Asphalt pavement structure state sensing method

Abstract

The present application relates to the technical field of asphalt pavement measurement, and particularly relates to a method for sensing the state of asphalt pavement structure, comprising the following steps: embedding a plurality of temperature sensors in each structural layer of the asphalt pavement; connecting the plurality of temperature sensors with a processor, and monitoring the temperature of the plurality of temperature sensors in real time, so as to realize the monitoring of the temperature field of the asphalt pavement; performing regression analysis on the temperature field of the asphalt pavement, and fitting the temperature field model of the asphalt pavement; embedding stress and strain sensors in each structural layer of the asphalt pavement; and testing the dynamic response of stress and strain of each layer under different axle loads, temperatures and vehicle speeds. The present application accurately describes the change of temperature by fitting the temperature field model of the asphalt pavement structure and then testing the dynamic response, thereby providing more reliable data support for the response of temperature, and improving the accuracy and applicability of the test.

Description

Technical Field

[0001] This invention relates to the field of asphalt pavement measurement technology, and in particular to a method for sensing the structural state of asphalt pavement. Background Technology

[0002] The structural condition of asphalt pavement includes its mechanical response state and its temperature field change state. Since asphalt mixture is a viscoelastic material, the temperature change of the pavement has a significant impact on the performance of the asphalt mixture. However, in the existing technology, the testing of the structural condition of asphalt pavement is mostly limited to the dynamic response of the pavement to speed, axle load, etc., and often ignores the influence of temperature, resulting in the test results being greatly affected by the environment.

[0003] In the prior art, for example, Chinese invention patent application CN113916147A, published on January 11, 2022, discloses an evaluation method for the co-deformation of an embedded fiber optic sensor and asphalt pavement. This method involves filling an asphalt mixture, a fiber optic grating sensor, and a linear displacement sensor into a specimen mold to form a standard specimen, and then applying a simulated vehicle impact force in the vertical direction. By collecting and compensating for displacement data, strain data, and temperature data, the method achieves the evaluation of the co-deformation of the asphalt pavement.

[0004] However, when implementing the above solution, the inventors found that since the performance of asphalt pavement is not the same under different temperature conditions, the temperature compensation method cannot truly reflect the real pavement condition information. Summary of the Invention

[0005] In view of at least one of the above technical problems, the present invention provides a method for sensing the structural state of asphalt pavement, which improves the accuracy of the test by first fitting a temperature field model and then conducting stress response tests.

[0006] According to a first aspect of the present invention, a method for sensing the structural state of asphalt pavement is provided, comprising the following steps:

[0007] Multiple temperature sensors are embedded in each structural layer of the asphalt pavement.

[0008] Multiple temperature sensors are connected to a processor to monitor the temperature of the multiple temperature sensors in real time, thereby enabling the monitoring of the temperature field of the asphalt pavement.

[0009] Regression analysis was performed on the temperature field of asphalt pavement, and a temperature field model of asphalt pavement was fitted.

[0010] Stress and strain sensors are embedded in each structural layer of the asphalt pavement.

[0011] The dynamic response of stress and strain in each layer was tested under different axle loads, temperatures, and vehicle speeds.

[0012] In some embodiments of the present invention, the temperature sensor and stress-strain sensor are installed by laying them during construction.

[0013] In some embodiments of the present invention, the temperature sensor and stress-strain sensor are installed by drilling holes at selected locations after construction is completed.

[0014] In some embodiments of the present invention, emulsified asphalt filler is used for filling during drilling and embedding.

[0015] In some embodiments of the present invention, the temperature sensor is laid at the bottom and middle of each layer during the laying process.

[0016] In some embodiments of the present invention, the temperature field model of the asphalt pavement is as follows:

[0017] T(z,t)=a×T 平均 +b×ΔT×(c×sin(ω(t-t0))+d×sin(2ω(t-t0)))

[0018] In the formula, T 平均 ω is the average of the daily maximum and minimum temperatures; ΔT is the difference between the daily maximum and minimum temperatures; ω is the angular frequency; a, b, c, and d are regression coefficients; and t0 is the initial phase.

[0019] In some embodiments of the present invention, in the asphalt pavement long-temperature model, the regression coefficients a, b, c, d and the initial phase t0 are fitted with functions using the pavement surface depth z as the independent variable based on the regression parameter values ​​of different months and different depths:

[0020] a(z) = 3E-05z 2 -0.0027z + 1.3052

[0021] b(z) = 2.175e -0.066z

[0022] c(z) = 6E-07z 4 -7E-05z 3 +0.0028z 2 -0.029z + 0.5154

[0023] d(z)=0.0001z 2 -0.0055z + 0.1212

[0024] t0(z) = 0.1685z + 9.5154.

[0025] In some embodiments of the present invention, the stress-strain sensor includes a horizontal strain sensor, a vertical strain sensor, and a water-stable strain sensor.

[0026] In some embodiments of the present invention, a soil pressure sensor is also included for measuring the vertical pressure of a physical quantity;

[0027] The prediction equation for the dynamic response of asphalt pavement is:

[0028] ε(p)=f*e g*v+h*T+i*W

[0029] In the formula, f, g, h, i are constants obtained through fitting, v is the vehicle speed, T corresponds to the temperature change of the structural layer, and W is the axle load.

[0030] The beneficial effects of this invention are as follows: by first fitting the temperature field model of the asphalt pavement structure and then conducting dynamic response tests, this invention accurately describes the temperature change, thereby providing more reliable data support for the temperature response and improving the accuracy and applicability of the test. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the steps of the asphalt pavement structure state sensing method in an embodiment of the present invention.

[0033] Figure 2 This is a schematic diagram of the sensor installation cross-section in an embodiment of the present invention;

[0034] Figure 3 This is a graph showing the measured and fitted temperature curves of the asphalt pavement temperature field in March, as described in this embodiment of the invention.

[0035] Figure 4 This is a graph showing the measured and fitted temperature curves of the asphalt pavement temperature field in December in an embodiment of the present invention.

[0036] Figure 5 This is a schematic diagram of the measured temperature and fitted temperature for each month in an embodiment of the present invention;

[0037] Figure 6 This is a schematic diagram showing the measured and predicted results of the triaxial strain of the bottom layer in an embodiment of the present invention;

[0038] Figure 7This is a schematic diagram of the drilling and embedding structure of the temperature sensor in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0040] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0042] like Figure 1 The asphalt pavement structure state sensing method shown includes the following steps:

[0043] S10: Multiple temperature sensors 1 are embedded in each structural layer of the asphalt pavement; such as Figure 2 As shown, during the actual paving process, temperature sensors 1 are installed at the top of the subbase 01 (bottom of the subbase 02), the middle of the subbase 02, the bottom of the lower base 03, the middle of the lower base 03, the bottom of the upper base 04, the middle of the upper base 04, the bottom of the lower layer 05, the middle of the lower layer 05, the bottom of the intermediate layer 06, the middle of the intermediate layer 06, and the bottom of the upper layer 07. By paving different structural layers at different depths, the temperature state of the asphalt pavement structural layers can be measured more accurately and comprehensively.

[0044] S20: Connect multiple temperature sensors 1 to the processor to monitor the temperature of multiple temperature sensors 1 in real time, so as to realize the monitoring of the temperature field of asphalt pavement; it should be noted that the processor can be connected to a computer through an industrial control computer; when collecting data, since the temperature changes slowly over time, combined with the collection frequency of temperature sensor 1, one temperature data is output every hour, that is, 24-hour temperature data.

[0045] S30: Perform regression analysis on the temperature field of asphalt pavement and fit an asphalt pavement temperature field model; in some embodiments of the present invention, the asphalt pavement temperature field model is as follows:

[0046] T(z,t)=a×T 平均 +b×ΔT×(c×sin(ω(t-t0))+d×sin(2ω(t-t0)))

[0047] In the formula, T 平均 ΔT is the average of the daily maximum and minimum temperatures; ΔT is the difference between the daily maximum and minimum temperatures; ω is the angular frequency; a, b, c d is the regression coefficient; t0 is the initial phase; z represents the depth value. Due to the heat conduction along the depth direction caused by the temperature difference, the initial phase t0 at which the highest temperature occurs, as well as the parameters a, b, c, and d, are different at different depths. In the embodiments of this invention, such as... Figure 3 and Figure 4 As shown in the figure, in this embodiment of the invention, the regression parameters a, b, c, d, and t0 in the formula first need to be assigned initial values. a and b correspond to the average value of the sensor data collected at different depths, the quotient of the average T, and ΔT, respectively; c and d are usually less than 1; t0 is the initial phase. Since the deeper the asphalt pavement, the later the time for the highest temperature to occur will be, i.e., its value will be larger, it is recommended that the initial value be 10. Substitute the initial values ​​of each regression parameter into Formula 1 to calculate the predicted value for 24 hours at the corresponding depth. Subtract the difference from the measured value and calculate the sum of squares. Using the "Linear Programming Solver" function in Excel, set each parameter as a variable cell, and find the value that minimizes the sum of squares of the differences while satisfying Formula 1. The obtained value is the value of each regression parameter at the corresponding depth. Fit the asphalt pavement temperature field data at different depths in different months. Further, in this embodiment of the invention, after obtaining the values ​​of each regression parameter at different depths in different months, organize the relationship between the double sine parameters a, b, c, d, and t0 at different depths from the road surface and their distances from the road surface. The average values ​​of the double sine curve parameters a(z), b(z), c(z), d(z), and t0(z) obtained by fitting at different depths in each month are respectively fitted with functions with the distance from the road surface depth z as the independent variable, and the corresponding functions for each parameter are obtained respectively:

[0048] a(z) = 3E-05z 2 -0.0027z + 1.3052

[0049] b(z) = 2.175e -0.066z

[0050] c(z) = 6E-07z 4 -7E-05z 3 +0.0028z2 -0.029z + 0.5154

[0051] d(z)=0.0001z 2 -0.0055z + 0.1212

[0052] t0(z) = 0.1685z + 9.5154.

[0053] In the specific calculations, substituting the above parameters into the formula yields the temperature prediction equation for the asphalt pavement temperature at different depths in different months, such as... Figure 5 The figure shows the relationship between the measured temperature and the fitted temperature for each month. It can be seen from the figure that the temperature calculated by the temperature prediction equation is similar to the actual test temperature. Therefore, the equation can be applied to the performance test of asphalt pavement.

[0054] S40: Stress and strain sensors are embedded in each structural layer of the asphalt pavement.

[0055] S50: The dynamic response of stress and strain in each layer is tested under different axle loads, temperatures, and vehicle speeds. In this embodiment of the invention, analysis shows that the internal response of the asphalt pavement is positively correlated with temperature and axle load, but negatively correlated with loading speed, and overall exhibits an exponential correlation. In this embodiment of the invention, the triaxial strain prediction equation is fitted using the prediction equation of the dynamic response of the asphalt pavement. Specifically, in this embodiment of the invention, the dynamic response equation is:

[0056] ε(p)=f*e g*v+h*T+i*W

[0057] In the formula, f, g, h, i are constants obtained through fitting, v is the vehicle speed, T corresponds to the temperature change of the structural layer, and W is the axle load.

[0058] The corresponding stress and strain values ​​under the influence of speed, temperature, and axle load are substituted into the dynamic response equation. Based on the characteristics of the exponential function, parameter g is negative, while the other parameters are positive. After testing according to the experimental plan, the corresponding values ​​of each parameter can be obtained through linear programming. In this embodiment of the invention, the fitting results of the triaxial strain prediction equation for the asphalt lower layer obtained after fitting the dynamic response equation are shown in the table below:

[0059] Table 1 Fitting function for peak triaxial strain of asphalt layer

[0060]

[0061] like Figure 6The figure shows the correlation between the measured and predicted triaxial strain results of the bottom layers. It can be seen that the correlation between the predicted and measured results is very high. Therefore, in this embodiment of the invention, the strain data corresponding to different depths and temperatures during testing can be calculated using the above prediction equation. Compared with the existing technology that only uses a single parameter on the specimen, the testing scope is broader and the accuracy is further improved.

[0062] Based on the above embodiments, the installation of sensors and stress-strain sensors in this invention embodiment has two methods. One method involves laying the temperature sensor 1 and stress-strain sensor during construction, i.e., laying the temperature sensor 1 in a predetermined layer. The second method involves drilling holes at selected locations after construction is completed. Figure 7 As shown, during the drilling and installation, emulsified asphalt filler is used for filling. Before filling, the sensor can be tied and fixed according to the predetermined burial depth, and then the corresponding emulsified asphalt filler filling operation is carried out.

[0063] In this embodiment of the invention, the stress-strain sensor includes a horizontal strain sensor, a vertical strain sensor, and a water-stabilized strain sensor. It also includes an earth pressure sensor for measuring the vertical pressure of the physical quantity. During installation, it is embedded at the bottom of the asphalt surface layer, the bottom of the water-stabilized base layer, and the roadbed. The specific installation method can be determined by those skilled in the art based on the actual situation.

[0064] Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for sensing the structural state of asphalt pavement, characterized in that, Includes the following steps: Multiple temperature sensors are embedded in each structural layer of the asphalt pavement. Multiple temperature sensors are connected to a processor to monitor the temperature of the multiple temperature sensors in real time, thereby enabling the monitoring of the temperature field of the asphalt pavement. Regression analysis was performed on the temperature field of asphalt pavement, and a temperature field model of asphalt pavement was fitted. Stress and strain sensors are embedded in each structural layer of the asphalt pavement. The dynamic response of stress and strain in each layer was tested under different axle loads, temperatures, and vehicle speeds. The temperature field model for the asphalt pavement is as follows: ； In the formula, T 平均 ω is the average of the daily maximum and minimum temperatures; ΔT is the difference between the daily maximum and minimum temperatures; ω is the angular frequency; a, b, c, and d are regression coefficients; t0 is the initial phase. In the aforementioned long-term temperature model for asphalt pavement, the regression coefficients a, b, c, d and the initial phase t0 are fitted with functions using the pavement surface depth z as the independent variable based on the regression parameter values ​​for different months and depths: a(z)=3×10 -5 with 2 -0.0027z+1.3052; b(z)=2.175e -0.066z； c(z)=6×10 -7 With 4 -7×10 -5 With 3 +0.0028z 2 -0.029z+0.5154; d(z)=0.0001z 2 -0.0055z+0.1212; t0(z) = 0.1685z + 9.5154; The stress-strain sensor includes a horizontal strain sensor, a vertical strain sensor, and a water-stable strain sensor. It also includes earth pressure sensors, used to measure the vertical pressure of physical quantities; The prediction equation for the dynamic response of asphalt pavement is: ； In the formula, f, g, h, i are constants obtained through fitting, v is the vehicle speed, T corresponds to the temperature change of the structural layer, and W is the axle load.

2. The asphalt pavement structure state sensing method according to claim 1, characterized in that, The temperature sensor and stress-strain sensor are installed by laying them during construction.

3. The asphalt pavement structure state sensing method according to claim 1, characterized in that, When installing the temperature sensor and stress-strain sensor, the method of drilling holes at the selected location after construction is completed is adopted.

4. The asphalt pavement structure state sensing method according to claim 3, characterized in that, When drilling and embedding, emulsified asphalt filler is used for filling.

5. The asphalt pavement structure state sensing method according to claim 1, characterized in that, When laying the temperature sensor, it is laid at the bottom and middle of each layer.

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