Steel bridge deck pavement interface hidden water damage identification method based on temperature field

Abstract

The invention discloses a steel bridge deck pavement interface hidden water damage identification method based on a temperature field, and the method comprises the steps: obtaining the actual measurement temperature data of a steel bridge deck pavement interface through a vehicle-mounted infrared thermal imaging device, scanning a large-area bridge deck in a short time, comprehensively obtaining the surface temperature distribution, covering the whole bridge deck, and obtaining the hidden water damage of the steel bridge deck pavement interface. The method can comprehensively assess whether the whole bridge floor has hidden water damage potential risks, overcomes the limitation that drilling coring and other damage detection can only obtain local information, meanwhile, the established temperature field finite element model can simulate thermal response rules under different disease sizes, filling media and environment conditions, the simulation result and actually measured data are combined for comparative analysis, and the accuracy of detection results is improved. According to the method, the bridge floor condition can be evaluated from multiple angles, the one-sidedness of single measured data is avoided, in addition, the model provides theoretical reference for measured data analysis, the hidden water damage can be more scientifically and accurately recognized from temperature data based on hidden water damage recognition criteria formulated by the model, and misjudgment and missed judgment are effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of hidden water damage identification technology, and in particular to a method for identifying hidden water damage at the interface of steel bridge deck pavement based on temperature field. Background Technology

[0002] As a key component of bridge engineering, the performance of steel bridge deck pavement directly affects the service life and traffic safety of the bridge. In actual use, the steel bridge deck pavement interface may suffer from hidden water damage. Because this damage is hidden inside the structure, it is often difficult to detect through routine visual inspection in the early stages. As the damage gradually develops, it will lead to a decrease in the bonding performance between the pavement layer and the steel plate, which will cause pavement peeling, potholes and other defects, seriously affecting the normal use of the bridge.

[0003] Currently, the main methods for detecting water damage at the interface of steel bridge deck pavement include non-destructive testing and destructive testing. Destructive testing methods, such as core drilling, can directly observe the internal structure, but they can damage the bridge deck pavement structure, affecting the normal use of the bridge. Furthermore, the detection range is limited and it is difficult to fully reflect the condition of the entire bridge deck.

[0004] Among non-destructive testing methods, commonly used ones include ultrasonic testing and radar testing. Ultrasonic testing determines whether there are defects inside the structure by analyzing the propagation characteristics of ultrasonic waves in the pavement layer. However, for the detection of hidden water damage, its signal is easily affected by factors such as the properties of the pavement layer material and construction process, resulting in low accuracy of the test results. Radar testing uses the reflection characteristics of electromagnetic waves to detect the internal condition of the structure. However, the metal material of the steel bridge deck will produce strong reflection and interference of electromagnetic waves, making the detection signal complex and difficult to accurately identify hidden water damage. Summary of the Invention

[0005] In view of this, the present invention proposes a method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, which can effectively solve the shortcomings of the existing technology that it is difficult to fully reflect the condition of the entire bridge deck and to accurately identify latent water damage.

[0006] The technical solution of this invention is implemented as follows:

[0007] A method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field includes:

[0008] A finite element model of the temperature field of the steel bridge deck pavement interface with hidden water damage was established. The temperature field finite element model was used to simulate the thermal response under different damage sizes, filling media and environmental conditions.

[0009] Measured temperature data of the steel bridge deck pavement interface were obtained using vehicle-mounted infrared thermal imaging equipment.

[0010] The simulation results of the finite element model of the temperature field are compared and analyzed with the measured temperature data;

[0011] Based on the comparative analysis results, the identification results of hidden water damage at the steel bridge deck pavement interface were obtained using the preset hidden water damage identification criteria.

[0012] As a further optional scheme of the method for identifying latent water damage at the steel bridge deck pavement interface based on temperature field, the establishment of a temperature field finite element model of latent water damage at the steel bridge deck pavement interface specifically includes:

[0013] Analyze the actual structure of the steel bridge deck pavement interface and construct a geometric model of the steel bridge deck pavement interface;

[0014] Define the material properties of different components of the geometric model to obtain a geometric model with material properties;

[0015] Based on the actual environmental conditions of the steel bridge deck pavement interface, the thermal boundary conditions of the geometric model with material properties are set to obtain the geometric model with thermal boundary conditions.

[0016] The geometric model with thermal boundary conditions is meshed to obtain the meshed geometric model.

[0017] The geometric model after meshing was solved to obtain the thermal response characteristics under different disease sizes, filling media and environmental conditions.

[0018] As a further alternative to the method for identifying latent water damage at the steel bridge deck pavement interface based on temperature field, the thermal boundary conditions include ambient temperature, solar radiation intensity, and convective heat transfer coefficient.

[0019] As a further optional solution to the method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, the step of comparing and analyzing the simulation results of the temperature field finite element model with the measured temperature data specifically includes:

[0020] Temperature measurement points are selected at the steel bridge deck pavement interface that correspond one-to-one with the node positions in the temperature field finite element model.

[0021] Extract the simulated temperature value and the measured temperature value corresponding to each temperature measurement point;

[0022] Calculate the absolute temperature deviation at each temperature measurement point;

[0023] Calculate the average, maximum, and standard deviation of the absolute temperature deviation at all temperature measurement points;

[0024] Based on the average, maximum, and standard deviation of the absolute temperature deviations at all temperature measurement points, the approximation and dispersion of the simulation results and the measured data in terms of temperature values ​​are evaluated.

[0025] As a further optional scheme of the method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, the average value of the absolute temperature deviation of all temperature measurement points is specifically calculated using the following formula:

[0026] ;

[0027] in, This is expressed as the average of the absolute temperature deviations at all temperature measurement points. This is expressed as the absolute temperature deviation between the simulated temperature value and the measured temperature value. This represents the total number of temperature measurement points.

[0028] As a further optional scheme of the method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, the standard deviation of the absolute temperature deviation of all temperature measurement points is specifically calculated using the following formula:

[0029] ;

[0030] in, This is expressed as the standard deviation of the absolute temperature deviation at all temperature measurement points. This is expressed as the absolute temperature deviation between the simulated temperature value and the measured temperature value. This is expressed as the average of the absolute temperature deviations at all temperature measurement points. This represents the total number of temperature measurement points.

[0031] As a further optional scheme of the method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, the evaluation of the closeness and dispersion of the simulation results and measured data in terms of temperature values ​​based on the average, maximum and standard deviation of the absolute temperature deviations of all temperature measurement points specifically includes:

[0032] If the average value is less than the preset first threshold, the simulation result is determined to be highly similar to the measured data.

[0033] If the maximum value is less than the preset second threshold, the simulation result is determined to have no extreme error.

[0034] If the standard deviation is less than the preset third threshold, the simulation results are judged to have a small degree of dispersion from the measured data.

[0035] A system for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, comprising:

[0036] The temperature field finite element model construction module is used to establish a temperature field finite element model of hidden water damage at the steel bridge deck pavement interface. The temperature field finite element model is used to simulate the thermal response under different damage sizes, filling media and environmental conditions.

[0037] The measured temperature data acquisition module is used to acquire measured temperature data of the steel bridge deck pavement interface using vehicle-mounted infrared thermal imaging equipment.

[0038] The comparative analysis module is used to compare and analyze the simulation results of the temperature field finite element model with the measured temperature data.

[0039] The identification result generation module is used to obtain the identification results of hidden water damage at the steel bridge deck pavement interface based on the comparative analysis results and using the preset hidden water damage identification criteria.

[0040] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for identifying latent water damage at the interface of steel bridge deck pavement based on a temperature field.

[0041] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for identifying latent water damage at the interface of steel bridge deck pavement based on a temperature field.

[0042] The beneficial effects of this invention are as follows: By utilizing vehicle-mounted infrared thermal imaging equipment to acquire measured temperature data of the steel bridge deck pavement interface, a large area of ​​the bridge deck can be scanned in a short time to obtain temperature distribution information of the entire bridge deck surface. Compared with destructive testing methods such as core drilling, which can only obtain information from local points, this method can cover the entire bridge deck, comprehensively reflecting the temperature conditions at different locations on the bridge deck. This allows for a more comprehensive assessment of the potential risk of hidden water damage to the entire bridge deck. Simultaneously, the established temperature field finite element model can simulate the thermal response under different defect sizes, filling media, and environmental conditions. By combining the simulation results with measured data for comparative analysis, not only is the actual temperature condition of the bridge deck considered, but the model also expands the scope of analysis for various possible defects. This comprehensive analysis approach allows for the assessment of bridge deck conditions from multiple angles and levels, avoiding the limitations that can arise from single measured data and providing a more complete understanding of the potential distribution and severity of latent water damage. Furthermore, the finite element model of the temperature field simulates the thermal response patterns under different damage conditions, providing a theoretical reference for the analysis of measured temperature data. When comparing the simulation results with measured data, the thermal response characteristics corresponding to different damage conditions in the model can be used to determine whether there are temperature anomalies related to latent water damage in the measured data. The preset latent water damage identification criteria are based on the model simulation results, enabling more scientific and accurate identification of latent water damage from temperature data, reducing the possibility of misjudgment and omission. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a method for identifying hidden water damage at the interface of steel bridge deck pavement based on temperature field, according to the present invention.

[0045] Figure 2 This is a schematic diagram of the components of a temperature field-based system for identifying latent water damage at the interface of steel bridge deck pavement according to the present invention.

[0046] Figure 3 This is a schematic diagram of the composition of a computing device according to the present invention. Detailed Implementation

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] refer to Figures 1 to 3 A method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, comprising:

[0049] A finite element model of the temperature field of latent water damage at the steel bridge deck pavement interface is established. This model is used to simulate the thermal response under different damage sizes, filling media, and environmental conditions. In some embodiments, establishing this finite element model specifically includes:

[0050] Analyze the actual structure of the steel bridge deck pavement interface and accurately construct its geometric model. This model needs to comprehensively cover the steel bridge deck, pavement layer, and potential hidden water damage areas, accurately reflecting the geometric shape, size, and relative positional relationship of each part. For example, it should specify the thickness and width of the steel bridge deck, the number of pavement layers, the thickness of each layer, and the specific location and approximate shape (such as circular, rectangular, etc.) of the hidden water damage areas in the pavement layer.

[0051] For each component in the geometric model, its material properties are defined. For the steel bridge deck, its thermal conductivity, specific heat capacity, density, and other thermal properties need to be determined, as these parameters vary with the type of steel and temperature. Similarly, for the pavement material, its thermal properties must be defined, taking into account the differences between different pavement materials (such as asphalt concrete and epoxy asphalt). For the medium filling areas with hidden water damage, its thermal properties, such as the thermal conductivity and specific heat capacity of water, are determined according to the actual situation. By defining these material properties, the geometric model acquires realistic physical characteristics, resulting in a geometric model with material properties.

[0052] Based on the actual environmental conditions of the steel bridge deck, reasonable thermal boundary conditions are set for the geometric model with material properties. These include ambient temperature, which needs to consider the temperature variation range of different seasons and day and night; solar radiation intensity, which is determined according to the geographical location, season and time of the steel bridge; and convective heat transfer coefficient, which considers the convective heat transfer between the air and the steel bridge deck pavement interface, and its magnitude is related to factors such as wind speed and air temperature. By setting these thermal boundary conditions, the heat exchange process of the steel bridge deck in the actual environment is simulated, and a geometric model with thermal boundary conditions is obtained.

[0053] For geometric models with thermal boundary conditions, a scientific and reasonable mesh generation is necessary. The quality of the mesh generation directly affects the accuracy and efficiency of the calculation results. When generating the mesh, it is necessary to select an appropriate mesh type (such as quadrilateral mesh, triangular mesh, etc.) and mesh density based on the geometric shape and stress characteristics of the model. For critical parts such as areas with hidden water damage and the contact area between the steel bridge deck and the pavement layer, a denser mesh should be used to improve the calculation accuracy. For other non-critical parts, a relatively sparse mesh can be used to reduce the amount of calculation. Through mesh generation, the continuous geometric model is discretized into a finite number of elements, resulting in the meshed geometric model.

[0054] Finite element analysis software was used to solve the geometric model after meshing. During the calculation, the variations in different defect sizes (such as the diameter and length of the hidden water damage area), filling media (such as water, air, and other impurities), and environmental conditions (such as ambient temperature, solar radiation intensity, and convective heat transfer coefficient) were considered. Through multiple simulation calculations, the thermal response characteristics of the steel bridge deck pavement interface under different working conditions were obtained, including temperature distribution and heat flux density distribution.

[0055] Specifically, by analyzing the actual structure of the steel bridge deck pavement interface and constructing a geometric model, the complex structure of the steel bridge deck pavement interface can be accurately reproduced, including the shape, size and relative position of each layer of material. This provides an accurate structural basis for subsequent temperature field simulation, making the simulation results closer to the actual situation and helping to more realistically reflect the impact of hidden water damage on the temperature field.

[0056] Define the material properties of different components of the geometric model to obtain a geometric model with material properties. Different materials have different thermal properties such as thermal conductivity and heat capacity. Accurately assigning material properties can ensure that the model can take into account the differences in material characteristics when simulating thermal response, thereby improving the accuracy and reliability of the simulation.

[0057] Mesh the geometric model with thermal boundary conditions. A reasonable mesh can discretize the continuous geometric model into a finite number of elements, which facilitates numerical calculation. By optimizing the mesh, we can improve computational efficiency and reduce computation time and resource consumption while ensuring computational accuracy. At the same time, an appropriate mesh density can better capture the details of temperature field changes and improve the accuracy of simulation results.

[0058] Solving the geometric model after mesh generation allows us to obtain the thermal response characteristics under different damage sizes, filling media, and environmental conditions. This multi-condition simulation capability enables the model to comprehensively cover all possible situations, providing rich data support for analyzing the performance of latent water damage under different conditions. Through simulation and analysis of various conditions, we can gain a deeper understanding of the relationship between latent water damage and the temperature field, thereby improving the accuracy and reliability of latent water damage identification.

[0059] Measured temperature data of the steel bridge deck pavement interface were obtained using vehicle-mounted infrared thermal imaging equipment.

[0060] Specifically, select a suitable vehicle-mounted infrared thermal imaging device to ensure it has sufficient temperature measurement accuracy and resolution, plan a reasonable temperature measurement route on the steel bridge deck to cover areas that may be susceptible to hidden water damage, and use the vehicle-mounted infrared thermal imaging device to measure the temperature of the steel bridge deck pavement interface under specific environmental conditions according to the planned temperature measurement route to obtain measured temperature data. Preprocess the obtained measured temperature data, including noise removal and error correction, to improve data quality.

[0061] The simulation results of the finite element model of the temperature field are compared and analyzed with the measured temperature data, specifically including:

[0062] On the steel bridge deck pavement interface, based on the node distribution of the temperature field finite element model, temperature measurement points that correspond one-to-one with the node positions in the model are precisely selected to ensure that each temperature measurement point can accurately represent the actual position of the corresponding node in the model, so as to ensure a high degree of spatial matching between the simulated data and the measured data.

[0063] From the calculation results of the temperature field finite element model, the simulated temperature value corresponding to each temperature measurement point is extracted; at the same time, the measured temperature value of each temperature measurement point at the same moment is obtained from the actual measuring equipment (such as temperature sensor) to ensure that the simulated temperature value and the measured temperature value are strictly corresponding in time, and to avoid deviations in the comparison results due to time differences.

[0064] Calculate the absolute temperature deviation at each temperature measurement point;

[0065] Calculate the average, maximum, and standard deviation of the absolute temperature deviation at all temperature measurement points;

[0066] Based on the average, maximum, and standard deviation of the absolute temperature deviations at all temperature measurement points, the approximation and dispersion of the simulation results and the measured data in terms of temperature values ​​are evaluated.

[0067] Specifically, on the steel bridge deck pavement interface, temperature measurement points were precisely selected based on the node distribution of the temperature field finite element model, ensuring a high degree of spatial matching between simulated and measured data. This measure enabled the comparative analysis to be based on accurate spatial correspondence, avoiding data errors caused by deviations in the position of temperature measurement points. Simulated and measured temperature values ​​at the same moment were obtained from the model calculation results and actual measuring equipment, respectively, ensuring a strict temporal correspondence between the two. Time factors have a significant impact on temperature changes, and strict temporal correspondence can effectively avoid temperature fluctuation interference caused by time differences, making the comparative analysis more reflective of the real situation.

[0068] By assessing the similarity and dispersion of temperature values ​​between simulation results and measured data, the reliability of the finite element model of the temperature field can be accurately determined. Only when the simulation results of the model have a high degree of consistency with the measured data can the accuracy of the analysis of hidden water damage based on the model be ensured. Accurate and reliable comparative analysis results provide a solid scientific basis for formulating criteria for identifying hidden water damage. By analyzing the differences between simulation and measured data, we can better understand the influence of hidden water damage on the temperature field, thereby formulating more scientific and reasonable identification criteria and improving the accuracy and efficiency of identifying hidden water damage.

[0069] In some embodiments, the average absolute temperature deviation of all temperature measurement points is specifically calculated using the following formula:

[0070] ;

[0071] in, This is expressed as the average of the absolute temperature deviations at all temperature measurement points. This is expressed as the absolute temperature deviation between the simulated temperature value and the measured temperature value. This represents the total number of temperature measurement points.

[0072] Specifically, by calculating the average absolute temperature deviation of all temperature measurement points, the overall difference between the simulated and measured temperature values ​​was quantified. This quantification method allows researchers to intuitively understand the accuracy of the finite element model in simulating the temperature of the steel bridge deck pavement interface. For example, a smaller ΔT value indicates that the model can simulate the actual temperature situation well overall; conversely, a larger ΔT value indicates a significant deviation between the model and the actual situation, requiring adjustment and optimization. In the process of identifying hidden water damage, this average value can serve as an important reference indicator. If the model is accurate, the average temperature deviation between the simulated and measured values ​​will be smaller, and the results of identifying hidden water damage based on this will be more reliable.

[0073] In some embodiments, the standard deviation of the absolute temperature deviation of all temperature measurement points is specifically calculated using the following formula:

[0074] ;

[0075] in, This is expressed as the standard deviation of the absolute temperature deviation at all temperature measurement points. This is expressed as the absolute temperature deviation between the simulated temperature value and the measured temperature value. This is expressed as the average of the absolute temperature deviations at all temperature measurement points. This represents the total number of temperature measurement points.

[0076] Specifically, by calculating the square root of the mean of the sum of squares of the absolute temperature deviation and the average deviation at each temperature measurement point, the dispersion of the absolute temperature deviation at all temperature measurement points is accurately measured. This can intuitively show the fluctuation of the difference between the simulated temperature value and the measured temperature value. If the standard deviation is small, it indicates that the absolute temperature deviation at each temperature measurement point is relatively concentrated, the simulation results of the model are relatively stable, and the simulation accuracy at different locations is relatively consistent. Conversely, if the standard deviation is large, it indicates that the simulation results vary greatly between different temperature measurement points, and the stability of the model is poor. The degree of dispersion is directly related to the reliability of the temperature field finite element model. Stable simulation results mean that the model can more reliably reflect the temperature distribution of the steel bridge deck pavement interface and reduce the risk of misjudgment caused by fluctuations in simulation results.

[0077] Analyzing the magnitude and distribution of standard deviation can help researchers pinpoint problems in finite element models of temperature fields. For example, if the absolute temperature deviation of temperature measurement points in certain regions is highly discrete, it may indicate that the model settings in those regions are unreasonable, such as the need for further optimization in material properties, boundary conditions, or mesh generation. Standard deviation provides guidance for adjusting model parameters. Researchers can adjust model parameters, such as the thermal conductivity of materials and the set values ​​of thermal boundary conditions, based on the analysis results of standard deviation, to reduce the dispersion between simulation results and measured data, thereby improving the accuracy and stability of the model.

[0078] In some embodiments, evaluating the closeness and dispersion of the simulation results and measured data in terms of temperature values ​​based on the average, maximum, and standard deviation of the absolute temperature deviations at all temperature measurement points specifically includes:

[0079] If the average value is less than the preset first threshold, the simulation result is determined to be highly similar to the measured data.

[0080] If the maximum value is less than the preset second threshold, the simulation result is determined to have no extreme error.

[0081] If the standard deviation is less than the preset third threshold, the simulation results are judged to have a small degree of dispersion from the measured data.

[0082] Specifically, using preset thresholds as the judgment criteria, the evaluation results are presented in a quantitative manner, such as comparing the average value with the first threshold, the maximum value with the second threshold, and the standard deviation with the third threshold, so that the evaluation results have clear numerical boundaries, making it easier for researchers to intuitively understand the degree of agreement between the simulation results and the measured data.

[0083] Accurately assessing the reliability of simulation results is the foundation for identifying hidden water damage. When the simulation results and measured data have a high degree of similarity in temperature values ​​and a small degree of dispersion, the hidden water damage identification criteria and damage judgments established based on the simulation results are more credible. This assessment scheme indirectly improves the accuracy of hidden water damage identification by ensuring the accuracy of simulation results, and reduces the possibility of misjudgment and omission.

[0084] The standard deviation reflects the degree of dispersion of the simulation results and is closely related to the stability of the identification of hidden water damage. A smaller standard deviation means that the simulation results are less different between different temperature measurement points, and the model has better stability. In the process of identifying hidden water damage, stable simulation results can ensure the consistency and reliability of the identification criteria, so that the identification results are not affected by individual abnormal data, thus improving the stability of the identification.

[0085] A system for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, comprising:

[0086] The temperature field finite element model construction module is used to establish a temperature field finite element model of hidden water damage at the steel bridge deck pavement interface. The temperature field finite element model is used to simulate the thermal response under different damage sizes, filling media and environmental conditions.

[0087] The measured temperature data acquisition module is used to acquire measured temperature data of the steel bridge deck pavement interface using vehicle-mounted infrared thermal imaging equipment.

[0088] The comparative analysis module is used to compare and analyze the simulation results of the temperature field finite element model with the measured temperature data.

[0089] The identification result generation module is used to obtain the identification results of hidden water damage at the steel bridge deck pavement interface based on the comparative analysis results and using the preset hidden water damage identification criteria.

[0090] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for identifying latent water damage at the interface of steel bridge deck pavement based on a temperature field.

[0091] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for identifying latent water damage at the interface of steel bridge deck pavement based on a temperature field.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, characterized in that, include: A finite element model of the temperature field of the steel bridge deck pavement interface with hidden water damage was established. The temperature field finite element model was used to simulate the thermal response under different damage sizes, filling media and environmental conditions. Measured temperature data of the steel bridge deck pavement interface were obtained using vehicle-mounted infrared thermal imaging equipment. The simulation results of the finite element model of the temperature field are compared and analyzed with the measured temperature data; Based on the comparative analysis results, the identification results of hidden water damage at the steel bridge deck pavement interface were obtained using the preset hidden water damage identification criteria.

2. The method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field as described in claim 1, characterized in that, The establishment of the temperature field finite element model for the latent water damage at the steel bridge deck pavement interface specifically includes: Analyze the actual structure of the steel bridge deck pavement interface and construct a geometric model of the steel bridge deck pavement interface; Define the material properties of different components of the geometric model to obtain a geometric model with material properties; Based on the actual environmental conditions of the steel bridge deck pavement interface, the thermal boundary conditions of the geometric model with material properties are set to obtain the geometric model with thermal boundary conditions. The geometric model with thermal boundary conditions is meshed to obtain the meshed geometric model. The geometric model after meshing was solved to obtain the thermal response characteristics under different disease sizes, filling media and environmental conditions.

3. The method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field according to claim 2, characterized in that, The thermal boundary conditions include ambient temperature, solar radiation intensity, and convective heat transfer coefficient.

4. The method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field according to claim 1, characterized in that, The comparison and analysis of the simulation results of the temperature field finite element model with the measured temperature data specifically includes: Temperature measurement points are selected at the steel bridge deck pavement interface that correspond one-to-one with the node positions in the temperature field finite element model. Extract the simulated temperature value and the measured temperature value corresponding to each temperature measurement point; Calculate the absolute temperature deviation at each temperature measurement point; Calculate the average, maximum, and standard deviation of the absolute temperature deviation at all temperature measurement points; Based on the average, maximum, and standard deviation of the absolute temperature deviations at all temperature measurement points, the approximation and dispersion of the simulation results and the measured data in terms of temperature values ​​are evaluated.

5. The method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field according to claim 4, characterized in that, The average absolute temperature deviation of all temperature measurement points is calculated using the following formula: ； in, This is expressed as the average of the absolute temperature deviations at all temperature measurement points. This is expressed as the absolute temperature deviation between the simulated temperature value and the measured temperature value. This represents the total number of temperature measurement points.

6. The method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field according to claim 4, characterized in that, The standard deviation of the absolute temperature deviation at all temperature measurement points is calculated using the following formula: ； in, This is expressed as the standard deviation of the absolute temperature deviation at all temperature measurement points. This is expressed as the absolute temperature deviation between the simulated temperature value and the measured temperature value. This is expressed as the average of the absolute temperature deviations at all temperature measurement points. This represents the total number of temperature measurement points.

7. The method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field according to claim 4, characterized in that, The method assesses the degree of closeness and dispersion of the simulation results and the measured data in terms of temperature values, based on the average, maximum, and standard deviation of the absolute temperature deviations at all temperature measurement points. Specifically, this includes: If the average value is less than the preset first threshold, the simulation result is determined to be highly similar to the measured data. If the maximum value is less than the preset second threshold, the simulation result is determined to have no extreme error. If the standard deviation is less than the preset third threshold, the simulation results are judged to have a small degree of dispersion from the measured data.

8. A system for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field, characterized in that, include: The temperature field finite element model construction module is used to establish a temperature field finite element model of hidden water damage at the steel bridge deck pavement interface. The temperature field finite element model is used to simulate the thermal response under different damage sizes, filling media and environmental conditions. The measured temperature data acquisition module is used to acquire measured temperature data of the steel bridge deck pavement interface using vehicle-mounted infrared thermal imaging equipment. The comparative analysis module is used to compare and analyze the simulation results of the temperature field finite element model with the measured temperature data. The identification result generation module is used to obtain the identification results of hidden water damage at the steel bridge deck pavement interface based on the comparative analysis results and using the preset hidden water damage identification criteria.

9. A computing device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for identifying latent water damage at the interface of steel bridge deck pavement based on temperature field as described in any one of claims 1-7.

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