Inversion method of viscoelastic parameters of asphalt layer based on embedded sensor data
By laying sensors at the bottom of the asphalt layer on the pavement and using the time-frequency domain theoretical relationship, the problem of difficulty in inversion of the asphalt layer in the existing technology is solved, real-time evaluation and simple and efficient inversion of the viscoelastic parameter are achieved, and pavement monitoring is promoted Development of technology.
Patent Information
- Application Number
- CN202311201248.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-18
- Publication Date
- 2024-12-31
- Estimated Expiration
- 2043-09-18
AI Technical Summary
The existing non-destructive testing technology of pavement is difficult to invert and evaluate the viscoelastic parameters of asphalt layer in real time, and it is difficult to obtain convergence and unique solutions for external testing.
Three-way strain sensors and vertical stress sensors are arranged at the bottom of the asphalt layer of the pavement, and the strain and stress time course curves are obtained through dynamic loads, and the equivalent elastic modulus and viscoelastic parameter frequency domain feature functions are calculated using the time-frequency domain theoretical relationship. The optimization algorithm iteratively adjusts the viscoelastic model to invert viscoelastic parameters.
Real-time evaluation of the asphalt layer viscoelastic parameters is achieved, which is simple and practical, reduces detection costs, broadens the functions of pavement monitoring technology, and promotes the asphalt layer service performance evaluation and scientific maintenance decisions.
Smart Images
Figure CN117252005B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to an asphalt layer viscoelastic parameter inversion method based on embedded sensor data, and belongs to the technical field of pavement monitoring. Background Art
[0002] Asphalt layer material is a typical viscoelastic material with extremely complex mechanical behavior. Understanding the evolution law of asphalt layer viscoelastic parameters under external complex loads and environments is of great guiding significance for the evaluation of asphalt layer service performance and scientific maintenance decision-making.
[0003] In past studies and applications, researchers mostly used external nondestructive testing technologies such as drop weight deflectometers and stress wave testers to detect the viscoelastic parameters of asphalt layers. However, the formulas of asphalt layer viscoelastic models (such as Burgers model, generalized Maxwell model, 2S2P1D fractional order model, MFZM fractional order model, etc.) are complex and have many parameters. It is difficult for commonly used external nondestructive testing technologies to obtain the convergence and unique solution of the viscoelastic parameters of asphalt layers. In recent years, by arranging stress and strain sensors inside the pavement to obtain the internal mechanical response of the pavement, high-quality data can be provided for the inversion of the mechanical parameters of each layer of the pavement. By optimizing the sensor layout scheme at each layer of the pavement, researchers can realize the inversion of the elastic modulus and traffic axle load of each layer of the pavement, but there is currently no inversion method for the viscoelastic parameters of the asphalt layer based on embedded sensor data. Summary of the invention
[0004] In view of the problem that existing pavement non-destructive testing and monitoring technologies are difficult to perform real-time inversion evaluation of viscoelastic parameters of asphalt layers, the present invention provides an asphalt layer viscoelastic parameter inversion method based on embedded sensor data.
[0005] The present invention provides an asphalt layer viscoelastic parameter inversion method based on embedded sensor data, comprising:
[0006] At the bottom of the asphalt layer of the road surface, three-dimensional strain sensors and vertical stress sensors are arranged;
[0007] Apply dynamic load on the pavement surface, obtain the triaxial strain time-history curve of the bottom of the asphalt layer by the triaxial strain sensor, and obtain the vertical stress time-history curve of the bottom of the asphalt layer by the vertical stress sensor;
[0008] The triaxial strain amplitude of the asphalt layer bottom is obtained based on the triaxial strain time-history curve of the asphalt layer bottom; the vertical stress amplitude of the asphalt layer bottom is obtained based on the vertical stress time-history curve of the asphalt layer bottom; the equivalent elastic modulus of the asphalt layer is calculated using the time-domain theoretical relationship between stress and strain;
[0009] The three-dimensional strain time-history curve at the bottom of the asphalt layer and the vertical stress time-history curve at the bottom of the asphalt layer are transformed from the time domain to the frequency domain through Fourier transform to obtain the frequency spectrum of the three-dimensional strain time-history curve and the frequency spectrum of the vertical stress time-history curve; the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer is calculated using the frequency domain theoretical relationship between stress and strain;
[0010] The viscoelastic parameters of the asphalt layer are inverted by combining the equivalent elastic modulus of the asphalt layer and the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer.
[0011] According to the inversion method of viscoelastic parameters of asphalt layer based on embedded sensor data of the present invention, the method of inverting viscoelastic parameters of asphalt layer is:
[0012] A viscoelastic model is selected, and the equivalent elastic modulus of the asphalt layer and the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer are taken as the model input. The viscoelastic parameters of the asphalt layer are calculated using the existing optimization algorithm as the model output. The viscoelastic parameters of the asphalt layer of the model are iteratively adjusted to make the frequency spectrum of the viscoelastic model consistent with the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer, and the final viscoelastic parameters of the asphalt layer are obtained by inversion.
[0013] According to the inversion method of viscoelastic parameters of asphalt layer based on embedded sensor data of the present invention, the calculation method of equivalent elastic modulus of asphalt layer is:
[0014]
[0015] In the formula is the equivalent elastic modulus of the asphalt layer, μ is the Poisson’s ratio of the asphalt layer material, is the vertical stress amplitude at the bottom of the asphalt layer, is the longitudinal strain amplitude at the bottom of the asphalt layer, is the transverse strain amplitude at the bottom of the asphalt layer, is the vertical strain amplitude at the bottom of the asphalt layer.
[0016] According to the inversion method of viscoelastic parameters of asphalt layer based on embedded sensor data of the present invention, the calculation method of the frequency domain characteristic function of the viscoelastic parameters of asphalt layer is:
[0017]
[0018] Where E(iω) represents the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer, ω is the frequency, and i is a complex number; is the frequency spectrum of the vertical stress time history curve, is the frequency spectrum of the longitudinal strain time history curve, is the frequency spectrum of the transverse strain time history curve, is the frequency spectrum of the vertical strain time history curve.
[0019] According to the asphalt layer viscoelastic parameter inversion method based on embedded sensor data of the present invention, the dynamic load is a sinusoidal load, an impact load or a vehicle moving load.
[0020] According to the inversion method of viscoelastic parameters of asphalt layers based on embedded sensor data of the present invention, the viscoelastic model is a Burgers model, a generalized Maxwell model, a 2S2P1D fractional order model or a MFZM fractional order model.
[0021] According to the inversion method of viscoelastic parameters of asphalt layers based on embedded sensor data of the present invention, existing optimization algorithms are Newton's method, pattern search, genetic algorithm, particle swarm algorithm or agent model method.
[0022] According to the inversion method of viscoelastic parameters of asphalt layers based on embedded sensor data of the present invention, the value range of Poisson's ratio μ of the asphalt layer material is 0.25-0.4.
[0023] Beneficial effects of the present invention: The method of the present invention obtains the measured mechanical response curve data through a three-dimensional strain sensor and a vertical stress sensor embedded in the bottom of the asphalt layer, and proposes an inversion method for the viscoelastic parameters of the asphalt layer material based on the time-frequency domain theoretical relationship between stress and strain; the inversion method of the present invention is simple and practical, and can evaluate the evolution law of the viscoelastic parameters of the asphalt layer in real time, thereby promoting the development of pavement monitoring technology.
[0024] The method of the present invention obtains the frequency domain characteristic function characteristics of the equivalent elastic modulus and viscoelastic parameters of the asphalt layer through the theoretical relationship between the three-dimensional strain and the vertical stress, which is used for the inversion of the viscoelastic parameters of the asphalt layer. The formula is simple and convenient, and the physical meaning is clear; the sensors embedded in the road surface only need three-dimensional strain sensors and vertical stress sensors, and the layout requirements are simple and the cost is low; the evolution law of the viscoelastic parameters of the asphalt layer can be evaluated in real time, which can promote the development of pavement monitoring technology. The present invention further broadens the function of pavement monitoring technology, provides an effective means for the inversion evaluation of the viscoelastic parameters of the asphalt layer, and has important guiding significance for the evaluation of the service performance of the asphalt layer and scientific maintenance decision-making. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 is a flow chart of the inversion method of viscoelastic parameters of asphalt layer based on embedded sensor data according to the present invention;
[0026] Figure 2 It is a schematic diagram of the sensors embedded in the road surface and the applied loads;
[0027] Figure 3 is a schematic diagram of a vertical stress time history curve in a specific embodiment;
[0028] Figure 4 Schematic diagram of a three-dimensional strain time history curve in a specific embodiment;
[0029] Figure 5 : is a schematic diagram of the frequency domain characteristic function of the viscoelastic parameter of the asphalt layer in a specific embodiment; in the figure, Real represents the real part of the frequency domain characteristic function of the viscoelastic parameter of the asphalt layer, and Imag represents the imaginary part of the frequency domain characteristic function of the viscoelastic parameter of the asphalt layer;
[0030] Figure 6 Schematic diagram of the MFZM fractional-order model used in the specific embodiment. DETAILED DESCRIPTION
[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0032] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.
[0033] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.
[0034] Specific implementation method 1. Combination Figure 1 and Figure 2 As shown, the present invention provides a method for inverting viscoelastic parameters of asphalt layers based on embedded sensor data, comprising:
[0035] At the bottom of the asphalt layer of the road surface, three-dimensional strain sensors and vertical stress sensors are arranged;
[0036] Apply dynamic load on the pavement surface, obtain the triaxial strain time-history curve of the bottom of the asphalt layer by the triaxial strain sensor, and obtain the vertical stress time-history curve of the bottom of the asphalt layer by the vertical stress sensor;
[0037] The triaxial strain amplitude of the asphalt layer bottom is obtained based on the triaxial strain time-history curve of the asphalt layer bottom; the vertical stress amplitude of the asphalt layer bottom is obtained based on the vertical stress time-history curve of the asphalt layer bottom; the equivalent elastic modulus of the asphalt layer is calculated using the time-domain theoretical relationship between stress and strain;
[0038] The three-dimensional strain time-history curve at the bottom of the asphalt layer and the vertical stress time-history curve at the bottom of the asphalt layer are transformed from the time domain to the frequency domain through Fourier transform to obtain the frequency spectrum of the three-dimensional strain time-history curve and the frequency spectrum of the vertical stress time-history curve; the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer is calculated using the frequency domain theoretical relationship between stress and strain;
[0039] The viscoelastic parameters of the asphalt layer are inverted by combining the equivalent elastic modulus of the asphalt layer and the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer.
[0040] Further, combined with Figure 1 As shown in Figure 2, the method for inverting the viscoelastic parameters of the asphalt layer is:
[0041] A viscoelastic model is selected, and the equivalent elastic modulus of the asphalt layer and the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer are taken as the model input. The viscoelastic parameters of the asphalt layer are calculated using the existing optimization algorithm as the model output. The viscoelastic parameters of the asphalt layer of the model are iteratively adjusted to make the frequency spectrum of the viscoelastic model consistent with the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer, and the final viscoelastic parameters of the asphalt layer are obtained by inversion.
[0042] In this embodiment, the three-axis strain sensor is used to obtain the time history curves of longitudinal strain, transverse strain and vertical strain under the external load, which can be expressed as ε x (t),ε y (t),ε z (t); The vertical stress sensor is used to obtain the vertical stress time history curve σ under the external load. z (t); t is time; the corresponding amplitude is expressed as and
[0043] The calculation method of the equivalent elastic modulus of the asphalt layer is:
[0044]
[0045] In the formula is the equivalent elastic modulus of the asphalt layer, μ is the Poisson’s ratio of the asphalt layer material, is the vertical stress amplitude at the bottom of the asphalt layer, is the longitudinal strain amplitude at the bottom of the asphalt layer, is the transverse strain amplitude at the bottom of the asphalt layer, is the vertical strain amplitude at the bottom of the asphalt layer.
[0046] The calculation method of the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer is:
[0047]
[0048] Where E(iω) represents the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer, ω is the frequency, and i is a complex number; is the frequency spectrum of the vertical stress time history curve, is the frequency spectrum of the longitudinal strain time history curve, is the frequency spectrum of the transverse strain time history curve, is the frequency spectrum of the vertical strain time history curve.
[0049] As examples, the dynamic load is a sinusoidal load, an impact load or a vehicle movement load.
[0050] As an example, the viscoelastic model is a Burgers model, a generalized Maxwell model, a 2S2P1D fractional order model, or a MFZM fractional order model.
[0051] As examples, existing optimization algorithms include, but are not limited to, Newton's method, pattern search, genetic algorithm, particle swarm algorithm, and agent model method.
[0052] According to experience, the Poisson's ratio μ of asphalt layer materials generally ranges from 0.25 to 0.4.
[0053] Specific embodiment: Combination Figure 2 Taking a typical asphalt pavement structure as an example, a three-dimensional strain sensor and a vertical stress sensor are arranged at the bottom of the asphalt layer. The layers of the typical asphalt pavement structure are asphalt layer, base layer and roadbed from top to bottom, with thicknesses of 0.2, 0.38, and +∞m, respectively. The Poisson's ratios of the layers are 0.3, 0.3, and 0.4 from top to bottom, and the densities of the layers are 2400, 2100, and 1900 kg / m from top to bottom. 3 The traffic load is in the form of a double-circle single-wheel standard axle load, with an axle load of 0.7 MPa, a load circle radius of 0.1065 m, a double-circle spacing of 0.3195 m, and a moving speed of 10 m / s. One of the wheel tracks of the traffic load is just above the sensor. The asphalt layer viscoelastic model uses the MFZM fractional order model, such as Figure 6 shown. Figure 6 Where E1 is the first spring elastic modulus, E2 is the second spring elastic modulus, η1 is the first fractional viscosity, η2 is the second fractional viscosity, α1 is the first fractional order, and α2 is the second fractional order.
[0054] Combination Figure 1 The specific inversion process is as follows: First, the time history curves of longitudinal strain, transverse strain, vertical strain and vertical stress are measured by the three-axis strain sensor and the vertical stress sensor, such as Figure 3 and Figure 4 As shown in Figure 2, the corresponding longitudinal strain, transverse strain, vertical strain and vertical stress amplitude are 6.5με, 2.2με, -17.2με and -199.0kPa respectively. According to the time domain theoretical relationship between stress and strain, the equivalent elastic modulus of the asphalt layer is calculated to be 10.973.5MPa.
[0055] Through Fourier transform, the time history curves of longitudinal strain, transverse strain, vertical strain and vertical stress are transformed into the frequency domain, and the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer is calculated using the frequency domain theoretical relationship of stress and strain, such as Figure 5 shown.
[0056] Finally, based on the particle swarm algorithm, with the equivalent elastic modulus of the asphalt layer and the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer as input and the viscoelastic parameters of the asphalt layer as output, the viscoelastic parameters of the asphalt layer were iteratively adjusted to make the spectrum of the viscoelastic model consistent with the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer. The viscoelastic parameters of the asphalt layer were inverted, and E1, E2, η1, η2, α1, and α2 were 391.5 MPa, 34999.9 MPa, 15676.7, 4837.6, 0.17, and 0.47, respectively.
[0057] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with a single embodiment may be used in other described embodiments.
Claims
1. A viscoelastic parameter inversion method for asphalt layer based on embedded sensor data, characterized in that include, At the bottom of the asphalt layer of the road surface, three-dimensional strain sensors and vertical stress sensors are arranged; Apply dynamic load on the pavement surface, obtain the triaxial strain time-history curve of the bottom of the asphalt layer by the triaxial strain sensor, and obtain the vertical stress time-history curve of the bottom of the asphalt layer by the vertical stress sensor; The three-dimensional strain amplitude of the bottom of the asphalt layer is obtained based on the three-dimensional strain time history curve of the bottom of the asphalt layer; The vertical stress amplitude at the bottom of the asphalt layer is obtained based on the vertical stress time history curve at the bottom of the asphalt layer; the equivalent elastic modulus of the asphalt layer is calculated using the time domain theoretical relationship between stress and strain; The three-dimensional strain time-history curve at the bottom of the asphalt layer and the vertical stress time-history curve at the bottom of the asphalt layer are transformed from the time domain to the frequency domain through Fourier transform to obtain the frequency spectrum of the three-dimensional strain time-history curve and the frequency spectrum of the vertical stress time-history curve; the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer is calculated using the frequency domain theoretical relationship between stress and strain; The viscoelastic parameters of the asphalt layer are inverted by combining the equivalent elastic modulus of the asphalt layer and the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer.
2. The asphalt layer viscoelastic parameter inversion method based on embedded sensor data according to claim 1 is characterized in that: The method for inverting the viscoelastic parameters of the asphalt layer is: A viscoelastic model is selected, and the equivalent elastic modulus of the asphalt layer and the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer are taken as the model input. The viscoelastic parameters of the asphalt layer are calculated using the existing optimization algorithm as the model output. The viscoelastic parameters of the asphalt layer of the model are iteratively adjusted to make the frequency spectrum of the viscoelastic model consistent with the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer, and the final viscoelastic parameters of the asphalt layer are obtained by inversion.
3. The asphalt layer viscoelastic parameter inversion method based on embedded sensor data according to claim 1 or 2, characterized in that: The calculation method of the equivalent elastic modulus of the asphalt layer is: In the formula is the equivalent elastic modulus of the asphalt layer, μ is the Poisson’s ratio of the asphalt layer material, is the vertical stress amplitude at the bottom of the asphalt layer, is the longitudinal strain amplitude at the bottom of the asphalt layer, is the transverse strain amplitude at the bottom of the asphalt layer, is the vertical strain amplitude at the bottom of the asphalt layer.
4. The asphalt layer viscoelastic parameter inversion method based on embedded sensor data according to claim 3 is characterized in that: The calculation method of the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer is: Where E(iω) represents the frequency domain characteristic function of the viscoelastic parameters of the asphalt layer, ω is the frequency, and i is a complex number; is the frequency spectrum of the vertical stress time history curve, is the frequency spectrum of the longitudinal strain time history curve, is the frequency spectrum of the transverse strain time history curve, is the frequency spectrum of the vertical strain time history curve.
5. The asphalt layer viscoelastic parameter inversion method based on embedded sensor data according to claim 1 or 2, characterized in that: The dynamic load is a sinusoidal load, an impact load or a vehicle moving load.
6. The asphalt layer viscoelastic parameter inversion method based on embedded sensor data according to claim 2 is characterized in that: The viscoelastic model is the Burgers model, the generalized Maxwell model, the 2S2P1D fractional order model or the MFZM fractional order model.
7. The asphalt layer viscoelastic parameter inversion method based on embedded sensor data according to claim 2 is characterized in that: Existing optimization algorithms are Newton's method, pattern search, genetic algorithm, particle swarm algorithm or agent model method.
8. The asphalt layer viscoelastic parameter inversion method based on embedded sensor data according to claim 3 is characterized in that: The Poisson's ratio μ of the asphalt layer material ranges from 0.25 to 0.4.