Prediction method for modulus matching correction coefficient of embedded fiber grating sensor and asphalt mixture

Abstract

The invention relates to a method for predicting a modulus matching correction coefficient of an embedded fiber grating sensor and an asphalt mixture, and the method comprises the steps: manufacturing asphalt mixture test pieces of fiber grating sensors with different moduli, carrying out a vehicle load test, and collecting strain data. Finite element software ABAQUS is used for modeling, and material parameters are obtained and defined. And establishing digital models of different modulus parameters, and simulating a vehicle load test to obtain strain data. Through regression analysis of actual measurement and simulation data, correction coefficients are fitted, and a prediction model is established. Compared with the prior art, the problems that the strain measured by the fiber bragg grating sensor is smaller than the real strain, the measured result is inaccurate and the like due to the fact that the modulus of the embedded fiber bragg grating sensor is not matched with the modulus of the asphalt mixture are solved through finite element simulation, a solution is provided for researching the real value of the pavement structure, and the method has wide applicability.

Description

Technical Field

[0001] The invention relates to the technical field of road engineering, and in particular to a method for predicting a correction coefficient of modulus matching between an embedded fiber grating sensor and an asphalt mixture. Background Art

[0002] Up to now, the total mileage of highways in my country has exceeded 170,000 kilometers, ranking first in the world, among which asphalt pavement structures are widely used in highways. However, with the continuous growth of traffic volume, road structure diseases have also continued to appear. These road diseases not only greatly shorten the service life of the road, but also bring a series of traffic safety hazards. In order to specifically study and analyze the real causes of road diseases under the influence of factors such as actual environment and vehicle load, it is very necessary to study the real mechanical response inside the asphalt pavement structure. Embedded fiber grating sensor has the characteristics of high sensitivity, strong corrosion resistance, simple structure, good stability, etc., and can monitor the stress-strain response and road temperature of the road on site, collect data in real time, and is an effective device to replace traditional measurement methods to measure the real response inside the pavement structure.

[0003] Since the modulus of fiber grating sensors is generally much larger than that of asphalt mixtures at this stage, fiber grating sensors cannot deform synergistically with asphalt mixtures. Therefore, the strain measured by fiber grating sensors cannot directly reflect the true strain inside the asphalt mixture structure layer. In order to correct the strain data measured by the embedded fiber grating sensor and improve its monitoring accuracy, it is necessary to find a suitable prediction method to determine the modulus matching relationship between fiber grating sensors with different moduli and asphalt mixtures. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of modulus matching between embedded fiber Bragg grating sensors and asphalt mixtures and to provide a prediction method for the modulus matching correction coefficient between embedded fiber Bragg grating sensors and asphalt mixtures. The present invention uses finite element software ABAQUS for numerical simulation, establishes a numerical model of embedded fiber Bragg grating sensors and asphalt mixtures, and fits the simulated strain value with the measured strain value of the fiber Bragg grating sensor to obtain the modulus matching correction coefficient, and uses this to predict fiber Bragg grating sensors and asphalt mixtures of different moduli. The method has wide applicability and simple operation, and the convenience of defining material parameters through finite element software replaces the cumbersome process of actual experiments.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for predicting the correction coefficient of the matching between an embedded fiber grating sensor and an asphalt mixture modulus, comprising the following steps:

[0006] S1: Prepare asphalt mixture specimens embedded with fiber Bragg grating sensors of different moduli and conduct vehicle load tests to collect strain data measured by the fiber Bragg grating sensors at the detection position;

[0007] S2: Use finite element software ABAQUS to model the asphalt mixture specimen embedded with fiber Bragg grating sensors, obtain the material parameters of the fiber Bragg grating sensors and asphalt mixture through indoor tests, and define them in the finite element software;

[0008] S3: By combining asphalt mixture modeled in finite element software ABAQUS and optical fibers with different moduli, digital models with various material parameters are established;

[0009] S4: By conducting a simulated vehicle load test on the models with different modulus parameters established in the finite element software ABAQUS, the strain data of each model detection position are obtained;

[0010] S5: Perform regression analysis on the measured strain data and the strain data calculated by the finite element software ABAQUS, obtain the correction coefficient based on the data results, and establish a prediction model of the modulus and strain of the optical fiber-asphalt mixture;

[0011] S6: Fiber Bragg grating sensors with different moduli are selected for indoor experiments and numerical simulations. After substituting the correction coefficients into calculations, it is concluded that the simulated data and measured data correspond to the prediction model.

[0012] Furthermore, in step S1, the prepared specimen needs to prefabricate two vertical through grooves on both sides of the mold, and place a fiber optic Bragg grating sensor in each of the through grooves on both sides. The actual preset position of the fiber optic Bragg grating sensor is at 1 / 2 of the asphalt mixture specimen, and the specimen is made by rotating and compacting the asphalt mixture to simulate the actual road surface.

[0013] Furthermore, in step S1, the MTS tester is used to perform dynamic loading of the test piece to simulate the vehicle load, and at the same time, the wavelength data measured by the fiber grating sensor is recorded and converted into strain data.

[0014] Furthermore, in step S2, the size of the model built using ABAQUS is completely consistent with the size of the specimen prepared in step S1, the elastic modulus of the fiber grating sensor is obtained through a tensile test, and the viscoelastic parameters of the asphalt mixture are obtained through a dynamic modulus test.

[0015] Furthermore, in step S3, the digital models with multiple different parameters refer to the fiber Bragg grating sensor elastic modulus parameters defined by different models being different, while the asphalt mixture viscoelastic parameters and model geometric dimensions are the same.

[0016] Furthermore, in step S4, the position, direction, size, time and other factors of the simulated vehicle load added to the model are exactly the same as those of the vehicle load added in step S1.

[0017] Furthermore, in step S5, the strain data obtained by numerical simulation calculation takes the strain value of the asphalt mixture at the fiber Bragg grating sensor in the numerical simulation, and the measured strain data of the fiber Bragg grating sensor is used as the independent variable, and the strain data calculated by numerical simulation is used as the strain variable for linear fitting to obtain the correction coefficient.

[0018] Furthermore, in step S6, another fiber Bragg grating sensor with a different modulus is selected to perform the above steps S1 to S5, and the correction coefficient prediction model can be verified by substituting the acquired data into the correction coefficient.

[0019] Furthermore, in step S1, the mold is an asphalt mixture rotary compaction specimen embedded with fiber grating sensors of different moduli.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1) The present invention achieves accurate prediction of the matching correction coefficient of the embedded fiber grating sensor and the asphalt mixture modulus by combining experimental testing and finite element simulation. This method not only improves the accuracy of the strain data measured by the sensor, but also provides an effective solution for the study of the true value of the pavement structure. Through the simulation of the finite element software ABAQUS, the present invention can simulate the asphalt mixture specimens under different modulus parameters, so as to obtain more accurate strain data, which is of great significance for improving the monitoring and evaluation of the pavement structure.

[0022] 2) Another significant advantage of the present invention is its wide applicability. Through the combination of indoor tests and numerical simulations, the present invention can adapt to fiber grating sensors of different moduli, and through the calculation of correction coefficients, the reliability of measurement results under different conditions is ensured. This flexibility allows the present invention to be applied to a variety of different engineering environments and road structures, providing a universal monitoring tool for road construction and maintenance.

[0023] 3) The implementation process of the present invention is highly systematic and scientific. Through detailed step definition, including specimen preparation, finite element modeling, parameter definition, simulation test and data regression analysis, the present invention provides a standardized operation process. This process not only ensures the repeatability of the experiment, but also makes the results highly credible. In addition, through the verification step, the present invention further ensures the accuracy and practicality of the prediction model, providing a solid foundation for the long-term monitoring and evaluation of pavement structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Provides an overall method flow chart for an embodiment of the present invention;

[0025] Figure 2 This is the diagram of the rotational compaction specimen;

[0026] Figure 3 It is an asphalt mixture simulation model;

[0027] Figure 4 Assemble models for asphalt mixture and optical fiber;

[0028] Figure 5 is the viscoelastic parameter of asphalt mixture;

[0029] Figure 6 Divide the mesh cells for the simulation model;

[0030] Figure 7 It is the simulated stress cloud diagram;

[0031] Figure 8 This is the simulation strain cloud diagram. DETAILED DESCRIPTION

[0032] On the whole, the present invention relates to a prediction method for a correction coefficient of the modulus matching between an embedded fiber grating sensor and an asphalt mixture, comprising: making an asphalt mixture specimen embedded with fiber grating sensors of different moduli and conducting a vehicle load test, collecting strain data measured by the fiber grating sensor at the detection position; using finite element software ABAQUS to model the asphalt mixture specimen embedded with the fiber grating sensor, obtaining material parameters of the fiber grating sensor and the asphalt mixture through indoor experiments, and defining them in the finite element software; establishing a digital model of multiple different material parameters by combining the asphalt mixture modeled in the finite element software ABAQUS and optical fibers of different moduli; obtaining strain data at each model detection position by conducting a simulated vehicle load test on the model of different modulus parameters established in the finite element software ABAQUS; performing regression analysis on the obtained measured strain data and the strain data calculated by the finite element software ABAQUS, obtaining the correction coefficient based on the data results, and establishing a prediction model of the modulus and strain of the optical fiber-asphalt mixture; selecting fiber grating sensors of different moduli for indoor experiments and numerical simulations, and substituting the correction coefficients into calculations to obtain a conclusion that the simulated data and the measured data correspond to the prediction model. The present invention solves the problem of the mismatch between the embedded fiber grating sensor and the asphalt mixture modulus through finite element simulation, which leads to the strain measured by the fiber grating sensor being smaller than the actual strain and the measured result being inaccurate. It provides a solution for studying the true value of pavement structure and has wide applicability.

[0033] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms and other features not clearly described in this technical solution are all considered to be common technical features disclosed in the prior art.

[0034] Example 1

[0035] This embodiment provides a method for predicting the correction coefficient of the matching between the embedded fiber grating sensor and the asphalt mixture modulus. Figure 1 , the embodiment comprises the following steps:

[0036] S1: Prepare asphalt mixture specimens embedded with fiber Bragg grating sensors of different moduli and conduct vehicle load tests to collect strain data measured by the fiber Bragg grating sensors at the detection position;

[0037] The asphalt mixture specimen embedded with the fiber Bragg grating sensor prepared in this embodiment adopts the rotary compaction specimen molding method. A vertical through groove with a width of 8 mm is prefabricated on both sides of the mold to place the fiber Bragg grating sensor and facilitate the demoulding of the specimen. The cylindrical specimen is prepared by rotary compaction by a rotary compactor. The density of the molded specimen meets the Marshall standard compaction specimen density requirement of 100% ± 1. The prepared specimen is as follows Figure 2 shown.

[0038] Three types of optical fiber sizes are selected in this embodiment: GFRP strain optical cable, armored strain optical cable, and armored spiral strain optical cable. The optical fibers are cylindrical with diameters of 0.3 cm, 0.3 cm, and 0.6 cm, and lengths of 20 cm. The elastic modulus of the three optical fibers are measured experimentally to be 13.74 GPa, 14.29 GPa, and 15.96 GPa, respectively.

[0039] In order to simulate the load value applied when an actual vehicle passes by, the indoor test load uses an MTS instrument to apply a sinusoidal wave load with a peak of 2000N and a loading frequency of 25 Hz, that is, a loading cycle of 0.04s.

[0040] In this embodiment, when the sensor is subjected to a load, the grating will deform and cause a change in the grating period, causing the refractive index to change, thereby causing the wavelength of the light to drift. Without considering the coupling between temperature and strain and temperature changes, the following relationship exists between the Bragg wavelength and strain:

[0041]

[0042] Among them, p 11 、p 12 is the photoelastic constant, v is the Poisson's ratio, and ε is the strain.

[0043] The wavelength measured by the optical fiber is converted into strain through formula calculation. At the same time, the fiber grating sensor needs to be calibrated to avoid errors.

[0044] S2: Use finite element software ABAQUS to model the asphalt mixture specimen embedded with fiber Bragg grating sensors, obtain the material parameters of the fiber Bragg grating sensors and asphalt mixture through indoor tests, and define them in the finite element software;

[0045] The asphalt mixture and optical fiber material models were established by entering the component module using ABAQUSs software. The model size is consistent with the size of the indoor test specimen. The size of the asphalt mixture model is a cylinder with a diameter of 15 cm and a height of 15 cm, and two small semi-cylinders are cut vertically on both sides 1.5 cm away from the center of the circle, such as Figure 3 shown.

[0046] Assemble the asphalt mixture and the optical fiber together. The assembly position should be consistent with the size of the indoor test specimen, such as Figure 4 shown.

[0047] In order to ensure the accuracy of numerical simulation, the asphalt mixture in the model of this embodiment adopts the viscoelastic model, and the optical fiber material adopts the elastic model. Therefore, it is necessary to calculate the parameters required for the viscoelastic model of the asphalt mixture. The viscoelastic model adopts the Prony series model. The required parameters are gi and τ. The Excel table is selected for calculation. First, the experimental data is imported into the table, and then the calculation is performed through the formula. The calculation results are as follows: Figure 5 shown.

[0048] Enter the viscoelastic parameters of the asphalt mixture into the material editing bar, and then assign the viscoelastic parameters of the asphalt mixture and the elastic modulus parameters of the optical fiber material to the two models respectively. Then define the contact relationship between the asphalt mixture and the optical fiber in the interaction module. The contact relationship between the optical fiber and the asphalt mixture adopts Penalty friction contact, and the Friction Coeff is set to 0.7. Next, create an analysis step in the analysis step module, and define the time length of the analysis step as 0.2. Finally, add a load, which is consistent with the indoor test load. In the amplitude manager area, choose to add five cycles of load data, corresponding to the time length of 0.2s in the analysis step.

[0049] S3: By combining asphalt mixture modeled in finite element software ABAQUS and optical fibers with different moduli, digital models with various material parameters are established;

[0050] Specifically, the asphalt mixture was assembled with the GFRP strain optical cable, the armored strain optical cable, and the armored spiral strain optical cable, and the corresponding fiber modulus was modified in each model parameter. The model was then meshed, and the place where the asphalt mixture and the optical fiber models were combined was re-meshed to avoid the mutual influence of the two models caused by the insertion of the optical fiber into the asphalt mixture after assembly. The asphalt mixture model unit type uses the 8-node hexahedral quadratic reduced integration unit C3D8R, and the optical fiber model uses the 8-node hexahedral non-coordinated unit C3D8I. After the components are divided into mesh units, Figure 6 shown.

[0051] S4: By conducting a simulated vehicle load test on the models with different modulus parameters established in the finite element software ABAQUS, the strain data of each model detection position are obtained;

[0052] Specifically, add bottom rigid constraints to the assembled model in ABAQUS software and apply sinusoidal loads to simulate the actual scene when a vehicle passes by. Click the calculation button to obtain the stress and strain cloud diagram as shown in the figure. Figure 7 , Figure 8 As shown, by clicking on each unit grid, the stress and strain data of the corresponding unit grid can be obtained, so as to obtain the strain generated by the model in the load experiment when the asphalt mixture is combined with fiber grating sensors of different moduli.

[0053] S5: Perform regression analysis on the measured strain data and the strain data calculated by the finite element software ABAQUS, obtain the correction coefficient based on the data results, and establish a prediction model of the modulus and strain of the optical fiber-asphalt mixture;

[0054] Specifically, the measured strain data of the specimens composed of three different fiber grating sensors and asphalt mixtures obtained through indoor tests were regressed with the simulation experimental data of the model constructed in ABAQUS to obtain the modulus matching function and correction coefficient. The functional expression of the modulus and strain of the fiber-asphalt mixture is:

[0055]

[0056] Among them, 1.3028 is the correction factor.

[0057] The formula is used in such a way that the actual elastic modulus of the fiber Bragg grating sensor can be calculated through the stress and strain data obtained through indoor experiments; conversely, the actual stress and strain values ​​can also be calculated through simulation experiments using the elastic modulus actually measured.

[0058] S6: Select fiber grating sensors with different moduli for indoor testing and numerical simulation, substitute the correction coefficients for calculation, and draw conclusions on the correspondence between the simulated data and the measured data and the prediction model, so as to determine the accuracy of the modulus and strain function obtained in this embodiment, and determine whether it can be used for modulus matching between fiber grating sensors and asphalt mixtures.

[0059] As a preferred embodiment, it is necessary to conduct experimental verification on the obtained modulus matching function and correction coefficient. Two other fiber Bragg grating sensors with moduli of 14.976 GPa and 16.638 GPa were selected for corresponding indoor tests and simulation experiments.

[0060] First, the actual stress and strain values ​​obtained from the indoor test under the same experimental conditions are used to calculate the modulus through the functional expression of the modulus and strain of the optical fiber-asphalt mixture obtained in this embodiment. The error between the calculated modulus and the actual modulus is 2.71%; accordingly, the modulus is input into the simulation experimental model to obtain the stress and strain cloud map, and the error between the actual stress and strain values ​​obtained from the indoor test is 1.87% and 1.93%. It is proved that the modulus and strain function obtained in this embodiment has good accuracy and can be used for modulus matching between fiber grating sensors and asphalt mixtures.

[0061] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A method for predicting the matching correction coefficient of embedded fiber grating sensor and asphalt mixture modulus, characterized in that: The following steps are involved: S1: Prepare asphalt mixture specimens embedded with fiber Bragg grating sensors of different moduli and conduct vehicle load tests to collect strain data measured by the fiber Bragg grating sensors at the detection position; S2: Use finite element software ABAQUS to model the asphalt mixture specimen embedded with fiber Bragg grating sensors, obtain the material parameters of the fiber Bragg grating sensor and asphalt mixture through indoor tests, and define them in the finite element software; S3: By combining asphalt mixture modeled in finite element software ABAQUS and optical fibers with different moduli, digital models with various material parameters are established; S4: By conducting a simulated vehicle load test on the models with different modulus parameters established in the finite element software ABAQUS, the strain data of each model detection position are obtained; S5: Perform regression analysis on the measured strain data and the strain data calculated by the finite element software ABAQUS, obtain the correction coefficient based on the data results, and establish a prediction model of the modulus and strain of the optical fiber-asphalt mixture.

2. The method for predicting the correction coefficient of the matching between the embedded fiber grating sensor and the asphalt mixture modulus according to claim 1, characterized in that: In step S1, the process of making asphalt mixture specimens embedded with fiber grating sensors of different moduli includes: Two vertical through grooves are prefabricated on both sides of the mold, and a fiber grating sensor is placed in each through groove on both sides. The fiber grating sensor is set at 1 / 2 of the asphalt mixture specimen, and the specimen is made by rotating and compacting the asphalt mixture to simulate the actual road surface.

3. The method for predicting the correction coefficient of the matching between the embedded fiber grating sensor and the asphalt mixture modulus according to claim 1, characterized in that: In step S1, the MTS tester is used to perform dynamic loading of the test piece to simulate the vehicle load, and the wavelength data measured by the fiber grating sensor is recorded and converted into strain data.

4. The method for predicting the correction coefficient of the matching between the embedded fiber grating sensor and the asphalt mixture modulus according to claim 1, characterized in that: In step S2, the size of the model built by ABAQUS is consistent with the size of the specimen prepared in step S1, the elastic modulus of the fiber Bragg grating sensor is obtained through a tensile test, and the viscoelastic parameters of the asphalt mixture are obtained through a dynamic modulus test.

5. The method for predicting the correction coefficient of the matching between the embedded fiber grating sensor and the asphalt mixture modulus according to claim 1, characterized in that: In step S3, during the process of establishing a plurality of digital models with different parameters: the elastic modulus parameters of the fiber Bragg grating sensors defined in different models are different, and the viscoelastic parameters of the asphalt mixture and the geometric dimensions of the models are the same.

6. The method for predicting the correction coefficient of the matching between the embedded fiber grating sensor and the asphalt mixture modulus according to claim 1, characterized in that: In step S4, the position, direction, size, and time factors of the simulated vehicle load added to the model are the same as those of the vehicle load added in step S1.

7. The method for predicting the matching correction coefficient of the embedded fiber grating sensor and asphalt mixture modulus according to claim 1, characterized in that: In step S5, the strain data obtained by numerical simulation calculation is taken as the strain value of the asphalt mixture at the fiber Bragg grating sensor in the numerical simulation, and the measured strain data of the fiber Bragg grating sensor is used as the independent variable and the strain data calculated by numerical simulation is used as the strain variable for linear fitting to obtain the correction coefficient.

8. The method for predicting the correction coefficient of the matching between the embedded fiber grating sensor and the asphalt mixture modulus as claimed in claim 2, characterized in that: In step S1, the mold is an asphalt mixture rotary compaction specimen capable of embedding fiber grating sensors of different moduli.

9. The method for predicting the correction coefficient of the matching between the embedded fiber grating sensor and the asphalt mixture modulus according to claim 1, characterized in that: After step S5, the method further includes: S6: Select fiber Bragg grating sensors with different moduli for indoor testing and numerical simulation. Substitute the correction coefficients into the calculation to draw a conclusion that the simulated data and measured data correspond to the prediction model, so as to verify the prediction results.

10. The method for predicting the matching correction coefficient of the embedded fiber grating sensor and asphalt mixture modulus according to claim 9, characterized in that: In step S6, it specifically includes selecting another fiber Bragg grating sensor with a different modulus to perform steps S1 to S5, and substituting the acquired data into the correction coefficient to verify the correction coefficient prediction model.

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