An asphalt pavement fatigue life prediction method based on vehicle platoon

Abstract

The present application relates to the technical field of road engineering, and particularly relates to a kind of asphalt pavement fatigue life prediction method based on vehicle platoon, comprising the following steps: S1, establishing three-dimensional finite element model of asphalt pavement considering viscoelasticity characteristics;S2, realizing numerical simulation of platoon periodic moving load;S3, extracting asphalt layer bottom strain response waveform characteristic parameter;S4, establishing platoon load nonlinear fatigue damage accumulation model considering loading sequence and intermittent effect;S5, calibrating model parameters and establishing platoon fatigue life prediction formula;S6, platoon fatigue life prediction and correction are carried out.The present application extends nonlinear damage model from material level to structure level, considers loading sequence effect and intermittent self-healing effect, overcomes the defect that traditional Miner linear cumulative criterion cannot reflect platoon load nonlinear damage accumulation, significantly improves pavement fatigue life prediction precision under platoon driving scenario, and has good engineering practical value.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and specifically to a method for predicting the fatigue life of asphalt pavement based on vehicle platooning. Background Technology

[0002] With the rapid development of autonomous driving technology, vehicle platooning is gradually becoming a practical application. In platooning, multiple vehicles continuously pass through the same road surface cross-section with extremely small spacing and highly consistent trajectories. This significantly enhances the periodicity and concentration of the load, altering the stress state of the road structure and posing challenges to the applicability of asphalt pavement fatigue life prediction methods based on traditional manned driving scenarios.

[0003] Currently, in terms of damage accumulation theory, existing methods generally adopt the Miner linear cumulative damage criterion. This criterion assumes that the damage generated by each level of load is independent and linearly superimposed, neglecting the influence of loading order on fatigue damage accumulation. In platoon load simulation, some studies have proposed methods to apply platoon cyclic moving loads through finite element modeling and the Dload subroutine. However, because the damage kernel still uses the linear cumulative criterion, it cannot reflect the nonlinear influence of loading order on damage accumulation when platoon vehicles pass in sequence. At the same time, it ignores the self-healing effect of asphalt mixture during load intervals, while the time interval between vehicles in platooning is a key variable affecting pavement damage accumulation. In terms of nonlinear damage models, they are only designed for two-level indoor loading conditions and cannot be directly applied to multi-level cyclic loading scenarios such as platooning. The model parameters are obtained through indoor specimen tests and no correlation is established with the pavement structural mechanical response. The influence of load intervals on fatigue damage is not considered.

[0004] In summary, existing technologies lack a method for predicting pavement fatigue life that can accurately characterize the convoy load pattern, precisely describe the nonlinear fatigue damage accumulation mechanism under variable amplitude loading, and take into account the self-healing effect during load intervals. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for predicting the fatigue life of asphalt pavements based on vehicle platooning, comprising the following steps: S1. Establish a three-dimensional finite element model of asphalt pavement considering viscoelastic properties; S2. Implement numerical simulation of cyclic moving loads in formation; S3. Extract characteristic parameters of the strain response waveform at the bottom of the asphalt layer; S4. Establish a nonlinear fatigue damage accumulation model for formation loads that considers loading sequence and intermittent effects; S5. Calibrate the model parameters and establish a formation fatigue life prediction formula; S6. Perform formation fatigue life prediction and correction.

[0006] Preferably, the step S1 of establishing a three-dimensional finite element model of asphalt pavement considering viscoelastic properties specifically includes: Obtain the geometric and material parameters of typical asphalt pavement structures; obtain dynamic modulus and phase angle data at at least three temperature levels and at least five loading frequencies through asphalt mixture dynamic modulus tests; based on the time-temperature equivalence principle, using reference temperature... Construct the dynamic modulus principal curve and phase angle principal curve; use the least squares method to fit the principal curve data into the Prony series form of the generalized Maxwell model, and obtain the relaxation time of each order. and the corresponding shear modulus With bulk modulus Input the fitted parameters into the finite element software to complete the definition of the viscoelastic material properties of the asphalt pavement.

[0007] Preferably, the numerical simulation of the formation periodic moving load described in step S2 specifically includes: The tire contact patch shape is simplified to a rectangular uniformly distributed load. The rectangle size is determined based on the tire model and tire pressure. For dual-wheel tires, the center-to-center distance between the two rectangular loads is 1.5 times the width of the rectangle. The load amplitude is converted into ground pressure based on the vehicle axle load and contact area. A Dload subroutine is written using the finite element software user subroutine interface. The subroutine includes a wheel track coordinate search algorithm and calculates the current load center position based on the analysis step time and vehicle speed. The platooning cycle time is calculated based on the number of vehicles n, vehicle speed v, distance between vehicles x, and lateral offset y. The cyclic loading of the platooned vehicles is achieved by setting the MOD function on the analysis step time. When the platoon consists of vehicles with different axle types, conditional statements are set in the subroutine to distinguish the load amplitude of different vehicles.

[0008] Preferably, the extraction of the characteristic parameters of the strain response waveform at the bottom of the asphalt layer in step S3 specifically includes: Finite element simulations were performed for different formation conditions, and the time history curves of the maximum longitudinal tensile strain at the bottom of the asphalt layer were output. The following characteristic parameters were extracted from the strain waveforms: peak strain. The time interval between peak strains of adjacent vehicles Duration of single vehicle load Amplitude attenuation rate between adjacent peak strains and the residual strain after the formation passes through. .

[0009] Preferably, the establishment of the nonlinear fatigue damage accumulation model for formation loading considering loading sequence and intermittent effects in step S4 includes establishing a constant amplitude loading damage evolution equation based on the Chaboche nonlinear fatigue damage evolution model: ; In the formula, As a damage variable, The number of load cycles, The fatigue life under constant amplitude loading, For model parameters that depend on stress level and temperature, These are temperature-dependent model parameters.

[0010] Preferably, step S4 further includes introducing an intermittent period influencing factor. and intermittent time function Establish a modified damage evolution equation: ; In the formula, This represents the damage increment resulting from a single load cycle. For stress level, Interval time The exponentially decaying function, The material healing rate coefficient, The material's healing ability coefficient; and The fatigue-intermittent test determined that in a constant amplitude loading fatigue test, when the damage reaches a preset value... Loading stops when the insertion length is reached. After the interval period, loading continued until failure, and the fatigue life extension rate under different interval times was recorded and fitted.

[0011] Preferably, the nonlinear fatigue damage accumulation model in step S4 uses the following recursive method for damage accumulation: (a) Initialize cumulative damage The formation passed through a counter. ; (b) For the first After the formation passes, the calculation of the nth formation in sequence is performed. Incremental damage to vehicles , , The total number of vehicles in the platoon; (c) Incremental damage to vehicles Based on the current stress level Current cumulative damage and the interval between the vehicle in front Joint decision; (d) Update cumulative damage ; (e) when Record the current time. The value is the formation fatigue life Nplatoon; otherwise, return to step (b).

[0012] Preferably, the calibration of model parameters in step S5 specifically includes: Fatigue life at each stress level was obtained through constant amplitude loading fatigue tests at at least three different stress levels. Based on the damage evolution curve, the test data at each stress level are nonlinearly fitted using the damage evolution equation to obtain the model parameters. and and establish parameters With stress level Functional relationship To characterize the nonlinear effect of loading order on damage accumulation; the healing capacity coefficient is calibrated through the fatigue-intermittent test. and healing rate coefficient .

[0013] Preferably, the step S5 of establishing the formation fatigue life prediction formula specifically includes: Orthogonal experimental design was used to generate simulation condition combinations covering the range of parameter values ​​for each formation, and the fatigue life of the formation was calculated for each condition. With the fatigue life of the formation as the dependent variable and the formation parameters and pavement structure parameters as independent variables, a multivariate nonlinear regression was used to establish an explicit prediction formula in the following form: ; ; In the formula, This represents the maximum tensile strain at the bottom of the asphalt layer when a single vehicle passes over it. , , The fitting constant is The exponent is the power factor; the number of vehicles in the formation parameters. Pick Vehicles, speed Pick km / h, distance between vehicles Pick m, lateral offset Pick cm.

[0014] Preferably, the formation fatigue life prediction and correction step S6 specifically includes: Substituting the target formation's driving parameters and road structure parameters into the estimation formula, the formation's fatigue life is calculated. When there is a difference between the actual service environment temperature and the model calibration temperature, a temperature correction coefficient is introduced. The predicted results are corrected using the following formula: ; in The dynamic modulus ratio at different temperatures is used to determine the formation fatigue life. The formation fatigue life output by the method is used for the design of pavement structure thickness for dedicated lanes for autonomous driving formations, the assessment and optimization of the impact of formation driving strategies on pavement damage, and the decision on the timing of pavement preventive maintenance based on the estimated life.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This method can accurately reflect the nonlinear influence of loading sequence on damage accumulation, and at the same time quantifies the self-healing behavior of asphalt mixture in the gaps between vehicles, thus eliminating the root cause of prediction error from the perspective of damage mechanism.

[0016] 2. This invention generates simulation working condition combinations through orthogonal experimental design and establishes an explicit prediction formula between formation fatigue life and formation parameters and road structure parameters using multivariate nonlinear regression. 3. This invention is applicable to various typical road structure forms. When the formation consists of vehicles with different axle types, conditional judgment statements can be set in the Dload subroutine to distinguish the load amplitude of different vehicles and realize fatigue life prediction of non-uniform formation. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a schematic diagram of the three-dimensional finite element model of the asphalt pavement in this invention. Figure 3 This is a comparison diagram of the strain waveforms at the bottom of the asphalt layer under different formation conditions in this invention; Figure 4 This is a comparison chart of formation fatigue life prediction results under different damage accumulation criteria in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings.

[0020] Please see Figure 1 and combined Figure 4 This application provides a method for predicting the fatigue life of asphalt pavement based on vehicle platooning, comprising the following steps: S1. Establish a three-dimensional finite element model of asphalt pavement considering viscoelastic properties; Geometric and material parameters of a typical asphalt pavement structure were obtained. A typical semi-rigid base asphalt pavement structure was selected, with the surface layer using SBS modified asphalt mixture SMA-13, 4cm thick for the top layer and 6cm thick for the bottom layer; the base layer was cement-stabilized crushed stone, 38cm thick; and the subbase layer was graded crushed stone, 20cm thick. Dynamic modulus tests were conducted on the asphalt mixture to obtain dynamic modulus and phase angle data at at least three temperature levels and at least five loading frequencies. Dynamic modulus tests were performed on the SMA-13 ​​asphalt mixture at five temperature levels (-10℃, 5℃, 20℃, 35℃, and 50℃) at six frequencies (0.1Hz, 0.5Hz, 1Hz, 5Hz, 10Hz, and 25Hz). Based on the time-temperature equivalence principle, a reference temperature was used. At 20℃, the dynamic modulus principal curve and phase angle principal curve were constructed; the principal curve data were fitted to the Prony series form of the generalized Maxwell model using the least squares method to obtain the relaxation time of each order. and the corresponding shear modulus With bulk modulus Input the fitted parameters into the finite element software to complete the definition of the viscoelastic material properties of the asphalt pavement.

[0021] S2. Implement numerical simulation of cyclic moving loads in formation; The tire contact patch shape is simplified to a 22cm × 18cm rectangular uniformly distributed load. For dual-wheel tires, the center-to-center distance between the two rectangular loads is taken as 1.5 times the width of the rectangle (i.e., 27cm). The load amplitude is converted to a ground pressure of 0.7MPa based on the vehicle axle load and contact area. A typical six-axle truck is selected as the platoon model, with the axle load distribution as follows: first axle 6t, second axle 10t, third axle 10t, fourth axle 10t, fifth axle 10t, and sixth axle 10t. The wheelbase is as follows: front overhang 1.5m, first axle to second axle 3.6m, second axle to third axle 1.35m, third axle to fourth axle 7.0m, fourth axle to fifth axle 1.35m, fifth axle to sixth axle 1.35m. A Dload subroutine was written using the finite element software user subroutine interface. This subroutine includes a wheel track coordinate search algorithm and calculates the current load center position based on the analysis step time and vehicle speed. The formation cycle time is calculated based on the number of vehicles n, vehicle speed v, distance between vehicles x, and lateral offset y. The periodic loading of the formation vehicles is achieved by setting the MOD function on the analysis step time. In this embodiment, the number of vehicles n is set to 3 or 5 vehicles, the vehicle speed v is set to 60km / h, 80km / h, or 100km / h, the distance between vehicles x is set to 4m, 8m, or 12m, and the lateral offset y is set to 0cm, 15cm, or 30cm. A total of 54 formation simulation conditions were generated using orthogonal experimental design.

[0022] S3. Extract characteristic parameters of the strain response waveform at the bottom of the asphalt layer; Finite element simulations were performed for each formation condition combination, and the time history curve of the maximum longitudinal tensile strain at the bottom of the asphalt layer was output. The following characteristic parameters were extracted from the strain waveform: peak strain. The time interval between peak strains of adjacent vehicles Duration of single vehicle load Amplitude attenuation rate between adjacent peak strains and the residual strain after the formation passes through. .

[0023] S4. Establish a nonlinear fatigue damage accumulation model for formation loads that considers loading sequence and intermittent effects; This includes establishing a constant-amplitude loading damage evolution equation based on Chaboche's nonlinear fatigue damage evolution model: ; Introducing the intermittent period impact factor and intermittent time function Establish a modified damage evolution equation: ; 7. Damage accumulation is performed using the following recursive method: (a) Initialize cumulative damage The formation passed through a counter. ; (b) For the first After the formation passes, the calculation of the nth formation in sequence is performed. Incremental damage to vehicles , , The total number of vehicles in the platoon; (c) Incremental damage to vehicles Based on the current stress level Current cumulative damage and the interval between the vehicle in front Joint decision; (d) Update cumulative damage ; (e) when Record the current time. The value is the formation fatigue life Nplatoon; otherwise, return to step (b).

[0024] S5. Calibrate the model parameters and establish a formation fatigue life prediction formula; Constant amplitude loading fatigue tests were conducted at three stress ratio levels of 0.10, 0.20, and 0.30, and the fatigue life was recorded. The model parameters were obtained by nonlinearly fitting the test data at various stress levels using the damage evolution equation. and and establish parameters With stress level Functional relationship In this embodiment, It is 0.68 at low stress levels and 0.82 at high stress levels; The value is 1.25; the healing capacity coefficient was calibrated through fatigue-intermittent testing. and healing rate coefficient In the constant amplitude loading test, when the damage reaches the preset value Loading was stopped at a certain time, and after intermittent periods of 0.1s, 0.5s, 2.0s, and 10.0s, loading continued until failure. The fatigue life extension rate under different intermittent periods was recorded. The results were obtained through fitting. =0.15 , =0.35.

[0025] For 54 simulation conditions, the above-mentioned damage accumulation model was used to calculate the fatigue life of the formation. With the fatigue life of the formation as the dependent variable and the formation parameters and pavement structure parameters as independent variables, a multivariate nonlinear regression was used to establish an explicit prediction formula in the following form: ; ; In this embodiment, the fitted parameter value is: A = 2.45 × 10 14 , B=3.97, C0=1.0, C1=0.15, p1=0.5, C2=-0.008, p2=1.2, C3=0.02, p3=0.8, C4=-0.01, p4=1.0.

[0026] S6. Perform formation fatigue life prediction and correction.

[0027] The fatigue life of the formation is calculated by substituting the target formation's driving parameters and road structure parameters into the prediction formula. When there is a difference between the actual service environment temperature and the model calibration temperature, a temperature correction factor is introduced. The predicted results are corrected using the following formula: ; The formation fatigue life output by the method of this invention can be used for the design of pavement structure thickness for dedicated lanes for autonomous driving formations, the assessment and optimization of the impact of formation driving strategies on pavement damage, and the decision on the timing of preventive pavement maintenance based on the estimated life.

[0028] It should be noted that, in this document, the terms "left," "right," "front," "rear," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0029] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the fatigue life of asphalt pavement based on vehicle platooning, characterized in that, Includes the following steps: S1. Establish a three-dimensional finite element model of asphalt pavement considering viscoelastic properties; S2. Implement numerical simulation of cyclic moving loads in formation; S3. Extract characteristic parameters of the strain response waveform at the bottom of the asphalt layer; S4. Establish a nonlinear fatigue damage accumulation model for formation loads that considers loading sequence and intermittent effects; S5. Calibrate the model parameters and establish a formation fatigue life prediction formula; S6. Perform formation fatigue life prediction and correction.

2. The method according to claim 1, characterized in that, Step S1, which involves establishing a three-dimensional finite element model of asphalt pavement considering viscoelastic properties, specifically includes: Obtain the geometric and material parameters of typical asphalt pavement structures; obtain dynamic modulus and phase angle data at at least three temperature levels and at least five loading frequencies through asphalt mixture dynamic modulus tests; based on the time-temperature equivalence principle, using reference temperature... Construct the dynamic modulus principal curve and phase angle principal curve; use the least squares method to fit the principal curve data into the Prony series form of the generalized Maxwell model, and obtain the relaxation time of each order. and the corresponding shear modulus With bulk modulus Input the fitted parameters into the finite element software to complete the definition of the viscoelastic material properties of the asphalt pavement.

3. The method according to claim 1, characterized in that, The numerical simulation of the periodic moving load in formation described in step S2 specifically includes: The tire contact patch shape is simplified to a rectangular uniformly distributed load. The rectangle size is determined based on the tire model and tire pressure. For dual-wheel tires, the center-to-center distance between the two rectangular loads is 1.5 times the width of the rectangle. The load amplitude is converted into ground pressure based on the vehicle axle load and contact area. A Dload subroutine is written using the finite element software user subroutine interface. The subroutine includes a wheel track coordinate search algorithm and calculates the current load center position based on the analysis step time and vehicle speed. The platooning cycle time is calculated based on the number of vehicles n, vehicle speed v, distance between vehicles x, and lateral offset y. The cyclic loading of the platooned vehicles is achieved by setting the MOD function on the analysis step time. When the platoon consists of vehicles with different axle types, conditional statements are set in the subroutine to distinguish the load amplitude of different vehicles.

4. The method according to claim 1, characterized in that, Step S3, which involves extracting the characteristic parameters of the strain response waveform at the bottom of the asphalt layer, specifically includes: Finite element simulations were performed for different formation conditions, and the time history curves of the maximum longitudinal tensile strain at the bottom of the asphalt layer were output. The following characteristic parameters were extracted from the strain waveforms: peak strain. Time interval between peak strains of adjacent vehicles Duration of single vehicle load Amplitude attenuation rate between adjacent peak strains and the residual strain after the formation passes through. .

5. The method according to claim 1, characterized in that, Step S4 describes establishing a nonlinear fatigue damage accumulation model for formation loading that considers loading sequence and intermittent effects, including establishing a constant amplitude loading damage evolution equation based on the Chaboche nonlinear fatigue damage evolution model: ； In the formula, As a damage variable, The number of load cycles, For fatigue life under constant amplitude loading, For model parameters that depend on stress level and temperature, These are temperature-dependent model parameters.

6. The method according to claim 5, characterized in that, Step S4 also includes introducing the intermittent period influence factor. and intermittent time function Establish a modified damage evolution equation: ； In the formula, This represents the damage increment resulting from a single load cycle. For stress level, Interval time The exponentially decaying function, The material healing rate coefficient, The material's healing ability coefficient; and The fatigue-intermittent test determined that in a constant amplitude loading fatigue test, when the damage reaches a preset value... Loading stops when the insertion length is reached. After the interval period, loading continued until failure, and the fatigue life extension rate under different interval times was recorded and fitted.

7. The method according to claim 6, characterized in that, The nonlinear fatigue damage accumulation model described in step S4 uses the following recursive method for damage accumulation: (a) Initialize cumulative damage The formation passed through a counter. ; (b) For the first After the formation passes, the calculation of the nth formation is performed sequentially. Incremental damage to vehicles , , The total number of vehicles in the platoon; (c) Incremental damage to vehicles Based on the current stress level Current cumulative damage and the interval between the vehicle in front Joint decision; (d) Update cumulative damage ; (e) when Record the current time. The value is the formation fatigue life Nplatoon; otherwise, return to step (b).

8. The method according to claim 6, characterized in that, The calibration of model parameters in step S5 specifically includes: Fatigue life at each stress level was obtained through constant amplitude loading fatigue tests at at least three different stress levels. Based on the damage evolution curve, the test data at each stress level are nonlinearly fitted using the damage evolution equation to obtain the model parameters. and and establish parameters With stress level Functional relationship To characterize the nonlinear effect of loading order on damage accumulation; the healing capacity coefficient is calibrated through the fatigue-intermittent test. and healing rate coefficient .

9. The method according to claim 1, characterized in that, Step S5, which involves establishing a formation fatigue life prediction formula, specifically includes: Orthogonal experimental design was used to generate simulation condition combinations covering the range of parameter values ​​for each formation, and the fatigue life of the formation was calculated for each condition. With the fatigue life of the formation as the dependent variable and the formation parameters and pavement structure parameters as independent variables, a multivariate nonlinear regression was used to establish an explicit prediction formula in the following form: ； ； In the formula, This represents the maximum tensile strain at the bottom of the asphalt layer when a single vehicle passes over it. , , Let be the fitting constant. The exponent is the power factor; the number of vehicles in the formation parameters. Pick Vehicles, speed Pick km / h, distance between vehicles Pick m, lateral offset Pick cm.

10. The method according to claim 9, characterized in that, Step S6, which involves predicting and correcting formation fatigue life, specifically includes: Substituting the target formation's driving parameters and road structure parameters into the estimation formula, the formation's fatigue life is calculated. When there is a difference between the actual service environment temperature and the model calibration temperature, a temperature correction coefficient is introduced. The predicted results are corrected using the following formula: ； in The dynamic modulus ratio at different temperatures is used to determine the formation fatigue life. The formation fatigue life output by the method is used for the design of pavement structure thickness for dedicated lanes for autonomous driving formations, the assessment and optimization of the impact of formation driving strategies on pavement damage, and the decision on the timing of pavement preventive maintenance based on the estimated life.

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