Evaluation method of bearing capacity of roadbed and pavement structure based on high-speed deflection basin index

Abstract

The present invention discloses a method for evaluating the bearing capacity of a roadbed and pavement structure based on a high-speed deflection basin index. Based on finite element simulation, different roadbed and pavement structure parameters are set, three deflection morphological indicators under various structural parameter combinations are simulated, and a road surface deflection index database is constructed; based on the road surface deflection index database, the three deflection morphological indicators under various structural parameter combinations are used as input items, and various roadbed and pavement structure parameters are used as output items to train a neural network; based on the measured deflection data of each point, the three deflection morphological indicators are calculated and input into the neural network to obtain various roadbed and pavement structure parameters, and the bearing capacity of the roadbed and pavement structure is evaluated accordingly.

Description

Technical Field

[0001] The invention belongs to the field of road engineering, and in particular relates to a method for evaluating the bearing capacity of a roadbed and pavement structure. Background Art

[0002] The laser high-speed deflectometer is the most advanced road deflection detection device currently. It consists of a carrier vehicle, a detection control system, a Doppler laser sensor, a distance measurement system, a temperature control system and other basic parts. Its rear axle is a standard axle load of 100kN for a double-wheel set. The deflectometer travels at a high speed of 30-120km / h, applies a load to the road surface to cause the road surface to deflect. The deflection value of each point on the road surface is detected by the laser displacement sensor on the deflectometer. Generally, the laser high-speed deflectometer has a total of 6 displacement sensors, which are 100, 200, 300, 600, 900, and 1500mm away from the rear axle respectively.

[0003] There is a lack of relevant research on the application of deflection data measured by laser high-speed deflectometers. The deflection value is often used directly to evaluate the structural state of the subgrade and pavement. That is, the greater the deflection at each point, the worse the bearing capacity of the subgrade and pavement. Such evaluation methods often use the analysis and evaluation methods of the deflection data measured by traditional drop-weight deflectometers. Summary of the invention

[0004] In order to solve the technical problems mentioned in the above background technology, the present invention proposes a method for evaluating the bearing capacity of a roadbed and pavement structure based on a high-speed deflection basin index.

[0005] In order to achieve the above technical objectives, the technical solution of the present invention is:

[0006] The method for evaluating the bearing capacity of roadbed and pavement structures based on high-speed bending basin indicators includes the following steps:

[0007] (1) Based on finite element simulation, different roadbed and pavement structural parameters were set to simulate the three deflection morphological indicators of deflection peak value DP, normalized peak hysteresis area NPLA, and normalized asymmetric area difference NAAD under various structural parameter combinations, and a road surface deflection index database was constructed;

[0008] (2) Based on the road surface deflection index database, the three deflection morphological indicators of deflection peak value DP, normalized peak hysteresis area NPLA and normalized asymmetric area difference NAAD under various structural parameter combinations are used as input items, and various roadbed and pavement structural parameters are used as output items to train the neural network;

[0009] (3) Based on the measured deflection data at each point, three deflection morphology indicators, namely, deflection peak value DP, normalized peak hysteresis area NPLA, and normalized asymmetric area difference NAAD, are calculated and input into the neural network trained in step (2) to obtain various roadbed and pavement structure parameters, and the bearing capacity of the roadbed and pavement structure is evaluated based on them.

[0010] Furthermore, the subgrade and pavement structure parameters include the dynamic modulus and phase angle of the asphalt surface layer, the dynamic modulus of the cement-stabilized gravel base layer, the dynamic modulus of the subgrade, and the friction coefficient between the surface layer and the base layer; the larger the dynamic modulus of the asphalt surface layer, the smaller the phase angle of the asphalt surface layer, the larger the dynamic modulus of the cement-stabilized gravel base layer, the larger the dynamic modulus of the subgrade, and the larger the friction coefficient between the surface layer and the base layer, indicating that the bearing capacity of the subgrade and pavement structure is better.

[0011] Furthermore, the road surface deflection index database contains more than 200 groups of data, including a group of asphalt surface layer dynamic modulus and phase angle, cement stabilized gravel base dynamic modulus, roadbed dynamic modulus, friction coefficient between surface layer and base layer, and their corresponding deflection peak DP, normalized peak hysteresis area NPLA and normalized asymmetric area difference NAAD as a group of data.

[0012] Furthermore, in step (2), the data in the road surface deflection index database is imported into MATLAB to train the BP neural network.

[0013] Furthermore, in step (3), before calculating the three deflection morphology indices based on the measured deflection data at each point, it is necessary to perform a road surface high-speed deflection basin curve fitting.

[0014] Furthermore, the method for fitting the high-speed bend basin curve of the road surface is as follows:

[0015] (A) High-speed deflection data marking: There are N laser displacement sensors installed on the laser high-speed deflectometer. The distance between the i-th laser displacement sensor and the deflectometer and the standard axis used for loading is denoted as x i , the deflection values ​​measured by each laser displacement sensor are recorded as y i , 1≤i≤N; thus, a complete set of laser high-speed deflection data is recorded as (x1,y1),(x2,y2)......(x N ,y N );

[0016] (B) Taking the foot of the perpendicular line drawn by the standard axial road surface of the high-speed deflection meter as the origin, the above points are plotted in a plane rectangular coordinate system, and the road surface high-speed deflection basin curve d(x) is obtained by fitting the first n terms of the trigonometric series.

[0017] Furthermore, n∈[8,15].

[0018] Furthermore, the method for calculating the deflection peak DP is as follows:

[0019] Solve the equation d′(x p )=0 to obtain the horizontal coordinate x corresponding to the peak point of deflection p , substitute into the formula DP = d(x p ) calculates the peak deflection DP, where d′(x p ) is d(x p ) is the first-order derivative of .

[0020] Furthermore, the method for calculating the normalized peak hysteresis area NPLA is as follows:

[0021] The left and right intersection points between the high-speed bend curve d(x) and the horizontal axis of the plane rectangular coordinate system are denoted as x l and x r , according to the formula Calculate the normalized peak hysteresis area NPLA.

[0022] Furthermore, the method for calculating the normalized asymmetric area difference NAAD is as follows:

[0023] (a) In a rectangular coordinate system, the vertical line x = x p The symmetry axis is the bending basin curve d(x) on the left side of the axis, which is symmetrical to the right side, that is, d(2x p -x);

[0024] (b) According to the formula Calculate the horizontal coordinate x0 of the midpoint of the high-speed bending basin;

[0025] (c) According to the formula The normalized asymmetry area difference NAAD was calculated.

[0026] The beneficial effects brought by adopting the above technical solution are:

[0027] The biggest difference between the high-speed deflection basin and the traditional drop-hammer deflection basin is that its deflection peak lags behind the wheel load, and the deflection basin is asymmetric on the left and right sides of the peak. Based on this feature, the present invention creatively proposes the normalized peak hysteresis area NPLA index and the normalized asymmetric area difference NAAD index based on the asphalt pavement laser high-speed deflection basin to obtain the dynamic modulus of the asphalt surface layer, cement-stabilized macadam, and roadbed, and the adhesion state between the surface layer and the base layer, thereby evaluating the bearing capacity of the roadbed and pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the high-speed bending basin fitting curve and related parameters;

[0029] Figure 2This is a schematic diagram of the deflection basin and related parameters at point A measured by a high-speed laser deflectometer on the road surface;

[0030] Figure 3 This is a schematic diagram of the deflection basin at point B and related parameters measured by the road surface high-speed laser deflection meter. DETAILED DESCRIPTION

[0031] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0032] The present invention proposes a method for evaluating the bearing capacity of a roadbed and pavement structure based on a high-speed deflection basin index, comprising the following steps:

[0033] (1) Based on finite element simulation, different roadbed and pavement structural parameters were set to simulate the three deflection morphological indicators of deflection peak value DP, normalized peak hysteresis area NPLA, and normalized asymmetric area difference NAAD under various structural parameter combinations, and a road surface deflection index database was constructed;

[0034] (2) Based on the road surface deflection index database, the three deflection morphological indicators of deflection peak value DP, normalized peak hysteresis area NPLA and normalized asymmetric area difference NAAD under various structural parameter combinations are used as input items, and various roadbed and pavement structural parameters are used as output items to train the neural network;

[0035] (3) Based on the measured deflection data at each point, three deflection morphology indicators, namely, deflection peak value DP, normalized peak hysteresis area NPLA, and normalized asymmetric area difference NAAD, are calculated and input into the neural network trained in step (2) to obtain various roadbed and pavement structure parameters, and the bearing capacity of the roadbed and pavement structure is evaluated based on them.

[0036] Preferably, the subgrade and pavement structure parameters include the dynamic modulus and phase angle of the asphalt surface layer, the dynamic modulus of the cement-stabilized gravel base layer, the dynamic modulus of the subgrade, and the friction coefficient between the surface layer and the base layer; the larger the dynamic modulus of the asphalt surface layer, the smaller the phase angle of the asphalt surface layer, the larger the dynamic modulus of the cement-stabilized gravel base layer, the larger the dynamic modulus of the subgrade, and the larger the friction coefficient between the surface layer and the base layer, indicating that the bearing capacity of the subgrade and pavement structure is better.

[0037] Preferably, the road surface deflection index database contains more than 200 groups of data, and a group of asphalt surface layer dynamic modulus and phase angle, cement stabilized macadam base dynamic modulus, roadbed dynamic modulus, friction coefficient between surface layer and base layer, and their corresponding deflection peak DP, normalized peak hysteresis area NPLA and normalized asymmetric area difference NAAD are taken as a group of data.

[0038] Preferably, in step (2), the data in the road surface deflection index database is imported into MATLAB to train the BP neural network.

[0039] Preferably, in step (3), before calculating the three deflection morphology indices based on the measured deflection data at each point, a road surface high-speed deflection basin curve fitting is required.

[0040] Furthermore, the method for fitting the high-speed bend basin curve of the road surface is as follows:

[0041] (A) High-speed deflection data marking: There are N laser displacement sensors installed on the laser high-speed deflectometer. The distance between the i-th laser displacement sensor and the deflectometer and the standard axis used for loading is denoted as x i , the deflection values ​​measured by each laser displacement sensor are recorded as y i , 1≤i≤N; thus, a complete set of laser high-speed deflection data is recorded as (x1,y1),(x2,y2)......(x N ,y N );

[0042] (B) Figure 1 As shown in the figure, the foot of the perpendicular line drawn by the standard axial road surface of the high-speed deflection meter is taken as the origin, and the above points are plotted in a plane rectangular coordinate system. The first n terms of the trigonometric series are used to fit the road surface high-speed deflection basin curve d(x), generally n∈[8,15].

[0043] Preferably, the method for calculating the deflection peak DP is as follows:

[0044] Solve the equation d′(x p )=0 to obtain the horizontal coordinate x corresponding to the peak point of deflection p , substitute into the formula DP = d(x p ) calculates the peak deflection DP, where d′(x p ) is d(x p ) is the first-order derivative of .

[0045] Preferably, the method for calculating the normalized peak hysteresis area NPLA is as follows:

[0046] like Figure 1 As shown in the figure, the left and right intersection points between the high-speed bend basin curve d(x) and the horizontal axis of the plane rectangular coordinate system are denoted as x l and x r , according to the formula Calculate the normalized peak hysteresis area NPLA.

[0047] Preferably, if Figure 1 As shown, the method for calculating the normalized asymmetric area difference NAAD is as follows:

[0048] (a) In a rectangular coordinate system, the vertical line x = x pThe symmetry axis is the bending basin curve d(x) on the left side of the axis, which is symmetrical to the right side, that is, d(2x p -x);

[0049] (b) According to the formula Calculate the horizontal coordinate x0 of the midpoint of the high-speed bending basin;

[0050] (c) According to the formula The normalized asymmetry area difference NAAD was calculated.

[0051] Embodiment 1:

[0052] The structure of a highway section with asphalt pavement is composed of: 18 cm asphalt mixture surface layer, 38 cm cement stabilized gravel, and fill roadbed. The laser high-speed deflection instrument performs road surface deflection detection on this section. Taking detection point A and detection point B as examples, the method of the present invention is used to calculate the relevant indicators of the high-speed deflection basins at the two road surfaces, and evaluate the structural bearing capacity of the roadbed and pavement at the two locations. The high-speed deflection basins and related parameters at A and B are shown in Figure 1. Figure 2 and Figure 3 As shown. Calculating the high-speed deflection basin related indicators at A and B, we can get: the deflection peak DP at point A is 18.98 (0.01mm), the normalized peak hysteresis area NPLA is 0.074, and the normalized asymmetric area difference NAAD is 0.0812; the deflection peak DP at point B is 16.64 (0.01mm), the normalized peak hysteresis area NPLA is 0.079, and the normalized asymmetric area difference NAAD is 0.0835. Substituting the above three indicators into the BP neural network, the following data are obtained by inversion:

[0053] For point A, the dynamic modulus of the asphalt surface layer is 7676MPa, the phase angle of the asphalt surface layer is 28.1°, the dynamic modulus of the cement stabilized macadam base is 10929MPa, the dynamic modulus of the roadbed is 87MPa, and the friction coefficient between the surface layer and the base is 0.87. For point B, the dynamic modulus of the asphalt surface layer is 7234MPa, the phase angle of the asphalt surface layer is 31.8°, the dynamic modulus of the cement stabilized macadam base is 10791MPa, the dynamic modulus of the roadbed is 101MPa, and the friction coefficient between the surface layer and the base is 0.84. According to the traditional deflection peak judgment method, it can only be judged that the overall structural bearing capacity of the roadbed and pavement at point A is lower than that at point B. According to the high-speed deflection basin morphological characteristic index and evaluation method proposed in the present invention, it can be seen that the bearing capacity of the asphalt surface layer at point A is lower than that at point B, the bearing capacity of the cement stabilized macadam base at point A and the bonding state of the surface layer base are similar to those at point B, and the bearing capacity of the roadbed at point A is better than that at point B.

[0054] The embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. Evaluation method of bearing capacity of roadbed and pavement structure based on high-speed deflection basin index, It is characterized in that The following steps are involved: (1) Based on finite element simulation, different roadbed and pavement structural parameters were set to simulate the three deflection morphological indicators of deflection peak value DP, normalized peak hysteresis area NPLA, and normalized asymmetric area difference NAAD under various structural parameter combinations, and a road surface deflection index database was constructed; (2) Based on the road surface deflection index database, the three deflection morphological indicators of deflection peak value DP, normalized peak hysteresis area NPLA and normalized asymmetric area difference NAAD under various structural parameter combinations are used as input items, and various roadbed and pavement structural parameters are used as output items to train the neural network; (3) Based on the measured deflection data of each point, three deflection morphology indicators, namely, deflection peak value DP, normalized peak hysteresis area NPLA, and normalized asymmetric area difference NAAD, are calculated and input into the neural network trained in step (2) to obtain various roadbed and pavement structure parameters, and the bearing capacity of the roadbed and pavement structure is evaluated based on the parameters; The roadbed and pavement structural parameters include the dynamic modulus and phase angle of the asphalt surface layer, the dynamic modulus of the cement-stabilized crushed stone base layer, the dynamic modulus of the roadbed, and the friction coefficient between the surface layer and the base layer; The method for calculating the peak deflection DP is as follows: Solve the equation d′(x p )=0 to obtain the horizontal coordinate x corresponding to the peak point of deflection p , substitute into the formula DP = d(x p ) calculates the peak deflection DP, where d′(x p ) is d(x p ) first-order derivative; The normalized peak hysteresis area NPLA is calculated as follows: The left and right intersection points between the high-speed bend basin curve d(x) and the horizontal axis of the plane rectangular coordinate system are denoted as x l and x r , according to the formula Calculate the normalized peak hysteresis area NPLA; The method for calculating the normalized asymmetry area difference NAAD is as follows: (a) In a rectangular coordinate system, the vertical line x = x p The symmetry axis is the bending basin curve d(x) on the left side of the axis, which is symmetrical to the right side, that is, d(2x p -x); (b) According to the formula Calculate the horizontal coordinate x of the midpoint of the high-speed bending basin 0 ; (c) According to the formula The normalized asymmetry area difference NAAD was calculated.

2. According to the method for evaluating the bearing capacity of roadbed and pavement structure based on high-speed deflection basin index according to claim 1, Characterized in that asphalt The larger the dynamic modulus of the surface layer, the smaller the phase angle of the asphalt surface layer, the larger the dynamic modulus of the cement-stabilized gravel base layer, the larger the dynamic modulus of the roadbed, and the greater the friction coefficient between the surface layer and the base layer, indicating that the bearing capacity of the roadbed and pavement structure is better.

3. According to the method for evaluating the bearing capacity of roadbed and pavement structure based on high-speed deflection basin index as claimed in claim 2, It is characterized in that The road surface deflection index database contains more than 200 groups of data, including a group of asphalt surface layer dynamic modulus and phase angle, cement stabilized gravel base dynamic modulus, roadbed dynamic modulus, friction coefficient between surface layer and base layer, and their corresponding deflection peak value DP, normalized peak hysteresis area NPLA and normalized asymmetric area difference NAAD as a group of data.

4. According to the method for evaluating the bearing capacity of roadbed and pavement structure based on high-speed deflection basin index as claimed in claim 1, It is characterized in that In step (2), the data in the road surface deflection index database is imported into MATLAB to train the BP neural network.

5. According to the method for evaluating the bearing capacity of roadbed and pavement structure based on high-speed deflection basin index according to claim 1, It is characterized in that In step (3), before calculating the three deflection morphology indices based on the measured deflection data at each point, it is necessary to fit the road surface high-speed deflection basin curve.

6. According to the method for evaluating the bearing capacity of roadbed and pavement structure based on high-speed deflection basin index according to claim 5, It is characterized in that The method for fitting the high-speed bend basin curve on the road surface is as follows: (A) High-speed deflection data marking: There are N laser displacement sensors installed on the laser high-speed deflectometer. The distance between the i-th laser displacement sensor and the deflectometer and the standard axis used for loading is denoted as x i , the deflection values ​​measured by each laser displacement sensor are recorded as y i , 1≤i≤N; thus, a complete set of laser high-speed deflection data is recorded as (x 1 ,y 1 ),(x 2 ,y 2 )......(x N ,y N ); (B) Taking the foot of the perpendicular line drawn by the standard axial road surface of the high-speed deflection meter as the origin, the above points are plotted in a plane rectangular coordinate system, and the road surface high-speed deflection basin curve d(x) is obtained by fitting the first n terms of the trigonometric series.

7. According to claim 6, the method for evaluating the bearing capacity of roadbed and pavement structure based on high-speed deflection basin index, It is characterized in that n∈[8,15]。

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