efficiency.

[0006] Therefore, road surface testing is essential during the construction of highways and must comply with the "Highway Subgrade and Pavement Field Testing Regulations," which involve static load, dynamic load, and crack testing. However, most of these tests are routine. To meet the needs of long-term use, it is now necessary to conduct classified testing for different environments and predict the usage conditions in different environments in order to ensure the smooth progress of subsequent road surface maintenance and other operations. Summary of the Invention

[0007] To address the above deficiencies, this invention provides a system for detecting the load-bearing capacity of highways, characterized by comprising:

[0008] The equipment control module is used to control testing equipment with different load capacities, and through an integrated adaptive dynamic adjustment mechanism, it automatically selects and adjusts the testing equipment and parameters based on real-time data provided by the testing module.

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

[0010] The calculation module is used to calculate the load-bearing capacity detected by the testing equipment, and the calculation results are fed back to the equipment control module.

[0011] The transmission module is used to transmit the information from the detection module to the back-end terminal, and the terminal processor in the back-end terminal transmits the information to the device control module according to the preset information.

[0012] The prediction module uses finite element analysis software to build road surface and structure models based on data collected by the detection module. Through an integrated feedback learning mechanism, it optimizes the prediction model in real time based on real-time monitoring data and historical data, simulates the deformation of highways under different loads and environmental conditions, and predicts the performance of highway pavement and structures under specific climate changes or traffic loads.

[0013] Furthermore, the environmental conditions triggered by the drying detection unit are: temperature > 15℃, humidity < 60%, static load test using a falling weight pavement bearing capacity tester, and pavement layer thickness and structure test using ground-penetrating radar.

[0014] The environmental conditions triggered by the humidity detection unit are: humidity ≥ 60% or precipitation. After scanning with ground-penetrating radar, the strength of the internal structure of the highway is detected by an acoustic detector.

[0015] The environmental conditions triggered by the cold detection unit are: temperature < 0℃, or snowy weather. Ground-penetrating radar is used for scanning detection in environments with snow cover, and a falling-weighted pavement bearing capacity tester is used for static load testing in environments without snow cover.

[0016] Furthermore, the detection frequency of the dryness detection unit using the falling weight pavement bearing capacity tester is:

[0017] A static load test was conducted every 100 meters. The weight of the falling hammer was set as the standard, and the test was repeated three times. The average value was taken.

[0018] The detection frequency of the ground-penetrating radar used in the drying detection unit is:

[0019] A ground-penetrating radar scan is performed every 200 meters.

[0020] Furthermore, the detection frequency of the ground-penetrating radar used in the moisture detection unit is:

[0021] A ground-penetrating radar scan was performed every 100 meters, using a high-frequency scan of 1.5 GHz.

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

[0023] A sound wave test is performed every 200 meters.

[0024] Furthermore, the monitoring frequency of the ground-penetrating radar used in the cold detection unit is:

[0025] A ground-penetrating radar scan is performed every 50 meters;

[0026] The cold weather detection unit uses a falling weight pavement bearing capacity tester with the following monitoring frequency:

[0027] A drop weight pavement bearing capacity tester scan is performed every 100 meters.

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

[0029] The structural testing unit is used for visual inspection of 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 types of data detected by the detection module from road surface, structure, and environmental monitoring.

[0032] A data processing and analysis unit that cleans, merges, and analyzes the data collected by the data acquisition unit, and identifies key features;

[0033] A simulation calculation unit that performs finite element analysis based on data from the data processing and analysis unit to simulate the deformation of highway pavement and structures under various conditions.

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

[0035] The present invention also discloses a highway load-bearing capacity testing device, applied to the aforementioned highway load-bearing capacity testing system, comprising a patrol vehicle equipped with a central processing unit integrating an equipment control module, a calculation module, and a prediction module; a ground-penetrating radar, an acoustic detector, and a falling-weight pavement load-bearing capacity tester for testing under different highway environments; a dynamic pavement performance tester and a drone for testing different highway performance; and a wireless transceiver module for data transmission.

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] 1. Through the flexible design of dry, humid and cold detection units, the system can automatically select the appropriate detection method according to different environmental conditions. This feature makes the detection more targeted and can operate under various climatic conditions, ensuring the accuracy and reliability of the detection.

[0038] 2. Based on different environmental conditions, the system sets appropriate detection frequencies (such as every 100 meters, every 50 meters, etc.), which improves the detection coverage, ensures that problems can be detected in a timely manner, avoids potential safety hazards, and by combining the detection frequency with environmental conditions, it can realize real-time monitoring of the condition of the road surface and structures, making maintenance decisions more scientific.

[0039] 3. The system integrates a variety of advanced equipment such as ground-penetrating radar, acoustic wave detector, falling weight pavement bearing capacity tester, and dynamic pavement performance tester, which can carry out comprehensive testing and reduce the number of equipment and the complexity of management.

[0040] 4. The data processing and analysis unit cleans, merges, and analyzes the collected data, identifies 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 to gain a deeper understanding of the deformation of the road surface and structures under specific conditions, providing a more scientific basis for prediction and decision-making.

[0041] 5. The system's prediction module not only considers environmental factors but also combines historical data for multi-dimensional analysis, enabling early warning of potential road and structural risks and ensuring traffic safety. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] Example

[0044] The highway tested in this embodiment is a highway in central my country, and a highway load-bearing capacity testing system is provided, which specifically includes the following modules:

[0045] The equipment control module is used to control testing equipment with different load capacities;

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

[0047] In detail, the environmental conditions triggered by the dry testing unit are: temperature > 15℃, humidity < 60%, static load test is performed using a drop weight pavement bearing capacity tester, and pavement layer thickness and structure test is performed using ground-penetrating radar. The detection frequency of the dry testing unit using the drop weight pavement bearing capacity tester is one static load test every 100 meters, the drop weight is 9.8kN, and three repeated tests are performed. The average value (close to the standard of 9.8kN) is taken. The measured pavement pressure strain is 1009895 (unit: micrometers). The detection frequency of the ground-penetrating radar is one scan every 200 meters.

[0048] The environmental conditions triggered by the humidity detection unit are: humidity ≥ 60% or precipitation. After scanning with ground-penetrating radar (frequency set to 1.5GHz), the strength of the internal structure of the highway is analyzed and detected using an acoustic detector. The thickness of the highway pavement layer is measured to be 20cm 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℃, or snowy weather. Ground-penetrating radar is used for scanning and detection in environments with snow cover. At the same time, the ground-penetrating radar can also be equipped with heating mechanisms such as heating wires to work efficiently in low-temperature environments. In environments without snow cover, a falling weight pavement bearing capacity tester is used to conduct static load tests and measure the sound wave propagation time: 0.5ms. Based on the concrete strength of the tested pavement, the sound wave velocity of the tested pavement in this embodiment is approximately 3000m / s.

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

[0051] The performance testing module includes a pavement performance testing unit (used with a dynamic pavement performance tester to measure the coefficient of friction and a drop weight pavement bearing capacity tester to measure the bearing capacity), a structure testing unit (used with ground-penetrating radar to measure soil integrity, an acoustic detector to measure internal pavement defects, and a drone to measure pavement cracks), and a material performance testing unit (used with a drop weight pavement bearing capacity tester to measure the bearing capacity).

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

[0053] The data from the drying detection unit are calculated as follows:

[0054] The formula for calculating load-bearing capacity is:

[0055]

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

[0057]

[0058] The load-bearing capacity of the tested highway is calculated as follows:

[0059] That is, 100.3 MPa;

[0060] The data from the humidity detection unit is calculated as follows:

[0061] The dielectric constant of soil has a non-linear relationship with its volumetric water content (VWC), which is calculated using the following formula:

[0062] ∈ r =∈ r，dry +(VWC×K);

[0063] Where, ∈ r It is the dielectric constant of the soil, which was measured to be 4. r，dry This is the dielectric constant of dry soil, taken as the standard value of 3. K is a constant related to soil type, usually between 5 and 10, taken as 10, which can be used to infer the moisture content:

[0064] 4 = 3 + VWC × 10, which gives VWC = 0.1, meaning the moisture content of the highway surface is 10% at this point.

[0065] The data from the cold detection unit are calculated as follows:

[0066] The formula for calculating concrete strength fc is:

[0067]

[0068] After the information from the detection module is transmitted to the back-end terminal, the terminal processor in the back-end terminal transmits it to the equipment control module according to preset information, which is the national standard for highway pavement. The following conclusions are all drawn after comparing with the national standard, as detailed below:

[0069] According to national standards, the qualified road bearing capacity should generally be above 80MPa in a dry environment. The calculated road bearing capacity under this condition is 100.3MPa. Therefore, the road surface is in good condition and can withstand the designed vehicle load. The conclusion "can be used normally and does not require immediate maintenance" is fed back to the back-end terminal.

[0070] In humid environments, a moisture content of 10% generally does not have a serious impact on the road surface's load-bearing capacity. However, it is necessary to be aware of the potential risk of moisture accumulation. The conclusion "No significant voids or cracks were found, but regular monitoring is required, especially during periods of high rainfall" should be fed back to the back-end terminal.

[0071] Under cold or extreme weather conditions, the concrete strength is approximately 0.667 MPa, which is lower than the minimum requirement for concrete under normal conditions (generally above 20 MPa). Insufficient strength may lead to cracking and subsidence of the road surface when heavy vehicles are driving, posing a safety hazard. This suggests that the concrete may have freeze-thaw damage or other structural damage. The conclusion, "Immediately conduct further structural testing, and reinforce or repave if necessary. At the same time, ensure that preventive measures are taken under cold conditions, such as appropriate snow removal and antifreeze treatment," is fed back to the back-end terminal.

[0072] Meanwhile, the back-end terminal can generate report form samples using traditional report generation functions and record and store them on the local hard drive:

[0073] environment Calculated data suggestion dry environment Load capacity 100.3MP Normal use, no maintenance required. humid environment Moisture content 10% Use appropriately, monitoring is recommended. cold environment Concrete strength 0.667 MPa Reinforcement is recommended.

[0074] The procedures for guiding subsequent testing include

[0075] The prediction module uses finite element analysis software such as ANSYS to build road surface and structure models based on the data collected by the detection module. It simulates the deformation of highways under different loads and environmental conditions and predicts the performance of highway pavement and structures under specific climate changes or traffic loads.

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

[0077] The data acquisition unit cleans, fuses, and analyzes the data collected, identifying key features. Data cleaning removes outliers and noise; data fusion integrates data from multiple detection devices into a unified database; and analysis identifies key features to ensure high-quality data support for subsequent simulation calculations. For example:

[0078] The road surface load data after cleaning is 98kN, the temperature data after treatment is 24.5℃, and the average road surface pressure strain is 198p m.

[0079] Based on the data processing and analysis unit, finite element analysis (FEA) is performed to simulate the deformation of highway pavement and structures under various conditions. The calculation content includes load influence (simulating the stress and deformation influence of different traffic loads on the pavement) and temperature deformation (simulating the expansion or contraction behavior of the pavement structure due to temperature changes), and the deformation results are obtained (e.g., the maximum stress is 1.5MPa and the maximum deformation is 0.003m).

[0080] A visualization unit (suitable for backend terminals) that presents the simulation results and real-time monitoring data output by the simulation calculation unit in a graphical form. The output content can be a schematic diagram of 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 be integrated with existing structural health monitoring (SHM) systems for systematic highway monitoring and management, and combined with the Internet of Things (IoT) to achieve more convenient management functions.

[0082] The hardware used in the above modules includes: a patrol vehicle equipped with a central processing unit that integrates an equipment control module, a computing module, and a prediction module; ground-penetrating radar, an acoustic detector, and a falling-weight pavement bearing capacity tester for detecting different environments on highways; a dynamic pavement performance tester and a drone for detecting different performance characteristics of highways; and a wireless transceiver module for data transmission.

[0083] It should be noted that visual inspection is achieved by using unmanned aerial vehicles (UAVs) and high-resolution cameras. UAVs are used to take high-resolution pictures of highway surfaces to identify surface cracks and other visible defects. This is existing technology and will not be elaborated on further.

[0084] In this embodiment, the detection parameters for the aforementioned device are as follows:

[0085] The dry testing unit uses a falling weight pavement bearing capacity tester at a frequency of 100 meters for static load testing, and performs 3 repeated tests with the falling weight as the standard, taking the average value. The ground-penetrating radar (GPR) is used at a frequency of 200 meters for scanning.

[0086] The moisture detection unit uses a ground-penetrating radar with a detection frequency of 1.5 GHz, performing one ground-penetrating radar scan every 100 meters; and uses an acoustic detector with a detection frequency of one acoustic detection every 200 meters.

[0087] The monitoring frequency of the cold detection unit using ground-penetrating radar is: one ground-penetrating radar scan every 50 meters; the monitoring frequency of the falling weight pavement bearing capacity tester is: one falling weight pavement bearing capacity tester scan every 100 meters.

[0088] In addition, the equipment control module has an adaptive dynamic adjustment mechanism that can automatically select and adjust the detection equipment and parameters based on real-time data. The prediction module also has a feedback learning mechanism that uses an incremental learning algorithm based on real-time monitoring data and historical data. After each detection, the prediction module adjusts its internal model in real time according to the results. 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 logic is as follows:

[0089] The adaptive dynamic adjustment mechanism includes dynamic parameter adjustment, whereby each detection device sets initial detection parameters (such as frequency and sensitivity) and continuously adjusts these parameters based on real-time monitoring data (such as temperature, humidity, and road surface condition). For example, when humidity is detected to increase 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 the use of incremental learning algorithms, where the prediction module adjusts its internal model based on the results after each detection. For example, if historical data indicates that the road surface bearing capacity decreases under certain humidity conditions, the system will use this information to update the detection strategy for similar conditions in the future, as detailed below:

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

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

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

[0094] Integrate the data into a dataset:

[0095] time humidity(%) Temperature (°C) Rainfall (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 linear or multiple regression models to establish mathematical models relating environmental factors such as humidity and temperature to the coefficient of friction.

[0101] Coefficient of friction = a × humidity + b × temperature + c;

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

[0103] P3, Time Series Analysis:

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

[0105] The results of the analysis are as follows:

[0106] Correlation results:

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

[0108] Model results:

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

[0110] Coefficient of friction = -0.002 × humidity + 0.01 × temperature + 0.25;

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

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

[0113] When the humidity exceeds a certain threshold (e.g., 80%), the frequency of friction coefficient detection is automatically increased (i.e., the instruction is sent to the central processor at the patrol vehicle to remind it to increase the detection frequency of the drop hammer pavement bearing capacity tester) to ensure timely understanding of the pavement condition. It should be noted that the above data is a hypothetical situation. In reality, there will also be situations where the temperature exceeds the preset threshold, the pavement deformation detection frequency is automatically increased, and when the precipitation exceeds the threshold, the crack detection frequency is automatically increased, etc.

[0114] Its function is that, as the usage time increases and the dataset becomes more and more extensive, the thresholds in the strategy become more and more accurate, thereby enabling the function of automatically adjusting the detection frequency based on real-time data.

[0115] It should be noted that the structure described in this invention can be implemented in many different forms and is not limited to the embodiments described. Any equivalent transformations made by those skilled in the art based on the content of this specification, or direct or indirect applications in other related technical fields, such as the loading and unloading of other items, are included within the protection scope of this invention.

Claims

1. A system for detecting the load capacity of a highway, characterized in that, The application relates to a highway load-bearing capacity testing system. The system comprises: a device control module for controlling different load-bearing capacity testing devices and automatically selecting and adjusting the testing devices and parameters according to real-time data provided by a detection module through an integrated adaptive dynamic adjustment mechanism; a detection module comprising an environment detection module and a performance testing module, the environment detection module being used for detecting the current environment of the highway and conveying corresponding environment information to the device control module, the environment detection module comprising a dry detection unit, a humid detection unit and a cold detection unit corresponding to three different environment conditions respectively, and the performance testing module comprising a road surface performance testing unit, a structure testing unit and a material performance testing unit; a calculation module for calculating the load-bearing capacity detected by the testing devices, and feeding the calculation results back to the device control module; a transmission module for conveying the information of the detection module to a background terminal, and then feeding the information to the device control module according to preset information by a terminal processor in the background terminal; 2. The highway load capacity detection system of claim 1, wherein: a prediction module for establishing a road surface and structure model based on the data information collected by the detection module through finite element analysis software, and real-time optimizing the prediction model based on real-time monitoring data and historical data through an integrated feedback learning mechanism, simulating the deformation of the highway under different loads and environment conditions, and predicting the performance of the highway road surface and structure under specific climate change or traffic load. The environment condition triggered by the dry detection unit is: temperature > 15 DEG C, humidity < 60%, static load testing is carried out by using a drop hammer type road surface load-bearing capacity tester, and road surface layer thickness and structure testing are carried out by using a geological radar; The environment condition triggered by the humid detection unit is: humidity >= 60% or precipitation weather, and the strength of the internal structure of the highway is detected by using a sound detector after scanning and analyzing by using a geological radar; 3. The highway load capacity detection system of claim 2, wherein: The environment condition triggered by the cold detection unit is: temperature < 0 DEG C or ice and snow weather, and the geological radar is used for scanning detection in the environment covered with snow, and the drop hammer type road surface load-bearing capacity tester is used for static load testing in the environment without snow cover. The detection frequency of the drop hammer type road surface load-bearing capacity tester used by the dry detection unit is: static load testing is carried out once every 100 meters, the drop hammer weight is set as a standard to carry out repeated testing for three times, and the average value is taken; The detection frequency of the geological radar used by the dry detection unit is:

4. The highway load capacity detection system of claim 2, wherein: geological radar scanning is carried out once every 200 meters. The detection frequency of the geological radar used by the humid detection unit is: geological radar scanning is carried out once every 100 meters, and 1.5GHz high-frequency scanning is adopted; The detection frequency of the sound wave detector used by the humid detection unit is:

5. The highway load capacity detection system of claim 2, wherein: sound wave detection is carried out once every 200 meters. The monitoring frequency of the geological radar used by the cold detection unit is: geological radar scanning is carried out once every 50 meters; The monitoring frequency of the drop hammer type road surface load-bearing capacity tester used by the cold detection unit is:

6. The highway load capacity detection system of claim 1, wherein: drop hammer type road surface load-bearing capacity tester scanning is carried out once every 100 meters. The road surface performance testing unit is used for testing the friction coefficient of the road surface under different humidity conditions and measuring the flatness of the road surface; The structure testing unit is used for visually detecting road surface cracks; The material performance test unit is used for testing the weather resistance and durability of the test material under different temperature and humidity conditions.

7. The highway load capacity detection system of claim 1, wherein: The prediction module comprises a data acquisition unit for acquiring various types of data detected by the detection module from the road surface, structures and environmental monitoring; A data processing and analysis unit for cleaning, fusing and analyzing the data collected by the data acquisition unit, and identifying key features; A simulation calculation unit for finite element analysis based on the data of the data processing and analysis unit, simulating the deformation of the highway pavement and structures under various conditions; A visualization unit for presenting the simulation results output by the simulation calculation unit and the real-time monitoring data in a graphical form.

8. The device for detecting the load capacity of a highway according to any one of claims 1 to 7, characterized in that: The patrol vehicle comprises a central processor integrated with a device control module, a calculation module and a prediction module, a geological radar, a sound wave detector and a falling weight type road bearing capacity tester for detecting different environments of the highway, a dynamic road performance tester and an unmanned aerial vehicle for detecting different performances of the highway, and a wireless transceiver module for data transmission.

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