A method for modulus inversion of thin surfacing asphalt pavement structure

Abstract

The application discloses a kind of for thin surface layer asphalt pavement structure modulus inversion method, it is related to road engineering technical field.The related information and initial population of the present application are obtained, the modulus and speed of particle in population are initialized, the interlayer stiffness coordination coefficient calculation method is determined, interlayer stiffness coordination coefficient value is calculated using pavement structure information and road surface temperature, the fitness of each particle in population is evaluated in combination with pavement structure information and road surface deflection basin information, the fitness of optimal individual in population and corresponding modulus are determined, initial temperature is calculated, the fitness of each particle is compared with individual historical optimal fitness, individual historical optimal and group historical optimal are updated, the group learning object of particle is determined and the velocity and modulus of each particle are updated, the fitness of each particle is recalculated, optimal solution accuracy is updated again, and each structure layer modulus is output.The present application can accurately obtain thin surface layer asphalt pavement structure inversion solution.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and in particular to a method for inverting the structural modulus of thin-layer asphalt pavement. Background Technology

[0002] Inverting structural layer modulus based on surface deflection basin data measured by a falling weight deflectometer (FWD) is a widely used and important method for evaluating and predicting the service capacity of pavement structures both domestically and internationally, and it has been extensively applied throughout the entire pavement structure lifecycle. During the pavement design and construction phases, the deflection values ​​at the load center point and the complete deflection basin can be used to invert the modulus and refine various design parameters of the pavement structure. During the service phase, the structural layer moduli derived from the overall deflection basin data can be used to determine the structural bearing capacity, thus providing guidance on the optimal timing for preventative maintenance and the reconstruction and repair of the pavement structure.

[0003] Currently, several mature pavement structure inversion analysis theories and methods with high inversion accuracy have been developed based on this technology. However, these modulus inversion methods have good inversion accuracy and stability when applied to pavement structures with thicker asphalt pavement layers; for thinner asphalt pavement structures, the accuracy of the structural layer modulus inversion results is very low and the variability is large. Furthermore, thin-layer asphalt pavement structures account for a high proportion of my country's actual road network, which significantly limits the application of current modulus inversion analysis methods and reduces the accuracy of pavement structure bearing capacity evaluation.

[0004] With the rise of artificial intelligence technology, intelligent technologies such as genetic algorithms, ant colony algorithms, and artificial neural networks, which can solve nonlinear problems, are gradually being applied to the modulus inversion analysis of pavement structures. There is an urgent need for an inversion analysis method that can realize the analysis, prediction, and intelligent optimization of the bearing capacity of pavement structures, thereby improving the evaluation and prediction level of the service capacity of pavement structures in my country.

[0005] Therefore, proposing a method for inverting the structural modulus of thin-layer asphalt pavement to address the problems of uncertainty, lack of generality, and inability to reconcile with actual results in existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method for inverting the structural modulus of thin-layer asphalt pavement, which can achieve the effect of inferring the irradiation direction.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for inverting the structural modulus of thin-layer asphalt pavements includes the following steps:

[0009] S1. Obtain pavement structure information, road surface deflection basin information, road surface temperature, load levels, bearing plate radius, and initial population;

[0010] S2. Initialize the modulus and velocity of particles in the population, determine the calculation method of the interlayer stiffness coordination coefficient, and calculate the value of the interlayer stiffness coordination coefficient using the pavement structure information and road surface temperature;

[0011] S3. Evaluate the fitness of each particle in the population according to the value of the interlayer stiffness coordination coefficient combined with the pavement structure information and road surface deflection basin information, determine the optimal individual fitness and the corresponding modulus in the population, and calculate the initial temperature;

[0012] S4. Compare the fitness of each particle with the individual historical optimal fitness, and update the individual historical optimal and the group historical optimal;

[0013] S5. Determine the group learning object of the particles, and update the velocity and modulus of each particle;

[0014] S6. Recalculate the fitness of each particle, update the individual historical optimal and the group historical optimal again, reach the optimal solution accuracy, and output the modulus of each structural layer.

[0015] For the above method, optionally, in S1, the road surface deflection information is the deflection value D measured by sensors at different radial distances based on the FWD detection device i .

[0016] For the above method, optionally, in S2, initialize the modulus and velocity of particles in the population, the interlayer stiffness coordination coefficient is expressed as K, and the expression is:

[0017] K = 1779ln(h) + 3.995T 2 - 225T;

[0018] Where, h is the thickness of the asphalt layer, in cm; T is the road surface temperature, in °C.

[0019] For the above method, optionally, in S3, the fitness p(i) of an individual is the sum of the squares of the relative error percentages of the deflection basin, and determine that the optimal individual fitness pBest in the population is the minimum value of p(i).

[0020] For the above method, optionally, in S4, updating the individual historical optimal and the group historical optimal includes: comparing the fitness of each particle with the individual historical optimal fitness, if p(i) < pBest, then update the individual historical optimal, otherwise accept it with a certain probability; if there is an individual fitness less than the group historical optimal value, then update the group historical optimal value, otherwise do not update.

[0021] Optionally, in the above method, the group learning object for determining the individual in S5 can be selected from the historical best values ​​of all individuals using a roulette wheel strategy.

[0022] Optionally, the above method may also include step S4 when the optimal solution accuracy cannot be achieved.

[0023] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method for inverting the structural modulus of thin-layer asphalt pavement, which has the following beneficial effects: 1) The method of the present invention takes into account that the reason for the inaccuracy of the structural modulus inversion results of thin-layer asphalt pavement is that when the surface layer is thin, there is a poor coordination ability between the surface layer and the base layer. Therefore, by adding an interlayer stiffness coordination coefficient to characterize the actual coordination ability between the surface layer and the base layer, the structural inversion solution of thin-layer asphalt pavement is more accurate; 2) The interlayer stiffness coordination coefficient of the present invention can be dynamically adjusted with the changes of pavement structure and temperature to invert the structural layer modulus, which is more reasonable than simply setting the surface layer modulus to a fixed value to invert the results of other structural layer moduli. Attached Figure Description

[0024] 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 This is a flowchart of a method for inverting the structural modulus of thin-layer asphalt pavement disclosed in this invention. Detailed Implementation

[0026] 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.

[0027] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. 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. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0028] Reference Figure 1 As shown, this invention discloses a method for inverting the structural modulus of thin-layer asphalt pavements, comprising the following steps:

[0029] S1. Obtain pavement structure information, pavement surface deflection basin information, pavement surface temperature, load level, bearing plate radius and initial population;

[0030] S2. Initialize the modulus and velocity of particles in the population, determine the calculation method of interlayer stiffness coordination coefficient, and calculate the value of interlayer stiffness coordination coefficient using pavement structure information and pavement surface temperature.

[0031] S3. Evaluate the fitness of each particle in the population based on the interlayer stiffness compatibility coefficient value, combined with pavement structure information and pavement surface deflection basin information, determine the fitness of the optimal individual in the population and its corresponding modulus, and calculate the initial temperature.

[0032] S4. Compare the fitness of each particle with its individual historical best fitness, and update the individual historical best and the group historical best.

[0033] S5. Determine the group learning objects of the particles and update the velocity and modulus of each particle;

[0034] S6. Recalculate the fitness of each particle, update the individual historical best and the group historical best again, achieve the accuracy of the optimal solution, and output the modulus of each structural layer.

[0035] Furthermore, in the initial population, the population size is m, and the position (modulus) and velocity of the i-th particle are x and x, respectively. (i，j) and v (i，j) The asphalt layer thickness is between 4-20cm, and the road surface temperature is between 0-50℃.

[0036] Furthermore, the information on pavement structure and pavement surface deflection basin is compiled as shown in Tables 1 and 2.

[0037] Table 1. Information on the Structure of Thin Asphalt Pavement

[0038] Layer Material Thickness / cm Poisson's ratio 1 Material1 <![CDATA[h1]]> <![CDATA[μ1]]> 2 Material2 <![CDATA[h2]]> <![CDATA[μ2]]> 3 Material3 <![CDATA[h3]]> <![CDATA[μ3]]> ... ... ... ...

[0039] Table 2 Road Surface Deflection Basin Information Table

[0040]

[0041] Furthermore, the road surface deflection information in S1 is the deflection value D measured by the sensor at different radial distances based on the FWD detection device i .

[0042] Furthermore, in S2, the modulus and velocity of the particles in the initial population are initialized, and the interlayer stiffness coordination coefficient is denoted as K, and the expression is

[0043] K = 1779ln(h) + 3.995T 2 - 225T;

[0044] where h is the thickness of the asphalt layer in cm; T is the road surface temperature in °C.

[0045] Furthermore, in S3, the fitness p(i) of an individual is the sum of the squares of the relative error percentages of the deflection basin, and the optimal individual fitness pBest in the population is determined as the minimum value of p(i).

[0046] Specifically, the expression of p(i) is:

[0047]

[0048] where n is the number of measurement points of the road surface deflection basin, w i is the weight of the i-th measurement point, is the theoretical deflection value of the i-th measurement point, is the measured deflection value of the i-th measurement point.

[0049] The expression of the initial temperature T0 is: T0 = pBest / ln(5).

[0050] Furthermore, in S4, updating the individual historical optimum and the group historical optimum includes: comparing the fitness of each particle with the individual historical optimum fitness. If p(i) < pBest, then update the individual historical optimum, otherwise accept it with a certain probability; if there is an individual fitness less than the group historical optimum value, then update the group historical optimum value, otherwise do not update.

[0051] Furthermore, in S5, the group learning object of an individual is determined by using the roulette wheel strategy to select from all the individual historical optimum values.

[0052] Furthermore, update the velocity v i，j (t + 1) and the modulus x i，j(t+1), the expressions are as follows:

[0053]

[0054] x i,j (t+1)=x i,j (t)+v i,j (t+1),

[0055]

[0056] c = c1 + c2,

[0057] Where c1 and c2 are learning factors for themselves and the population, with a value of 3; γ1 and γ2 are random numbers in the range [0,1].

[0058] Furthermore, S6 also includes returning to step S4 when the optimal solution accuracy cannot be achieved.

[0059] Specifically, if the accuracy of the optimal solution is not achieved, a cooling-down operation is required. The cooling-down expression is as follows:

[0060] T k =T0a k ,

[0061] Among them, T k Let α be the simulated temperature for the k-th iteration, and α be the annealing constant with a value of 0.7.

[0062] In one specific embodiment, the pavement structure information of the pavement structure to be evaluated is shown in Table 3. Among them, layer 1 is an asphalt surface layer, layer 2 is a flexible base layer, and the Poisson's ratio distribution of the subgrade and the rigid underlying layer is 0.4 and 0.2, respectively. The deflection data of 5 measuring points were detected on this pavement structure, and the test results are shown in Table 4. The FWD applied load and the radius of the bearing plate are 50kN and 15cm, respectively. The final inversion results are shown in Table 5.

[0063] Table 3. Information on pavement structure to be evaluated

[0064] Layer Material Thickness / cm Poisson's ratio 1 Material1 11.68 0.25 2 Material2 13.72 0.35

[0065] Table 4. Surface deflection and temperature data of the road surface to be evaluated.

[0066]

[0067] Table 5 Modulus Inversion Results

[0068] Inversion results Measurement point 1 Measurement point 2 Measurement point 3 Measurement point 4 Measurement point 5 Surface modulus / MPa 2528 2360 2443 2526 1446 Base modulus / MPa 757 936 929 724 1755 Subgrade modulus / MPa 150.8 157.5 148.7 153.8 157.7

[0069] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0070] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for thin surfacing asphalt pavement structure modulus inversion, characterized in that, The method comprises the following steps: S1, obtaining pavement structure information, road surface deflection basin information, road surface temperature, load level, bearing plate radius and initial population; S2, initializing the modulus and speed of the particles in the population, determining the interlayer stiffness coordination coefficient calculation method, and calculating the interlayer stiffness coordination coefficient value using the pavement structure information and the road surface temperature; S3, evaluating the fitness of each particle in the population according to the interlayer stiffness coordination coefficient value combined with the pavement structure information and the road surface deflection basin information, determining the optimal individual fitness and the corresponding modulus in the population, and calculating the initial temperature; S4, comparing the fitness of each particle with the individual historical optimal fitness, updating the individual historical optimal and the group historical optimal; S5, determining the group learning object of the particles, updating the speed and modulus of each particle; S6, recalculating the fitness of each particle, updating the individual historical optimal and the group historical optimal again, reaching the optimal solution accuracy, and outputting the modulus of each structure layer; The modulus and velocity of the particles in the initialized population in S2 are initialized, and the interlayer stiffness coordination coefficient is expressed as K The expression is: K =1779 ln ( h )+3.995 T 2 -225 T ； wherein, h is the asphalt layer thickness in cm; T is the road surface temperature in °C; Fitness of individuals in S3 p ( i ) is the relative percentage error square sum of the deflection basin, and the fitness of the optimal individual in the population is determined pBest is p ( i ) is the minimum value of p i ) Expression is:​ ， wherein, n is the number of measuring points of the road table deflection basin, w i is the weight of the i th measuring point, is the theoretical deflection value of the i th measuring point, is the measured deflection value of the i th measuring point, Initial temperature T 0 Expression is: T 0= pBest / ln (5).

2. The method for thin surfacing asphalt pavement structure modulus inversion according to claim 1, characterized in that, The deflection information of the road table in S1 is the deflection value measured by the sensors of the FWD detection device at different radial distances D i .

3. The method for thin surfacing asphalt pavement structure modulus inversion according to claim 1, characterized in that, The updating of the individual historical optimum and the group historical optimum in S4 includes: comparing the fitness of each particle with the individual historical optimum fitness, if p ( i )< pBest , the individual historical optimum is updated, otherwise it is accepted with a certain probability; if there is an individual fitness less than the group historical optimum value, the group historical optimum value is updated, otherwise it is not updated.

4. The method for thin surfacing asphalt pavement structure modulus inversion according to claim 1, characterized in that, The group learning object of the individual in S5 is selected from all individual historical optimal values by using a roulette strategy.

5. The method for thin surfacing asphalt pavement structure modulus inversion according to claim 1, characterized in that, S6 further comprises returning to step S4 when the optimal solution accuracy cannot be reached.

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