Pavement base surface interlayer bonding state evaluation method, device, equipment and medium

Abstract

The invention discloses a pavement base surface interlayer bonding state evaluation method, device, equipment and medium, and relates to the field of road engineering, the method comprises the following steps: obtaining pavement structure data, deflection basin data, actual interlayer mechanical response data and environmental data of each structure layer of a pavement to be evaluated; performing modulus inversion based on the deflection basin data and the environment data to obtain a target modulus value of each structural layer; performing iterative optimization processing on the initial interlayer adhesion coefficient value according to the target modulus value, the actual interlayer mechanical response data, the pavement structure data and the environment data to obtain a target interlayer adhesion coefficient value; and analyzing the target interlayer bonding coefficient value to obtain a base plane interlayer bonding state evaluation result. According to the method, the evaluation accuracy of the bonding state between the base layers is remarkably improved, and a reliable basis is provided for scientific selection of a permeable sealing layer material and optimization of a construction process.

Description

Technical Field

[0001] This application relates to the field of road engineering technology, and in particular to a method, apparatus, equipment and medium for evaluating the interlayer bonding state of pavement base. Background Technology

[0002] With the rapid development of the road transportation industry, pavement structure, as the core infrastructure of the transportation system, directly determines traffic efficiency, driving safety, and maintenance economy through its load-bearing capacity and service performance. The base layer, as a key load-bearing unit of the pavement structure, relies heavily on the interfacial bonding state (usually formed by a tack coat material) between itself and the base layer. This interfacial bonding performance directly affects the overall load-bearing efficiency, stress distribution characteristics, and damage evolution of the pavement structure. When the base layer and base course are well bonded, the surface layer and base course can work together to bear the load, effectively dispersing the stress generated by traffic loads. However, when the bonding deteriorates or delaminates at the interface, it leads to stress concentration within the surface layer, accelerating the initiation and expansion of structural defects such as cracks and rutting, significantly shortening the pavement's service life. Therefore, evaluating the bonding state of the base layer is crucial for achieving non-destructive evaluation of pavement structures, early damage identification, and optimized maintenance decisions.

[0003] Currently, one approach in related technologies is to use a pavement structure evaluation theory based on deflection response. However, this theory generally adopts two extreme assumptions when dealing with the interface contact conditions of the base layer and surface layer: "completely continuous (ideal bonding)" or "completely smooth (complete delamination)". This simplifies the complex interface bonding state to ideal mechanical boundary conditions, ignoring the partial bonding state that exists at the interface of the base layer and surface layer in actual engineering. Another approach is to use indoor testing methods or field testing methods to detect the current bonding state of the base layer and surface layer. However, these methods are essentially destructive testing methods at discrete points, which cannot achieve long-term tracking of the dynamic changes in the bonding state of the base layer and surface layer. This results in poor accuracy in evaluating the bonding state between base layers and surface layers, which in turn makes it impossible to effectively judge the performance and construction process of different tack coat materials and to provide a reliable basis for the scientific selection of tack coat materials and the optimization of construction processes. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment, and medium for evaluating the interlayer bonding state of road surface, in order to solve the technical problem that the evaluation of the interlayer bonding state of the road surface is inaccurate and cannot provide a reliable basis for the scientific selection of tack coat materials and the optimization of construction technology.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for evaluating the interlayer bonding state of a pavement base layer, the method comprising: The process involves acquiring pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data for each structural layer of the pavement to be evaluated. The deflection basin data refers to a set of surface displacement values ​​measured at key locations after a load is applied to the pavement to be evaluated. The actual interlayer mechanical response data reflects the actual stress or strain between the structural layers of the pavement to be evaluated under load. Based on the deflection basin data and the environmental data, modulus inversion is performed to obtain the target modulus values ​​of each structural layer. Based on the target modulus value, the actual interlayer mechanical response data, the pavement structure data, and the environmental data, the initial interlayer bonding coefficient value is iteratively optimized to obtain the target interlayer bonding coefficient value. The interlayer bonding coefficient of the target layer is analyzed to obtain the evaluation result of the interlayer bonding state of the base surface.

[0006] Secondly, this application provides a device for evaluating the interlayer bonding state of a road surface, the device comprising: The acquisition module is used to acquire pavement structure data, deflection basin data, actual interlayer mechanical response data and environmental data of each structural layer of the pavement to be evaluated; the deflection basin data refers to a set of surface displacement values ​​measured at key locations after the load is applied to the pavement to be evaluated, and the actual interlayer mechanical response data reflects the real stress or strain between each structural layer of the pavement under load. The inversion module is used to perform modulus inversion based on the deflection basin data and the environmental data to obtain the target modulus values ​​of each structural layer. The iterative optimization module is used to iteratively optimize the initial interlayer bonding coefficient value based on the target modulus value, the actual interlayer mechanical response data, the pavement structure data, and the environmental data to obtain the target interlayer bonding coefficient value. The result determination module is used to analyze the target interlayer bonding coefficient value and obtain the evaluation result of the interlayer bonding state of the base surface.

[0007] Thirdly, this application provides a computer device, including: 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 the method for evaluating the interlayer bonding state of the pavement base layer as described above.

[0008] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for evaluating the interlayer bonding state of the pavement base layer as described above.

[0009] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, apparatus, equipment, and medium for evaluating the interlayer bonding state of a pavement base surface. The method includes: acquiring pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data for each structural layer of the pavement to be evaluated; the deflection basin data refers to a set of surface displacement values ​​measured at key locations after a load is applied to the pavement to be evaluated; the actual interlayer mechanical response data reflects the actual stress or strain between the structural layers of the pavement to be evaluated under load; performing modulus inversion based on the deflection basin data and environmental data to obtain the target modulus value for each structural layer; iteratively optimizing the initial interlayer bonding coefficient value based on the target modulus value, actual interlayer mechanical response data, pavement structure data, and environmental data to obtain the target interlayer bonding coefficient value; and analyzing the target interlayer bonding coefficient value to obtain the evaluation result of the interlayer bonding state of the base surface.

[0010] Compared with existing technologies, this solution acquires pavement structure data, deflection basin data, actual interlayer mechanical response data reflecting true interlayer stress and strain, and environmental data for each structural layer of the pavement to be evaluated. This allows for comprehensive long-term tracking of the dynamic changes in the interfacial bonding state of the base layer without damaging the pavement or affecting its normal use. It collects multi-dimensional data on structure, mechanical response, and environment, providing complete and accurate data support for subsequent evaluation. Furthermore, it performs modulus inversion based on deflection basin and environmental data, ensuring that the determined target modulus value more closely matches the actual service condition, improving the accuracy of the modulus parameters. Finally, it considers the target modulus value, actual interlayer mechanical response data, and pavement structure data... Based on environmental data, the initial interlayer bond coefficient value is iteratively optimized, abandoning traditional extreme assumptions and utilizing actual interlayer mechanical response data to fully reflect the "partial bond" state. Through multi-data fusion and iterative optimization, the interlayer bond coefficient is accurately quantified, solving the problem that traditional evaluation methods cannot quantify partial bond states. Then, the target interlayer bond coefficient value is analyzed to obtain the evaluation results of the interlayer bond state of the substrate. Evaluation is conducted based on the accurately quantified bond coefficient, replacing traditional discrete destructive testing. This enables the evaluation of the bond state across the entire substrate, significantly improving the accuracy of substrate layer bond state evaluation. This ensures the scientific selection of the bonding layer material and provides a reliable basis for optimizing construction processes. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1This is a structural schematic diagram of the application environment of a method for evaluating the interlayer bonding state of a road surface according to an embodiment of this application; Figure 2 A schematic flowchart illustrating a method for evaluating the interlayer bonding state of a road surface according to an embodiment of this application; Figure 3 A flowchart illustrating a method for obtaining the target modulus values ​​of each structural layer according to an embodiment of this application; Figure 4 A flowchart illustrating a method for evaluating the interlayer bonding state of a road surface according to another embodiment of this application; Figure 5 This is a schematic diagram of the installation location of a strain sensor according to an embodiment of this application; Figure 6 A schematic diagram of the functional modules of a roadbed interlayer bonding state evaluation device provided in this application embodiment; Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] One approach in related technologies is to use a falling weight deflectometer (FWD) to detect deflection basin data of the road surface to evaluate the pavement structure. When dealing with the contact conditions at the base-to-surface interface, this approach adopts a binary extreme assumption of "complete continuity (ideal bonding)" or "complete smoothness (complete delamination)". This assumption simplifies the complex interface state to ideal mechanical boundary conditions, ignoring the "partial bonding" state that widely exists at the base-to-surface interface in actual engineering due to fluctuations in construction quality, material performance degradation, environmental effects, and repeated traffic loads. This leads to a systematic deviation between the theoretically calculated pavement mechanical response and the actual pavement response, making it impossible to quantify the interface shear stress transfer efficiency based on this theory. Consequently, it is impossible to accurately identify the degree of degradation of the bonding state and lose the ability to detect early damage to the pavement structure.

[0016] Another approach involves two categories: indoor testing and field testing, to assess the current bonding state of the base layer. While indoor testing can obtain mechanical parameters such as interfacial bond strength and shear modulus, it suffers from fundamental drawbacks, including significant size effects, lack of service environment data, and destructive sampling. Regarding the significant size effect: indoor specimens are much smaller than actual pavement structures, leading to order-of-magnitude differences in boundary conditions and stress field distribution between the specimens and the actual pavement structure, resulting in test results that fail to reflect the interfacial mechanical behavior under real-world conditions. Regarding the lack of service environment data: it cannot simulate the coupling effects of dynamic traffic loads and complex temperature and humidity fields in the field, meaning test parameters only reflect bonding performance under ideal conditions, which is disconnected from the bonding degradation patterns under actual service conditions. Regarding destructive sampling: it requires core drilling of the actual pavement, directly damaging the base layer interfacial structure and the overall pavement integrity, affecting pavement service performance and making long-term monitoring of the bonding state at the same cross-section and location impossible.

[0017] On-site inspection methods mainly rely on visual observation or simple shear tests after core sampling. However, this approach suffers from severe spatial representativeness issues: core sampling is a discrete point test, and a single core sample can only reflect the bonding state of a small area at the sampling point. The bonding quality of the base layer is significantly spatially variable due to factors such as construction uniformity and material segregation. The limited number of test points cannot characterize the overall bonding quality distribution of the pavement. Furthermore, it lacks dynamic evolution tracking capabilities: core drilling is a destructive operation, and repeated sampling and testing at the same location are not possible, making it impossible to capture the dynamic deterioration process of the base layer bonding state over service time and identify key information such as the onset time and rate of bonding deterioration. Finally, it suffers from low quantification and high subjectivity: visual observation relies on the experience of the testing personnel to judge bonding integrity, which is highly subjective and lacks a unified quantitative evaluation standard, making it impossible to achieve accurate quantitative characterization of bonding performance.

[0018] The methods described above are essentially destructive detection paradigms based on discrete points, which cannot achieve long-term tracking of the dynamic changes in the bonding state of the substrate layers. This results in poor accuracy in evaluating the bonding state between substrate layers, which in turn affects the scientific selection of through-bonding materials.

[0019] To address the aforementioned shortcomings, this application provides a method for evaluating the interlayer bonding state of pavement base layers. Compared with existing technologies, this method acquires pavement structure data, deflection basin data, actual interlayer mechanical response data reflecting true stress and strain, and environmental data for each structural layer of the pavement to be evaluated. This allows for comprehensive long-term tracking of the dynamic changes in the interlayer bonding state without damaging the pavement or affecting its normal use. It collects multi-dimensional data on structure, mechanical response, and environment, providing complete and accurate data support for subsequent evaluations. Furthermore, it performs modulus inversion based on deflection basin and environmental data, ensuring that the determined target modulus value better reflects the actual service condition and improving the accuracy of the modulus parameters. Finally, it uses the target modulus value, actual interlayer mechanical response data, and pavement structure data to... Based on environmental data, the initial interlayer bond coefficient value is iteratively optimized, abandoning traditional extreme assumptions and utilizing actual interlayer mechanical response data to fully reflect the "partial bond" state. Through multi-data fusion and iterative optimization, the interlayer bond coefficient is accurately quantified, solving the problem that traditional evaluation methods cannot quantify partial bond states. Then, the target interlayer bond coefficient value is analyzed to obtain the evaluation results of the interlayer bond state of the substrate. Evaluation is conducted based on the accurately quantified bond coefficient, replacing traditional discrete destructive testing. This enables the evaluation of the bond state across the entire substrate, significantly improving the accuracy of substrate layer bond state evaluation. This ensures the scientific selection of the bonding layer material and provides a reliable basis for optimizing construction processes.

[0020] This application provides a method for evaluating the interlayer bonding state of a road surface, which can be applied to, for example... Figure 1 The application environment of the pavement base layer interlayer bonding state evaluation method is shown. This application environment includes a terminal 102, a server 104, and a data storage system. The terminal 102 communicates with the server 104 via a network. The data storage system can store pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data of each structural layer of the pavement to be evaluated, acquired by the server 104. The data storage system can be set up independently, integrated into the server 104, or placed in the cloud or on another server. The terminal 102 can send the acquired pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data of the structural layers to the server 104. After receiving the pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data of the structural layers, the server 104 analyzes and processes the data to obtain the base layer interlayer bonding state evaluation result. Furthermore, in some embodiments, the pavement base layer interlayer bonding state evaluation method can also be implemented independently by the server 104 or the terminal 102; for example, the terminal 102 can directly perform the analysis to obtain the base layer interlayer bonding state evaluation result.

[0021] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0022] In one exemplary embodiment, such as Figure 2 As shown, a method for evaluating the interlayer bonding state of a road surface is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S201 to S204. Wherein: Step S201: Obtain pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data for each structural layer of the pavement to be evaluated. Deflection basin data refers to a set of surface displacement values ​​measured at key locations after a load is applied to the pavement to be evaluated. Actual interlayer mechanical response data reflects the actual stress or strain between the structural layers of the pavement to be evaluated under load.

[0023] It should be noted that the pavement to be evaluated above is the pavement for which bond condition evaluation is required. The stress transfer path and functional positioning of the pavement structure can usually be divided from top to bottom into surface layer, base course, subgrade, and subbase. The subgrade can also be understood as the subbase course.

[0024] The surface layer is the uppermost layer of the pavement structure, directly in contact with vehicle tires, pedestrians, and the atmosphere. It requires high strength, high wear resistance, good smoothness, and skid resistance, while also resisting environmental factors such as temperature changes and water erosion. Depending on the material type, it can include asphalt-based surface layers, cement concrete surface layers, and granular surface layers. The base layer, located below the surface layer, is the main load-bearing layer of the pavement structure, responsible for distributing the vehicle load from the surface layer to the underlying layers. It needs sufficient compressive strength, stiffness, and stability, as well as good water stability to prevent performance degradation due to water erosion. Based on material stiffness and composition, the base layer can be divided into rigid base layers, semi-rigid base layers, and flexible base layers.

[0025] The subbase is located below the base course and is installed when the base course is thick or the underlying subbase / subgrade has low strength. Its main function is to assist the base course in dispersing loads, reducing the base course thickness requirements, and protecting the subgrade top surface from water erosion and damage from construction machinery. The material requirements for the subbase are lower than those for the base course, typically using lower-cost, slightly lower-strength materials. Common types include semi-rigid subbases, flexible subbases, and granular subbases. The subbase is located below the subbase and above the subgrade, and is only installed under special geological or environmental conditions. Its core function is to address the subgrade's water temperature stability issues, such as drainage, waterproofing, frost protection, and stress diffusion. Based on functional requirements, subbases are mainly divided into three categories: drainage subbases, waterproofing subbases, and frost protection subbases.

[0026] The aforementioned deflection basin data refers to a set of vertical surface displacement (deflection) values ​​simultaneously measured at various key locations after an instantaneous load is applied to the pavement using a falling weight deflectometer (FWD). These values ​​reflect the overall deformation characteristics of the pavement structure under impact loads. These key locations can be the load center and multiple radial offset locations. Actual interlayer mechanical response data refers to the true stress, strain, or displacement transfer state between the pavement structural layers under load, obtained through embedded sensors or numerical simulation. This data characterizes the interlayer contact conditions and mechanical interactions. The aforementioned environmental data can include temperature and humidity data. Temperature has a significant impact on the performance of pavement materials; for example, the modulus of asphalt materials changes considerably at different temperatures. Therefore, accurate recording of temperature data is crucial to consider temperature-related modulus corrections in subsequent analyses.

[0027] Optionally, the pavement structure information, deflection basin data, mechanical response data, and environmental data of the pavement to be evaluated can be obtained from external devices, imported from blockchain or databases, or collected in real time by various different devices or sensors. This embodiment does not limit the method of obtaining the data.

[0028] As one feasible approach to acquiring actual interlayer mechanical response data, at least one mechanical sensor can be embedded at the interface between the surface layer and the base layer to directly monitor the mechanical response data of the interface under load. This mechanical response data can include stress or strain, and it is a core data source for evaluating the bond state. The surface layer can be an asphalt layer, and the base layer can be an old asphalt layer. Furthermore, a temperature and humidity sensor is embedded in the middle of the surface layer to record the temperature and humidity information inside the pavement in real time, obtaining environmental data. This environmental data is crucial for correcting material parameters and interpreting changes in mechanical response information.

[0029] The aforementioned pavement structure data includes the thickness information, material type, gradation type, and relevant dimensional parameters of each structural layer, such as the thickness of the surface layer and the base layer. Detailed material type information is also essential, including the type of surface layer and whether the base layer material is cement-stabilized crushed stone or lime-soil. Different materials have different mechanical properties, which directly affect the modulus inversion results. This pavement structure data is shown in Table 1 below. Table 1

[0030] The aforementioned pavement structure is a composite system composed of multiple layers of materials with different functions. The gradation type is a key indicator that characterizes the proportion of particles in the pavement material, which directly affects the mechanical properties and service stability of the material. Layer 1 can be, for example, surface layer-upper layer, base layer-upper base layer, etc. Material 1 can be, for example, asphalt mixture, and material 2 can be, for example, cement, etc.

[0031] As another feasible approach, the acquisition of deflection basin data requires standardizing the testing conditions to ensure the road surface is dry, the testing speed is uniform, and the load level matches the road type. Then, the testing equipment parameters are set, and deflection basin data is acquired using the testing equipment according to these parameters. This testing equipment may include a falling weight deflectometer (FWD) and a standard loading vehicle. The FWD simulates the instantaneous impact of traffic loads on the road surface, collecting vertical displacement data of the road surface at the load application point and surrounding distances. It also records the arrangement information of each sensor, including the distance between each sensor and the load center, the applicable load level range, and the radius of the load plate. The standard loading vehicle is used to determine the axle load of its standard rear axle and the effective contact area between one tire and the road surface. After setting the testing equipment parameters, an impact load is applied to the road surface to be evaluated using the FWD, and a series of deflection values ​​from the road markers are collected and processed to obtain deflection basin data, which can be seen in Table 2 below. Table 2

[0032] Among them, measuring point 1 and measuring point 2 can be different locations at the distance from the load center, and sensor 1 and sensor 2 can be sensors buried at different measuring points.

[0033] As another feasible method, in the process of acquiring actual interlayer mechanical response data, a standard loading vehicle can be driven at a specified speed to ensure that its rear axle tires precisely roll over the pre-embedded mechanical sensor, simultaneously recording the mechanical response data output by the mechanical sensor, and extracting the key response value from the mechanical response data, which is the peak value. The actual interlayer mechanical response data can be seen in Table 3 below: Table 3

[0034] This embodiment acquires pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data for each structural layer of the pavement to be evaluated. This allows for more comprehensive acquisition of multi-dimensional data, overcoming the limitations of traditional discrete point destructive testing. By comprehensively covering pavement structural characteristics, surface displacement response under load, actual interlayer stress-strain state, and environmental influencing factors through multi-dimensional data, it achieves a holistic and refined characterization of the pavement base layer bonding state. Furthermore, it provides data support for abandoning extreme interface assumptions such as "completely continuous" and "completely smooth." Actual interlayer mechanical response data directly reflects the actual interlayer bonding state, and combined with environmental data, the influence of temperature, humidity, and other factors on bonding performance can be quantified, making the evaluation model more closely aligned with engineering realities. It also provides precise input for modulus inversion and iterative optimization of interlayer bonding coefficients. Pavement structure data clarifies the physical parameters of each layer, deflection basin data reflects the overall load-bearing response, and the synergy of multiple data types ensures the accuracy of target modulus and target bonding coefficient calculations. This provides reliable data foundation information for evaluating the interlayer bonding state of the base layer, enabling long-term tracking and accurate judgment of dynamic changes in the bonding state.

[0035] Step S202: Based on the deflection basin data and environmental data, perform modulus inversion to obtain the target modulus values ​​for each structural layer.

[0036] The aforementioned structural layers include the surface layer, base layer, and subgrade. After obtaining the deflection basin data and environmental data, the interlayer bonding coefficients of the base layer and surface layer can be quantitatively inverted using a modulus inversion algorithm to obtain the target modulus values ​​for the surface layer, base layer, and subgrade. The target modulus value of a structural layer is an elastic modulus parameter used to reflect the mechanical properties of the material in that layer under current load conditions, environmental conditions, and service wear.

[0037] In one embodiment, please refer to the above. Figure 2 Based on this, a specific implementation method is provided for modulus inversion based on deflection basin data and the aforementioned environmental data to obtain the target modulus values ​​of each structural layer. Please refer to [link to relevant documentation]. Figure 3 As shown, the method includes: Step S301: Under the condition of complete continuity between each structural layer, based on the deflection basin data, the initial modulus value of each structural layer is obtained by using the modulus inversion algorithm; each structural layer includes the surface layer, base layer and subgrade.

[0038] Step S302: Based on the environmental data, perform environmental correction on the initial modulus values ​​of each structural layer to obtain the target modulus values ​​of each structural layer.

[0039] Specifically, after obtaining multi-point deflection basin data and environmental data measured by a falling weight deflectometer, a modulus inversion algorithm can be used to invert the initial modulus value E of each structural layer under the assumption that each structural layer is completely continuous. 0i (i = 1, 2, ..., n, where n is the number of structural layers). Where E 01 It can be used as the initial modulus value of the surface layer, reflecting its aging, temperature sensitivity, and fatigue state. The modulus is high at low temperatures and decreases significantly at high temperatures. E 02 The initial modulus value of the base layer reflects the strength and integrity of the base layer material. A low modulus may indicate cracking, water damage, or insufficient compaction. E 03 The initial modulus value of the roadbed is used to characterize the bearing capacity of the roadbed. It is greatly affected by moisture content and density. If the modulus is too low, it is easy to cause permanent deformation.

[0040] Optionally, the aforementioned modulus inversion algorithm can be the deflection basin law method, genetic algorithm, or neural network method, etc. The deflection basin law method can include empirical or semi-empirical methods, which estimate the modulus using the empirical relationship between the geometric features of the deflection basin and the modulus. The geometric features of the deflection basin include, for example, deflection gradient, curvature, and normalized parameters. The genetic algorithm is a global optimization method that searches for the optimal combination of moduli by simulating biological evolution mechanisms, including selection, crossover, and mutation. The neural network method trains the network with a large amount of forward simulation data to establish a mapping relationship between deflection basin data and modulus.

[0041] For example, a falling weight deflectometer is used to apply a simulated traffic load to the pavement under evaluation. Simultaneously, vertical displacement data of the pavement surface at different horizontal distances around the load's center point are collected, forming multi-point deflection basin data reflecting the overall bearing capacity mechanical response of the pavement. This measured multi-point deflection basin data is then imported into a pre-defined calculation model. A modulus inversion algorithm, such as the deflection basin law method, genetic algorithm, or neural network method, is selected as the core tool for data processing. The algorithm uses the mechanical propagation laws inherent in the deflection basin data as a basis to perform data analysis and reverse calculation. Simultaneously, the mechanical assumption of "complete interlayer continuity" is incorporated into the calculation process. This means that there is no relative slippage between adjacent structural layers such as the asphalt surface layer and the base layer, and between the base layer and the subgrade in the constraint model, and displacement and stress can be transmitted completely continuously, thus simplifying the initial calculation boundary. Through the algorithm's reverse calculation of the measured deflection basin data and the constraint adaptation of the assumption, the initial modulus values ​​corresponding to each structural layer, such as the asphalt surface layer, base layer, and subgrade, are obtained, completing the data flow transformation from measured displacement data to structural layer mechanical parameters. This initial modulus value provides a basic mechanical parameter reference for subsequent modulus optimization and interlayer bonding coefficient iterative calculation considering the actual interlayer bonding state.

[0042] Environmental corrections are performed on the initial modulus values ​​of each structural layer based on environmental data to obtain the target modulus values ​​of each structural layer. This includes: using a preset temperature modulus correction model based on temperature data to uniformly correct the initial modulus value of the surface layer to the standard temperature value, thereby obtaining the target modulus value of the surface layer under standard temperature conditions; and using humidity data to perform humidity corrections on the initial modulus values ​​of the base layer and the subgrade, thereby obtaining the target modulus values ​​of the base layer and the subgrade under standard humidity conditions.

[0043] Understandably, considering the significant temperature sensitivity of the surface layer's modulus and the potential influence of moisture content on the base layer and subgrade modulus, it is necessary to perform environmental correction on the initial modulus values ​​of each structural layer to obtain the target modulus values ​​for each layer. The preset temperature modulus correction models include: the Witczak prediction model and the sigmoidal function.

[0044] The aforementioned temperature data can be obtained in real-time from infrared thermal imager measurements or temperature sensors based on on-site measurements. After acquiring the temperature data, for the surface layer, the collected on-site measured temperature data is input into the temperature-modulus correction model. When the temperature-modulus correction model is a Witczak prediction model, a quantitative relationship between temperature and the modulus of the surface layer is established through a regression formula. When the temperature-modulus correction model is a sigmoidal function, the attenuation law of temperature on modulus is described through nonlinear fitting. The model uses the on-site temperature as the input variable and calculates to uniformly correct the surface layer modulus corresponding to the on-site temperature to the modulus value at the industry-standard temperature (e.g., 20℃), thereby eliminating the influence of temperature differences on the modulus of the asphalt layer. For the base course and subgrade, the collected humidity data is combined with the humidity sensitivity characteristics of the base course and subgrade materials themselves, i.e., the correspondence between humidity changes and modulus attenuation. For example, for every 5% increase in humidity, the base course modulus decreases by 15%. The initial modulus of the base course and subgrade is corrected for humidity through quantitative calculation to compensate for the modulus deviation caused by humidity changes. The base course includes, for example, cement-stabilized crushed stone, and the subgrade includes, for example, silty clay. Finally, the target modulus value E after environmental correction is output. i This includes the target modulus values ​​of the surface layer after temperature correction, and the target modulus values ​​of the base and subgrade after humidity correction.

[0045] In this embodiment, the initial modulus values ​​of each structural layer are corrected by environmental data, which can eliminate the influence of environmental factors such as temperature and humidity on the modulus calculation, thereby accurately determining the target modulus values ​​of each structural layer. This facilitates the provision of accurate mechanical parameters that fit the actual on-site environmental conditions for subsequent iterative optimization calculations of the actual interlayer bonding state.

[0046] Step S203: Based on the target modulus value, actual interlayer mechanical response data, pavement structure data, and environmental data, the initial interlayer bonding coefficient value is iteratively optimized to obtain the target interlayer bonding coefficient value.

[0047] The target interlayer bond coefficient is a mechanical parameter used to quantitatively reflect the actual bonding degree between adjacent structural layers of the pavement under evaluation, conforming to the actual engineering situation. The initial interlayer bond coefficient is used to reflect the initial bonding degree between the base course and the surface course.

[0048] In obtaining the target interlayer bond coefficient value, the target modulus value, pavement structure data, and initial interlayer bond coefficient are first analyzed using elastic system mechanics to obtain theoretical interlayer mechanical response data at key locations. The initial interlayer bond coefficient represents the initial bonding degree between the base layer and the surface layer interface. Based on the actual interlayer mechanical response data, theoretical interlayer mechanical response data, and the initial interlayer bond coefficient value, an objective function is constructed. The optimization objective is to minimize the error between the actual and theoretical interlayer mechanical response data. An optimization algorithm iteratively adjusts the initial interlayer bond coefficient value until the convergence condition is met, yielding the target interlayer bond coefficient value. The convergence condition includes that the error corresponding to the objective function is less than a preset value or the number of iterations reaches a preset upper limit; the target interlayer bond coefficient value is located in the range of 0 to 1.

[0049] After obtaining the target modulus value by performing environmental correction on the initial modulus value using environmental data, the thickness h of each structural layer is determined from the pavement structure data. i The thickness of the structural layer can be measured in-situ through core drilling, radar detection, or other methods. An initial interlayer bond coefficient value is set. Considering the unknown nature of the actual bond state in the initial calculation stage, the initial value can usually be set to the value corresponding to a fully bonded state, i.e., K=1, representing no relative slip between layers and complete continuous transmission of stress and displacement. Then, the target modulus value, the initial interlayer bond coefficient value, and the thickness of each structural layer are used for elastic system mechanical analysis. That is, these three sets of parameters are input into multilayer elastic system mechanical analysis software, and under the same load conditions as the field measurement, the theoretical interlayer mechanical response data at key locations are obtained. The multilayer elastic system mechanical analysis software can be, for example, BISAR software which focuses on pavement structure mechanical calculation, ABAQUS software with powerful nonlinear analysis capabilities, or a dedicated calculation program developed according to project requirements.

[0050] During the software calculation process, load conditions that are completely consistent with actual field measurements must be strictly simulated. These load conditions can include load magnitude, application mode, and application location, such as a 50kN instantaneous impact load and a circular uniformly distributed load. The software's built-in elastic layered system mechanics theory is used for calculation, ultimately obtaining the theoretical interlayer mechanical response data σ at key locations. calThis can include theoretical stress response values ​​or theoretical strain response values. Key locations typically include the interfaces between structural layers and the locations of strain sensors already embedded in the field. The interfaces between structural layers directly reflect the mechanical response of interlayer bonding, and the locations of embedded strain sensors facilitate subsequent comparison with measured mechanical response data.

[0051] Taking the actual interlaminar mechanical response data as the actual interlaminar mechanical response value for each measuring point and the theoretical interlaminar mechanical response data as the theoretical interlaminar mechanical response value for each measuring point as an example, after determining the theoretical interlaminar mechanical response data, an objective function is constructed based on the actual interlaminar mechanical response data, the theoretical interlaminar mechanical response data, and the initial interlaminar bonding coefficient value. This objective function can be expressed by the following formula: ; in, w represents the strain response value (stress or strain) at the i-th measuring point. i is the weighting coefficient, which can be customized according to actual needs; K is the interlayer bonding coefficient of the base layer to be inverted. When the first iteration is performed, it is the initial interlayer bonding coefficient value. When the iteration is not performed for the first time, it is the interlayer bonding coefficient value obtained from the previous iteration. This represents the theoretical inter-layer mechanical response data for the i-th measurement point. This represents the actual interlayer mechanical response data for the i-th measurement point.

[0052] After constructing the objective function, the optimization objective is to minimize the error between the actual interlayer mechanical response data and the theoretical interlayer mechanical response data. An intelligent optimization algorithm continuously adjusts the value of the interlayer adhesion coefficient K until the error corresponding to the objective function converges to a preset value or the number of iterations reaches a preset upper limit. This preset value and upper limit can be customized according to actual needs. For example, a preset value of 5% means that the convergence condition is met when the error is <5%. The intelligent optimization algorithm can be, for example, the golden section method, Newton's iteration method, or particle swarm optimization. When the convergence condition is met, the interlayer adhesion coefficient K at this point is output as the final inversion result for that measurement point, yielding the target interlayer adhesion coefficient value.

[0053] In this embodiment, by substituting the target modulus value, pavement structure data, and initial interlayer bond coefficient into the elastic system mechanical analysis, theoretical interlayer mechanical response data can be accurately obtained. Then, by combining this with actual interlayer mechanical response data, an objective function is constructed, with the optimization objective being minimizing the error between the two. The initial interlayer bond coefficient is iteratively adjusted through an optimization algorithm until the convergence condition is met, ultimately yielding the target interlayer bond coefficient value within the 0-1 range. This approach abandons the extreme interface assumptions of "completely continuous" and "completely smooth" in traditional evaluations. It achieves a quantitative characterization of the interlayer bond state through precise alignment of theoretical and measured mechanical responses. Furthermore, iterative optimization allows the target interlayer bond coefficient value to accurately match the partial bond state of the base layer interface in actual engineering projects, significantly improving the accuracy of interlayer bond state evaluation. Simultaneously, the 0-1 value range clearly defines the strength range of the bond, making the evaluation results more intuitive and understandable. This provides scientific and reliable core parameter support for the subsequent scientific selection of tack coat materials and optimization of construction processes.

[0054] Step S204: Analyze the target interlayer bonding coefficient value to obtain the evaluation result of the interlayer bonding state of the base surface.

[0055] The aforementioned evaluation results of interlayer bonding state are used to describe and assess the bonding state between the base layer and the surface layer, and may include: a first result, a second result, a third result, a fourth result, and a fifth result; the degree of interlayer bonding increases sequentially from the first result to the fifth result. Specifically, the first result characterizes complete failure of the bonding state between the base layer and the surface layer, i.e., delamination; the second result characterizes failure of the bonding state between the base layer and the surface layer, i.e., poor bonding; the third result characterizes partial bonding between the base layer and the surface layer, i.e., moderate bonding; the fourth result characterizes basic bonding between the base layer and the surface layer, i.e., good bonding; and the fifth result characterizes complete bonding between the base layer and the surface layer, i.e., excellent bonding.

[0056] In the process of determining the evaluation results of the interlayer bonding state of the base surface, multiple preset thresholds can be set, including a first preset threshold, a second preset threshold, a third preset threshold, and a fourth preset threshold. The first preset threshold is greater than 0 and less than the second preset threshold, the second preset threshold is less than the third preset threshold, the third preset threshold is less than the fourth preset threshold, and the fourth preset threshold is less than 1.

[0057] When the target interlayer bonding coefficient is not less than 0 and less than the first preset threshold K1, the evaluation result of the interlayer bonding state of the substrate is determined as the first result; when the target interlayer bonding coefficient is not less than the first preset threshold K1 and less than the second preset threshold K2, the evaluation result of the interlayer bonding state of the substrate is determined as the second result; when the target interlayer bonding coefficient is not less than the second preset threshold K2 and less than the third preset threshold K3, the evaluation result of the interlayer bonding state of the substrate is determined as the third result; when the target interlayer bonding coefficient is not less than the third preset threshold K3 and less than the fourth preset threshold K4, the evaluation result of the interlayer bonding state of the substrate is determined as the fourth result; when the target interlayer bonding coefficient is not less than the fourth preset threshold K4 and not greater than 1, the evaluation result of the interlayer bonding state of the substrate is determined as the fifth result.

[0058] Specifically, when 0 ≤ K < K1, the first result is achieved, indicating complete bond failure. This signifies complete delamination or near-smooth contact between the base and surface layers, with almost no shear stress transfer between them. The surface layer bears the load independently, leading to premature failure. This state severely impacts driving safety and comfort, necessitating immediate major repairs or remediation. When K1 ≤ K < K2, the second result is achieved, indicating bond failure. This signifies severely insufficient interlayer bonding or localized delamination. The base and surface layers cannot effectively work together, making the surface layer prone to shoving, shoving, and other defects under load. The pavement's load-bearing capacity is significantly reduced. In this state, corrective maintenance measures (such as milling and repaving, overlay reinforcement, etc.) must be implemented immediately to restore structural integrity. When K2 ≤ K < K3, the third result is achieved, indicating partial bonding. This signifies significant deterioration in interlayer bonding performance, with a certain degree of slippage between the base and surface layers. This leads to increased tensile strain at the bottom of the surface layer and concentrated compressive stress on the top surface of the base layer, accelerating fatigue damage accumulation. In this state, it should be included in the maintenance plan, and it is recommended to take preventive maintenance measures (such as crack sealing, micro-surfacing, etc.) in the next maintenance cycle to prevent further deterioration. When K3≤K<K4, it is the fourth result, namely basic bonding, which indicates that the interlayer bonding basically meets the usage requirements, but the bonding performance is slightly reduced compared to the ideal state. Under heavy load or adverse environmental conditions, slight stress concentration may occur. It is recommended to strengthen daily observation, and no immediate maintenance treatment is required. When K4≤K<1, it is the fifth result, namely complete bonding, which indicates that the interlayer bonding is strong, can effectively transfer shear stress, the structural layers work together well, and the overall bearing capacity of the pavement meets the design requirements. In this state, there will be no relative slippage at the interlayer interface, and the structural stress distribution is reasonable. For example, the above preset thresholds can be customized according to the needs of actual application based on the site conditions, such as K4=0.8, K3=0.7, K2=0.4, K1=0.2.

[0059] This embodiment establishes an inversion analysis model based on measured mechanical response, which can accurately identify the interlayer bonding coefficient between the surface layer and the base layer. This overcomes the technical bottleneck of traditional methods being unable to quantitatively evaluate the interlayer bonding state, achieving a fundamental shift from qualitative judgment to quantitative assessment. This significantly improves the accuracy and reliability of evaluating the interlayer bonding state of pavement structures. Furthermore, by adopting on-site service data acquisition, it avoids the destructive testing defects of traditional indoor tests and core drilling. It can achieve continuous monitoring and long-term tracking of the interlayer bonding state of the base and surface layers without damaging the pavement structure. This provides reliable technical support for the scientific selection of tack coat materials and the optimization of construction processes. At the same time, based on mechanical response data under actual pavement service conditions, it overcomes the limitations of size effects and disconnection from environmental conditions in indoor tests. It can truly reflect the dynamic evolution law of the interlayer bonding state of the base and surface layers, providing a scientific basis for early warning and preventive maintenance.

[0060] Please see Figure 4 As shown, pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data are first acquired. Based on the deflection basin and environmental data, inversion processing is performed to obtain the initial modulus values ​​of each structural layer. These initial modulus values ​​are then corrected based on the environmental data to obtain the target modulus values ​​for each structural layer. Theoretical interlayer mechanical response data are then calculated using the corrected target modulus values. The adhesion coefficient K is adjusted to ensure that the error between the theoretical and actual interlayer mechanical response data matches the expected error until the convergence condition is met. At this point, the target interlayer adhesion coefficient value is determined. Analysis is then performed based on the target interlayer adhesion coefficient value to obtain the evaluation results of the interlayer bonding state of the base surface.

[0061] For example, taking a newly built highway test section as an example, it is necessary to evaluate the base bond state of the pavement to be evaluated. First, the pavement structure information of the highway test section needs to be obtained. The pavement structure information may include structural layers, material type, gradation type, thickness, etc., as shown in Table 4 below: Table 4

[0062] The structural layers consist of a surface layer with a thickness of 18cm, a base layer with a thickness of 60cm, and an infinite depth below the top surface of the subgrade. The surface layer may include an upper layer, an intermediate layer, and a lower layer. The base layer includes a semi-rigid base layer. To obtain strain response data at the interface between the base layer and the surface layer, a strain sensor array is embedded at the interface between the surface layer (lower layer) and the base layer (cement-stabilized crushed stone). The arrangement is as follows: Figure 5 As shown, the sensor array includes a transverse strain sensor and a longitudinal strain sensor.

[0063] Lateral strain sensors can be arranged perpendicular to the driving direction to measure the lateral tensile strain at interlayer interfaces. Longitudinal strain sensors can be arranged parallel to the driving direction to measure the longitudinal tensile strain at interlayer interfaces. The sensor is buried 18cm below the road surface (bottom of the surface layer), with its center directly below the standard axle load wheel track. A high-precision strain gauge sensor is used, with a measurement accuracy of ±0.1. The sampling frequency is 200Hz.

[0064] The pavement under evaluation was tested using a falling weight deflectometer (FWD), specifically a standard falling weight deflectometer for pot deflection testing. The load level was controlled at 50±2.5 kN (standard load), and the bearing plate diameter was 300 mm. Seven sensors were placed at distances of 0, 30, 60, 90, 120, 150, and 180 cm from the load center. During testing, the pavement surface temperature was 32℃, and the internal pavement temperature (middle layer) was 25℃. Testing was conducted between 10:00 AM and 11:30 AM in June 2024. Each measuring point was loaded three times, and the average value was taken. The measured pot deflection data are shown in Table 5. Table 5

[0065] Dynamic strain testing was conducted using a standard loaded vehicle with a rear axle load of 10 tons. The standard loaded vehicle was configured with a 2-ton front axle and a 10-ton rear dual-wheel set (5 tons per wheel), with tire pressure of 0.7 MPa. The standard loaded vehicle passed the test section at a constant speed of 60±2 km / h. The road surface temperature was 35℃, and the internal road surface temperature (middle layer) was 30℃. The test was conducted from 14:00 to 15:30 in June 2024 (4 hours after the FWD test). It was assumed that the standard loaded vehicle passed the test section 5 times in each direction, and the maximum strain peak value was extracted as the test result. The obtained measured strain response data included the maximum lateral strain value of 39.1 με and the maximum longitudinal strain value of 55.5 με.

[0066] After acquiring the data from various dimensions, a structural layer modulus inversion operation was performed. First, an initial modulus inversion was conducted. Based on the pavement structure data shown in Table 4, layers with similar structural mechanical properties were merged. Specifically: the surface layer (upper, middle, and lower layers merged) had a thickness of 18cm; the base layer (base and subbase merged) had a thickness of 60cm; and the subgrade (soil base) was a semi-infinite body. Using an intelligent inversion algorithm, under the assumption of complete interlayer continuity (K=1.0), the initial modulus values ​​of each structural layer at 25℃ were obtained based on the deflection basin data in Table 5. These initial modulus values ​​are shown in Table 6. Table 6

[0067] Understandably, considering that the road surface temperature is 25℃ during the FWD test and 30℃ during the standard loaded vehicle strain test, the asphalt mixture modulus is significantly sensitive to temperature. Therefore, the initial modulus values ​​of the surface layer in Table 6 need to be corrected for temperature to ensure consistency between the calculation and actual conditions. The following formula can be used for correction to obtain the corrected target modulus value of the surface layer: ; ; Wherein, α, β, γ, δ, C1 and C2 are fitting parameters, which can be determined through multiple experiments. When there is no measured data for calibration, the fitting coefficients can be taken as 2.767, -0.541, 0.619, 7.468, -25.871 and -187.702, respectively. Here, T is the conversion factor, and T is the temperature at the time of measurement. For reference temperature, a value of 20℃ or 25℃ can be used. The target modulus value.

[0068] After environmental data correction, the target modulus value of the surface layer was obtained as 1608 MPa. Since the pavement structure is a newly built pavement with good performance, the base course and subgrade have stable moisture content, and the interval between the FWD test and the loaded vehicle test is short (only 4 hours), the base course modulus and subgrade modulus do not need to be corrected. The average values ​​in Table 6 are still used, that is, the target modulus value of the base course is 11808 MPa, and the target modulus value of the subgrade is 96 MPa.

[0069] After obtaining the target modulus values ​​for each structural layer, the corrected target modulus values, pavement structure data, and actual interlayer mechanical response data are input into the multilayer elastic system analysis software BISAR 3.0 for mechanical response calculation. Input parameters include: surface layer thickness 18cm, target modulus value 1608MPa, Poisson's ratio 0.25; base layer thickness 60cm, target modulus value 11808MPa, Poisson's ratio 0.25; subgrade as a semi-infinite body, target modulus value 96MPa, Poisson's ratio 0.40. The load is set as a single-axle dual-wheel set, ground pressure 0.7MPa, and circular contact area (equivalent circle radius 10.65cm). The interlayer contact state is characterized by the surface layer-base layer interface bonding coefficient K, with a value range of 0-1, to obtain theoretical interlayer mechanical response data at key locations. This key location can be the center point of the bottom of the surface layer (18cm depth). The theoretical interlayer mechanical response data can include transverse strain response values ​​and longitudinal strain response values.

[0070] Furthermore, an objective function is established by comparing theoretical and actual interlayer mechanical response data. The golden section method is then used to iteratively optimize the interlayer adhesion coefficient K of the base layer. The initial search interval can be set to [0, 1], with a required accuracy of 0.001. The theoretical interlayer strain response data corresponding to different K values ​​are calculated. When K = 0.8008, the error corresponding to the objective function reaches its minimum value of 0.18. At this point, the calculated transverse strain is 39.0. (Error 0.26%), the calculated longitudinal strain is 55.6. (Error 0.18%), Goodness of fit R 2 =0.998. The inversion results show that the interlayer bonding coefficient K=0.8008 at the surface layer-base layer interface of this road section, so this interlayer bonding coefficient is taken as the target interlayer bonding coefficient value.

[0071] Then, according to the quantitative evaluation standard for the interlayer bonding state of the base layer, the grades are classified. For the excellent (completely bonded) grade, the corresponding bonding coefficient range is 0.80 ≤ K ≤ 1.0; for the good (basically bonded) grade, it is 0.60 ≤ K < 0.80; for the average (partially bonded) grade, it is 0.40 ≤ K < 0.60; for the poor (bonding failure) grade, it is 0.20 ≤ K < 0.40; and for the delamination (complete failure) grade, it is 0 ≤ K < 0.20. Since the interlayer bonding coefficient K = 0.8008 obtained from the inversion of this road section is greater than 0.80, the interlayer bonding state of the base layer is rated as "excellent". The evaluation conclusion shows that the interlayer bonding of the base layer is strong, the structural layer has good collaborative performance, meets the design requirements, and no maintenance treatment measures are required; it can continue to be used normally. Specifically, when the pavement is newly paved and the interlayer bonding coefficient K is in the range of [0.8, 1], it indicates that the used sealant material is excellent. Furthermore, the long-term performance of the sealant material can be further evaluated.

[0072] In one embodiment, when the service life of the pavement to be evaluated is 0-2 years, the testing frequency is once a year; when the service life of the pavement to be evaluated is 3-5 years, the testing frequency is once a year; when the service life of the pavement to be evaluated is 6-8 years, the testing frequency is once every six months; and when the service life of the pavement to be evaluated exceeds 8 years, the testing frequency is once a quarter.

[0073] Specifically, in order to track the evolution trend of the interfacial bonding state of the base course layers in this section over the service time, repeated tests can be carried out according to the following test frequencies: monitor once a year during the service period of 0 - 2 years, and the expected bonding coefficient is maintained at K > 0.80 (excellent); monitor once a year during the service period of 3 - 5 years, and the expected bonding coefficient is in the range of 0.70 < K < 0.8; monitor once every six months during the service period of 6 - 8 years, and the expected bonding coefficient is in the range of 0.50 < K < 0.75; monitor once a quarter after the service period exceeds 8 years. At this time, K may drop to less than 0.60, and preventive maintenance measures need to be taken.

[0074] In this embodiment, by repeatedly carrying out FWD deflection basin tests and standard loading vehicle strain tests in different years, and calculating the interfacial bonding coefficients of the base course layers in each period according to the same inversion method as above, a time series database of the bonding coefficients of this section can be established, realizing the full-life cycle dynamic monitoring of the interfacial bonding state of the base course layers, and providing a scientific basis for predictive maintenance decisions.

[0075] This application provides a method for evaluating the interfacial bonding state of the pavement base course. Compared with the prior art, in this solution, by obtaining the pavement structure data, deflection basin data, actual interlayer mechanical response data reflecting the true stress and strain between layers, and environmental data of each structural layer of the pavement to be evaluated, it is possible to comprehensively track the dynamic change trend of the interfacial bonding state of the base course layers in the long term without damaging the pavement and without affecting its normal use, collect multi-dimensional data such as structure, mechanical response, and environment, and provide complete and real data support for subsequent evaluation; and based on the deflection basin data and environmental data for modulus inversion, the determined target modulus value is more in line with the actual service state, improving the accuracy of modulus parameters; and according to the target modulus value, actual interlayer mechanical response data, pavement structure data, and environmental data, iterative optimization processing is carried out on the initial interlayer bonding coefficient value, abandoning the traditional extreme assumptions, using the actual interlayer mechanical response data to fully reflect the "partial bonding" state, and through multi-data fusion iterative optimization, realizing the precise quantification of the interlayer bonding coefficient, and solving the problem that the traditional evaluation cannot quantify the partial bonding state; and then analyzing the target interlayer bonding coefficient value to obtain the evaluation result of the interfacial bonding state of the base course layer. Based on the precisely quantified bonding coefficient for evaluation, replacing the traditional discrete destructive testing, it is possible to realize the evaluation of the global bonding state, greatly improving the accuracy of the evaluation of the interfacial bonding state of the base course layer, and thus ensuring the scientific selection of the penetration bonding layer material and providing a reliable basis for the optimization of the construction process.

[0076] Based on the same inventive concept, this application also provides a device for evaluating the interlayer bonding state of pavement subgrade as described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the pavement subgrade interlayer bonding state evaluation device provided below can be found in the limitations of the pavement subgrade interlayer bonding state evaluation method described above, and will not be repeated here.

[0077] In one exemplary embodiment, such as Figure 6 As shown, a device for evaluating the interlayer bonding state of a road surface is provided. The device includes: The acquisition module 510 is used to acquire pavement structure data, deflection basin data, actual interlayer mechanical response data and environmental data of each structural layer of the pavement to be evaluated. Deflection basin data refers to a set of surface displacement values ​​measured at key locations after the pavement to be evaluated is loaded. Actual interlayer mechanical response data reflects the real stress or strain between each structural layer of the pavement under load. Inversion module 520 is used to perform modulus inversion based on deflection basin data and environmental data to obtain the target modulus values ​​of each structural layer. The iterative optimization module 530 is used to iteratively optimize the initial interlayer bonding coefficient value based on the target modulus value, actual interlayer mechanical response data, pavement structure data and environmental data, so as to obtain the target interlayer bonding coefficient value. The result determination module 540 is used to analyze the target interlayer bonding coefficient value and obtain the evaluation result of the interlayer bonding state of the base surface.

[0078] As an optional implementation, the inversion module 520 is specifically used for: Under the condition of complete continuity between structural layers, the initial modulus values ​​of each structural layer are obtained by using a modulus inversion algorithm based on deflection basin data; each structural layer includes surface layer, base layer and subgrade. The initial modulus values ​​of each structural layer are corrected based on environmental data to obtain the target modulus values ​​of each structural layer.

[0079] As an optional implementation, the inversion module 520 is also used for: Based on the temperature data, the initial modulus value of the surface layer is uniformly corrected to the standard temperature value using a preset temperature modulus correction model, thus obtaining the target modulus value of the surface layer under standard temperature conditions. Based on humidity data, the initial modulus values ​​of the base course and the subgrade are corrected for humidity to obtain the target modulus values ​​of the base course and the subgrade under standard humidity conditions.

[0080] As an optional implementation, the preset temperature modulus correction model includes: Witczak prediction model and sigmoidal function.

[0081] As an optional implementation, the iterative optimization module 530 is specifically used for: The target modulus value, pavement structure data, and initial interlayer bond coefficient are used to conduct elastic system mechanical analysis to obtain theoretical interlayer mechanical response data at key locations; the initial interlayer bond coefficient is the degree of bonding between the initial base layer and surface layer interface; Based on actual interlayer mechanical response data, theoretical interlayer mechanical response data, and initial interlayer bonding coefficient values, an objective function is constructed. The optimization objective is to minimize the error between the actual interlayer mechanical response data and the theoretical interlayer mechanical response data. An optimization algorithm is used to iteratively adjust the initial interlayer bonding coefficient value until the convergence condition is met, and the target interlayer bonding coefficient value is obtained. The convergence condition includes that the error corresponding to the objective function is less than a preset value or the number of iterations reaches a preset upper limit. The target interlayer bonding coefficient value is located in the range of 0 to 1.

[0082] As an optional implementation, the result determination module 540 is specifically used for: When the target interlayer bonding coefficient is not less than 0 and is less than the first preset threshold, the evaluation result of the interlayer bonding state of the base surface is determined as the first result. When the target interlayer bonding coefficient value is not less than the first preset threshold and less than the second preset threshold, the evaluation result of the interlayer bonding state of the base surface is determined as the second result; When the target interlayer bonding coefficient value is not less than the second preset threshold and less than the third preset threshold, the evaluation result of the interlayer bonding state of the base surface is determined as the third result; When the target interlayer bonding coefficient value is not less than the third preset threshold and less than the fourth preset threshold, the evaluation result of the interlayer bonding state of the base surface is determined as the fourth result; When the target interlayer bonding coefficient is not less than the fourth preset threshold and not greater than 1, the evaluation result of the interlayer bonding state of the base surface is determined as the fifth result. The first preset threshold is greater than 0 and less than the second preset threshold, the second preset threshold is less than the third preset threshold, the third preset threshold is less than the fourth preset threshold, and the fourth preset threshold is less than 1; the degree of interlayer adhesion in the first result, second result, third result, fourth result, and fifth result increases sequentially.

[0083] As an optional implementation, the above-described apparatus is further used for: When the service life of the road surface to be evaluated is 0-2 years, the testing frequency is once a year. When the service life of the road surface to be evaluated is 3-5 years, the testing frequency is once a year. When the service life of the road surface to be evaluated is 6-8 years, the testing frequency is once every six months; When the service life of the road surface to be evaluated exceeds 8 years, the testing frequency is once per quarter.

[0084] The pavement base layer interlayer bonding state evaluation device provided in this application embodiment acquires pavement structure data, deflection basin data, actual interlayer mechanical response data reflecting the true stress and strain between layers, and environmental data for each structural layer of the pavement to be evaluated. This allows for comprehensive long-term tracking of the dynamic changes in the bonding state of the base layer interface without damaging the pavement or affecting its normal use. It collects multi-dimensional data including structural, mechanical response, and environmental data, providing complete and accurate data support for subsequent evaluations. Furthermore, it performs modulus inversion based on deflection basin data and environmental data, making the determined target modulus value more closely match the actual service condition and improving the accuracy of the modulus parameters. Finally, it calculates the target modulus value and the actual interlayer mechanical response data. Based on data from pavement structure and environmental data, the initial interlayer bond coefficient value is iteratively optimized. This approach abandons traditional extreme assumptions and utilizes actual interlayer mechanical response data to fully reflect the "partial bond" state. Through multi-data fusion and iterative optimization, the interlayer bond coefficient is accurately quantified, solving the problem that traditional evaluation methods cannot quantify partial bond states. Furthermore, the target interlayer bond coefficient value is analyzed to obtain the evaluation results of the base layer interlayer bond state. Evaluation based on the accurately quantified bond coefficient replaces traditional discrete destructive testing, enabling the evaluation of the bond state across the entire surface area. This significantly improves the accuracy of base layer bond state evaluation, ensuring the scientific selection of tack coat materials and providing a reliable basis for optimizing construction processes.

[0085] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores video tag processing data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for evaluating the interlayer bonding state of a pavement base.

[0086] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0087] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0088] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0089] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0090] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0091] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0092] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating the bonding state between pavement base layers, characterized in that, The method for evaluating the interlayer bonding state of the pavement base includes: The process involves acquiring pavement structure data, deflection basin data, actual interlayer mechanical response data, and environmental data for each structural layer of the pavement to be evaluated. The deflection basin data refers to a set of surface displacement values ​​measured at key locations after a load is applied to the pavement to be evaluated. The actual interlayer mechanical response data reflects the actual stress or strain between the structural layers of the pavement to be evaluated under load. Based on the deflection basin data and the environmental data, modulus inversion is performed to obtain the target modulus values ​​of each structural layer. Based on the target modulus value, the actual interlayer mechanical response data, the pavement structure data, and the environmental data, the initial interlayer bonding coefficient value is iteratively optimized to obtain the target interlayer bonding coefficient value. The interlayer bonding coefficient of the target layer is analyzed to obtain the evaluation result of the interlayer bonding state of the base surface.

2. The method for evaluating the interlayer bonding state of road surface according to claim 1, characterized in that, Based on the deflection basin data and the environmental data, modulus inversion is performed to obtain the target modulus values ​​for each structural layer, including: Under the condition that the structural layers are completely continuous, the initial modulus values ​​of each structural layer are obtained by using the modulus inversion algorithm based on the deflection basin data; the structural layers include the surface layer, the base layer, and the subgrade. Based on the environmental data, the initial modulus values ​​of each structural layer are corrected for environmental conditions to obtain the target modulus values ​​of each structural layer.

3. The method for evaluating the interlayer bonding state of road surface according to claim 2, characterized in that, The environmental data includes: temperature data and humidity data; Based on the environmental data, the initial modulus values ​​of each structural layer are adjusted according to environmental conditions to obtain the target modulus values ​​of each structural layer, including: Based on the temperature data, the initial modulus value of the surface layer is uniformly corrected to the standard temperature value using a preset temperature modulus correction model to obtain the target modulus value of the surface layer under standard temperature conditions. Based on the humidity data, the initial modulus values ​​of the base layer and the roadbed are corrected for humidity to obtain the target modulus values ​​of the base layer and the roadbed under standard humidity conditions.

4. The method for evaluating the interlayer bonding state of road surface according to claim 3, characterized in that, The preset temperature modulus correction model includes: Witczak prediction model and sigmoidal function.

5. The method for evaluating the interlayer bonding state of road surface according to claim 1, characterized in that, Based on the target modulus value, the actual interlayer mechanical response data, the pavement structure data, and the environmental data, the initial interlayer bond coefficient is iteratively optimized to obtain the target interlayer bond coefficient value, including: The target modulus value, the pavement structure data, and the initial interlayer bonding coefficient are used to perform elastic system mechanical analysis to obtain the theoretical interlayer mechanical response data at the key locations; the initial interlayer bonding coefficient is the degree of bonding between the initial base layer and the surface layer interface; Based on the actual interlayer mechanical response data, the theoretical interlayer mechanical response data, and the initial interlayer bonding coefficient value, an objective function is constructed. The optimization objective is to minimize the error between the actual interlayer mechanical response data and the theoretical interlayer mechanical response data. An optimization algorithm is used to iteratively adjust the initial interlayer bonding coefficient value until the convergence condition is met, and the target interlayer bonding coefficient value is obtained. The convergence condition includes that the error corresponding to the objective function is less than a preset value or the number of iterations reaches a preset upper limit. The target interlayer bonding coefficient value is located in the range of 0 to 1.

6. The method for evaluating the interlayer bonding state of road surface according to claim 1, characterized in that, The evaluation results of the interlayer bonding state of the base surface include: the first result, the second result, the third result, the fourth result, and the fifth result; The interlayer bonding coefficient value of the target layer is analyzed to obtain the evaluation result of the interlayer bonding state of the base surface, including: When the target interlayer bonding coefficient value is not less than 0 and is less than the first preset threshold, the evaluation result of the interlayer bonding state of the base surface is determined as the first result; When the target interlayer bonding coefficient value is not less than the first preset threshold and less than the second preset threshold, the evaluation result of the interlayer bonding state of the base surface is determined as the second result; When the target interlayer bonding coefficient value is not less than the second preset threshold and less than the third preset threshold, the evaluation result of the interlayer bonding state of the base surface is determined to be the third result; When the target interlayer bonding coefficient value is not less than the third preset threshold and less than the fourth preset threshold, the evaluation result of the interlayer bonding state of the base surface is determined to be the fourth result; When the target interlayer bonding coefficient value is not less than the fourth preset threshold and not greater than 1, the evaluation result of the interlayer bonding state of the base surface is determined as the fifth result; The first preset threshold is greater than 0 and less than the second preset threshold, the second preset threshold is less than the third preset threshold, the third preset threshold is less than the fourth preset threshold, and the fourth preset threshold is less than 1; the interlayer adhesion in the first result, second result, third result, fourth result, and fifth result increases sequentially.

7. The method for evaluating the interlayer bonding state of road surface according to claim 1, characterized in that, The method further includes: When the service life of the road surface to be evaluated is 0-2 years, the testing frequency is once a year. When the service life of the road surface to be evaluated is 3-5 years, the testing frequency is once a year. When the service life of the road surface to be evaluated is 6-8 years, the testing frequency is once every six months; When the service life of the road surface to be evaluated exceeds 8 years, the testing frequency is once per quarter.

8. A device for evaluating the interlayer bonding state of road surface, characterized in that, The road surface interlayer bonding state evaluation device includes: The acquisition module is used to acquire pavement structure data, deflection basin data, actual interlayer mechanical response data and environmental data of each structural layer of the pavement to be evaluated; the deflection basin data refers to a set of surface displacement values ​​measured at key locations after the load is applied to the pavement to be evaluated, and the actual interlayer mechanical response data reflects the real stress or strain between each structural layer of the pavement under load. The inversion module is used to perform modulus inversion based on the deflection basin data and the environmental data to obtain the target modulus values ​​of each structural layer. The iterative optimization module is used to iteratively optimize the initial interlayer bonding coefficient value based on the target modulus value, the actual interlayer mechanical response data, the pavement structure data, and the environmental data to obtain the target interlayer bonding coefficient value. The result determination module is used to analyze the target interlayer bonding coefficient value and obtain the evaluation result of the interlayer bonding state of the base surface.

9. A computer device, comprising: The memory and processor are computer programs stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method for evaluating the interlayer bonding state of pavement base layers according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for evaluating the interlayer bonding state of the pavement base layer as described in any one of claims 1-7.

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