All-solid waste asphalt pavement structure and material integrated design system and method

Abstract

The invention relates to the technical field of asphalt pavement design, in particular to an all-solid waste asphalt pavement structure and material integrated design system and method. The server comprises a data acquisition module used for acquiring historical traffic data and roadbed basic information corresponding to a road to be constructed; the scheme generation module is used for determining each pavement structure layer corresponding to the pavement to be paved, randomly generating a plurality of pavement structure design schemes, and preliminarily drawing up the plurality of pavement structure design schemes according to the obtained historical traffic data and roadbed basic information corresponding to the road to be constructed; and the optimization module is used for optimizing each pavement structure design scheme based on a preset improved optimization algorithm according to each corresponding pavement structure design scheme, selecting an optimal pavement structure design scheme and feeding back the optimal pavement structure design scheme to the user side.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt pavement design, and in particular to a system and method for integrated design of all-solid waste asphalt pavement structure and materials. Background Art

[0002] With the acceleration of urbanization and the continuous improvement of transportation infrastructure, highway construction has become one of the important forces to promote economic development. However, in the process of highway construction and maintenance, how to effectively utilize a large amount of construction waste and old road materials has become an urgent problem to be solved.

[0003] Traditional pavement design methods often ignore the comprehensive utilization of these solid waste materials. Even if the corresponding solid waste materials are used, the corresponding pavement structure design is mostly manually drawn up by designers, who manually set the material type and thickness of each layer of the pavement and optimize the schemes involved through limited calculations or experience. However, this method has some problems. For example, in the process of optimizing the pavement structure design, the entire pavement structure design workload is large, the manual design efficiency is low, and the optimization reliability is low. Summary of the invention

[0004] One of the purposes of the present invention is to provide a system and method for the integrated design of all-solid waste asphalt pavement structure and materials, which can achieve accurate and rapid optimization of pavement structure design during the pavement structure design process.

[0005] In order to achieve the above-mentioned purpose, a full solid waste asphalt pavement structure and material integrated design system is provided, including a service end and a user end;

[0006] The server includes:

[0007] A data acquisition module is used to acquire historical traffic data corresponding to the road to be constructed, and basic roadbed information, wherein the basic roadbed information includes roadbed soil quality information, roadbed top surface equivalent rebound modulus data, and roadbed temperature and humidity data;

[0008] A scheme generation module is used to determine the various pavement structure layers corresponding to the pavement to be paved according to the asphalt pavement design scheme corresponding to the road to be constructed, and randomly generate a number of pavement structure design schemes, wherein the pavement structure design schemes include the paving material type and paving thickness corresponding to each pavement structure layer;

[0009] It is also used to preliminarily formulate several pavement structure design plans based on the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed;

[0010] The optimization module is used to optimize each pavement structure design scheme according to the corresponding pavement structure design scheme based on the preset improved optimization algorithm, select the best pavement structure design scheme and feed back the best pavement result design scheme to the user end.

[0011] Technical principles and effects of this solution: In this solution, firstly, the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed are obtained. The basic information of the roadbed includes the roadbed soil information, the equivalent rebound modulus data of the roadbed top surface and the roadbed temperature and humidity data. The basic information of the road to be constructed is obtained to provide reliable data support for the optimization of the subsequent design scheme.

[0012] Then a preliminary pavement structure design scheme is generated. There are two ways to generate preliminary data. Specifically, one is to determine the various pavement structure layers corresponding to the pavement to be paved according to the asphalt pavement design scheme corresponding to the road to be constructed, and randomly generate several pavement structure design schemes. The pavement structure design scheme includes the paving material type and paving thickness corresponding to each pavement structure layer; the other is to preliminarily formulate several pavement structure design schemes based on the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed. The preliminary generation of the pavement structure design scheme through these two methods improves the comprehensiveness of the acquisition of preliminary data of the algorithm and greatly improves the globality of the algorithm.

[0013] Finally, according to the corresponding pavement structure design schemes, based on the preset improved optimization algorithm, each pavement structure design scheme is optimized, the optimal pavement structure design scheme is selected and the optimal pavement result design scheme is fed back to the user end. The results of the design scheme are fed back in real time and adjusted according to the user's feedback, making the design process more efficient.

[0014] This solution can achieve accurate and rapid optimization of pavement structure design during the pavement structure design process through the cooperation between the solution generation module and the optimization module.

[0015] Further, the optimization module includes:

[0016] A gray wolf initialization module is used to generate a plurality of pavement structure design schemes as corresponding gray wolf populations, wherein the gray wolf individuals of the gray wolf population correspond to a certain pavement structure design scheme;

[0017] The preliminary screening module is used to perform preliminary screening and judgment on each individual of the gray wolf population based on a preset preliminary screening strategy, to determine whether each individual gray wolf meets the corresponding conditions, and if not, the corresponding gray wolf individual is eliminated;

[0018] The fitness calculation module is used to determine the expression of the corresponding fitness function according to the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, and calculate the fitness corresponding to all gray wolf individuals through the fitness function, and find the top three gray wolves in fitness as α, β, and γ wolves respectively;

[0019] The hunting module is used to update the location of gray wolves based on the hunting behavior of the gray wolf population;

[0020] The recalculation module, after updating the position of the gray wolf, recalculates the fitness of all the gray wolves based on the fitness function and sorts them from high to low according to the fitness;

[0021] The judgment module is used to select the gray wolf with the largest fitness, and judge whether the corresponding α wolf is re-elected according to the preset alpha wolf re-election mechanism strategy. If so, the α wolf will not be updated, and only the fitness and position of the β and γ wolves will be updated. Otherwise, the fitness and position of the α, β, and γ wolves will be updated;

[0022] The output module is used to determine whether the current number of iterations reaches the preset number of iteration convergences. If not, the preliminary screening module continues to be executed. Otherwise, the corresponding position of the α wolf is output as the optimal driving path plan.

[0023] Beneficial effects: In this solution, at each iteration, each design solution is evaluated based on the preset preliminary screening strategy to see if it meets the basic technical requirements, and the design solutions that do not meet the requirements are eliminated, which improves the effectiveness of subsequent calculations and reduces unnecessary consumption of computing resources.

[0024] Then, the fitness function is defined to calculate the fitness values ​​of all gray wolf individuals. The three individuals with the highest fitness are found, and the direction of the optimal solution is clarified through quantitative evaluation to guide the subsequent search direction. By simulating the predation behavior in nature, the algorithm's search ability and ability to jump out of the local optimum are enhanced. After the position is updated, the fitness of all gray wolves is recalculated and sorted according to the fitness value. Ensure that the effect of the new position can be reflected in time after each iteration to maintain the vitality of the population.

[0025] According to the leader re-election mechanism, decide whether to update the position of wolf α, and update the positions of wolf β and wolf γ at the same time. Prevent premature convergence to the local optimum, while maintaining the relative stability of the leader individuals in the population.

[0026] Further, the hunting module includes:

[0027] A first updating module is used to update the positions of other gray wolves in the gray wolf population according to the positions of α, β, and γ wolves in the population and based on a first updating formula;

[0028] The first update formula is:

[0029] D α =|C1×X α -X|

[0030] D β =|C2×X β -X|

[0031] D γ =|C3×X γ -X|

[0032] Where, X α , X β and X γ Represent the positions of α wolf, β wolf, and γ wolf respectively, X represents the position of the current gray wolf individual; C1, C2, and C3 are position coefficient vectors, and each element value in C1, C2, and C3 is a random number between 0 and 2; D1, D2, and D3 represent the distances between α wolf, β wolf, and γ wolf and other individuals in the population respectively;

[0033] The first calculation module is used to update the initial positions X1, X2 and X3 of the gray wolf according to the second update formula based on the information feedback of the α wolf, the β wolf and the γ wolf respectively;

[0034] The second update formula is:

[0035] X1=X α -A1×D α

[0036] X2=X β -A2×D β

[0037] X3=X γ -A3×D γ

[0038] Where A1, A2, A3 are distance coefficient vectors;

[0039] The second updating module is used to update the position of the gray wolf at the t+1th iteration according to the third updating formula;

[0040] The third update formula is:

[0041]

[0042] Where X1, X2 and X3 are the updated initial positions of the gray wolf.

[0043] Furthermore, the expressions of A1, A2, and A3 are:

[0044] A b =2×a×ra

[0045] Where r is a vector of the same dimension as the distance coefficient vector, whose elements are random numbers between 0 and 1, and a is a convergence factor, whose value decreases linearly from 2 to 0 as the number of iterations increases.

[0046] Furthermore, the initial screening strategy is:

[0047] According to the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, based on the preset pavement structure verification dimensions, the dimensional values ​​corresponding to each pavement structure verification dimension corresponding to each gray wolf individual are calculated, and it is determined whether all dimensional values ​​meet the preset dimensional requirements. If so, the corresponding gray wolf individual is a feasible solution, otherwise it is an infeasible solution; the pavement structure verification dimensions include the permanent deformation dimension of the asphalt layer, the vertical compressive strain dimension of the roadbed top surface, the low-temperature cracking index dimension, the fatigue cracking dimension of the inorganic binder stabilization layer, and the deflection value dimension of the roadbed top surface and road surface acceptance.

[0048] Beneficial effects: By determining whether the pavement structure design scheme corresponding to the gray wolf individual meets the verification requirements by using the dimensional values ​​of the gray wolf individual in each dimension, and by pre-screening out infeasible design schemes, unnecessary waste of computing resources can be reduced, so that the optimization algorithm can converge to the feasible solution space more quickly. Since the preliminary screening strategy ensures that only those design schemes that meet specific engineering standards will be further considered, the final result is more likely to meet the actual application requirements. At the same time, by ensuring the compliance of the design scheme with respect to key performance indicators, the overall reliability and safety of the engineering project can be improved.

[0049] Furthermore, the server also includes:

[0050] The backup storage module is used to determine the dimensional level corresponding to each pavement structure verification dimension corresponding to each gray wolf individual according to the dimensional value corresponding to each pavement structure verification dimension corresponding to each gray wolf individual eliminated by the preliminary screening module, and calculate the design feasibility corresponding to each gray wolf individual based on the weight value corresponding to each pavement structure verification dimension;

[0051] It is also used to sort the design feasibility of each gray wolf individual in the order of design feasibility from large to small, form a corresponding gray wolf design feasibility table, and store the gray wolf design feasibility table;

[0052] The processing module is used to select the fitness corresponding to the α, β, and γ wolves of this iteration, the previous iteration, and the previous iteration after the judgment module completes the corresponding alpha wolf update, and calculate the corresponding gray wolf update average stability based on the preset gray wolf update average stability calculation formula, and judge whether the gray wolf update average stability is greater than the preset stability threshold. If so, select the gray wolf individuals with preset rankings from the gray wolf design feasible table to supplement the gray wolf population corresponding to this iteration, otherwise, no supplementation is performed;

[0053] The gray wolf update average stability calculation formula is:

[0054]

[0055] Where W i is the gray wolf update stability corresponding to gray wolf i, f it is the fitness of the gray wolf i corresponding to this iteration, and w is the average stability of the gray wolf update corresponding to this iteration.

[0056] Beneficial effects: In this embodiment, the gray wolf individuals that fail to meet the verification dimensions of each pavement structure are stored and retained, and these eliminated gray wolf individuals are processed. Specifically, the dimensional values ​​and verification performance of each gray wolf in the corresponding dimensions are determined to determine the level of each gray wolf individual in different dimensions, so as to calculate the design feasibility of each gray wolf. After the alpha wolf is updated, the fitness of the alpha wolf in the current iteration, the previous iteration and the previous iteration is selected, and the average stability of the gray wolf update is calculated using a preset formula. If the average stability of the gray wolf update is greater than the preset stability threshold, the gray wolf individual with a preset ranking is selected from the gray wolf design feasibility table to be added to the gray wolf population of the current iteration; otherwise, no supplementary operation is performed. Not only the fitness of the gray wolf individual in the optimization process is paid attention to, but also the concept of design feasibility is introduced, which helps to improve the quality of the solution while maintaining the diversity of the population. By using the design feasibility sorting, the system can better maintain the exploration ability of the population during the iteration process, and can dynamically add new individuals to the population when specific conditions are met, thereby increasing the possibility of finding the global optimal solution.

[0057] The present invention also provides a method for integrated design of all-solid waste asphalt pavement structure and materials, using the above-mentioned integrated design system of all-solid waste asphalt pavement structure and materials, comprising the following steps:

[0058] S1. Obtaining historical traffic data corresponding to the road to be constructed and basic roadbed information, wherein the basic roadbed information includes roadbed soil quality information, roadbed top surface equivalent rebound modulus data, and roadbed temperature and humidity data;

[0059] S2. According to the asphalt pavement design scheme corresponding to the road to be constructed, determine each pavement structure layer corresponding to the pavement to be paved, and randomly generate a number of pavement structure design schemes, wherein the pavement structure design schemes include the paving material type and paving thickness corresponding to each pavement structure layer;

[0060] S3. Based on the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, several pavement structure design plans are initially formulated;

[0061] S4. According to the corresponding pavement structure design schemes, based on the preset improved optimization algorithm, each pavement structure design scheme is optimized, the optimal pavement structure design scheme is selected and the optimal pavement result design scheme is fed back to the user end. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a logic block diagram of the all-solid waste asphalt pavement structure and material integrated design system in the first embodiment of the present invention;

[0063] Figure 2 This is a flow chart of the integrated design method of all-solid waste asphalt pavement structure and materials in Example 1 of the present invention. DETAILED DESCRIPTION

[0064] The following is further described in detail through specific implementation methods:

[0065] Embodiment 1

[0066] An integrated design system for all-solid waste asphalt pavement structure and materials, basically Figure 1 As shown, it includes a server and a user;

[0067] The server includes:

[0068] A data acquisition module is used to acquire historical traffic data corresponding to the road to be constructed, and basic roadbed information, wherein the basic roadbed information includes roadbed soil quality information, roadbed top surface equivalent rebound modulus data, and roadbed temperature and humidity data;

[0069] A scheme generation module is used to determine the various pavement structure layers corresponding to the pavement to be paved according to the asphalt pavement design scheme corresponding to the road to be constructed, and randomly generate a number of pavement structure design schemes, wherein the pavement structure design schemes include the paving material type and paving thickness corresponding to each pavement structure layer;

[0070] It is also used to preliminarily formulate several pavement structure design schemes according to the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed; in this embodiment, for example, the corresponding pavement structure design scheme is preliminarily formulated, and ten structure design schemes are formulated, namely, pavement structure design scheme 1, pavement structure design scheme 2, pavement structure design scheme 3, pavement structure design scheme 4, pavement structure design scheme 5, pavement structure design scheme 6, pavement structure design scheme 7, pavement structure design scheme 8, pavement structure design scheme 9 and pavement structure design scheme 10, and the corresponding modulus and Poisson's ratio are calculated according to the paving material type and paving thickness corresponding to each pavement structure design scheme, and the specific contents of the corresponding schemes are shown in Tables 1 to 10;

[0071]

[0072] Table 1 Pavement structure design scheme 1

[0073]

[0074]

[0075] Table 2 Pavement structure design scheme 2

[0076]

[0077] Table 3 Pavement structure design scheme 3

[0078]

[0079] Table 4 Pavement structure design scheme 4

[0080]

[0081] Table 5 Pavement structure design scheme 5

[0082]

[0083] Table 6 Pavement structure design scheme VI

[0084]

[0085]

[0086] Table 7 Pavement structure design scheme 7

[0087]

[0088] Table 8 Pavement structure design scheme eight

[0089]

[0090]

[0091] Table 9 Pavement structure design scheme 9

[0092]

[0093] Table 10 Pavement structure design plan XI

[0094] The optimization module is used to optimize each pavement structure design scheme according to the corresponding pavement structure design scheme based on the preset improved optimization algorithm, select the best pavement structure design scheme and feed back the best pavement result design scheme to the user end.

[0095] The optimization module includes:

[0096] A gray wolf initialization module is used to generate a plurality of pavement structure design schemes as corresponding gray wolf populations, wherein the gray wolf individuals of the gray wolf population correspond to a certain pavement structure design scheme;

[0097] The preliminary screening module is used to perform preliminary screening and judgment on each individual of the gray wolf population based on a preset preliminary screening strategy, to determine whether each individual gray wolf meets the corresponding conditions, and if not, the corresponding gray wolf individual is eliminated;

[0098] The initial screening strategy is:

[0099] According to the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, based on the preset pavement structure verification dimension, the dimension values ​​corresponding to each pavement structure verification dimension corresponding to each gray wolf individual are calculated, and it is judged whether all the dimension values ​​meet the preset dimension requirements. If so, the corresponding gray wolf individual is a feasible solution, otherwise it is an infeasible solution; the pavement structure verification dimension includes the dimension of permanent deformation of the asphalt layer, the dimension of vertical compressive strain on the top surface of the roadbed, the dimension of low-temperature cracking index, the dimension of fatigue cracking of the inorganic binder stabilization layer, and the dimension of the acceptance deflection value of the top surface of the roadbed and the road surface. In this embodiment, the determination of the corresponding pavement structure verification dimension is determined according to the existing highway asphalt pavement design specifications. When calculating the dimension value of each pavement structure verification dimension, the corresponding calculation formula is called for calculation, so as to obtain the corresponding calculation result. For example, when calculating the permanent deformation of the permanent deformation dimension of the asphalt layer for the ten proposed pavement structure design schemes, the dimension values ​​corresponding to the corresponding permanent deformation dimension of the asphalt layer are shown in Tables 11 to 20.

[0100]

[0101] Table 11 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer of pavement structure design scheme 1

[0102]

[0103]

[0104] Table 12 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer of pavement structure design scheme 2

[0105]

[0106] Table 13 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer of pavement structure design scheme 3

[0107]

[0108]

[0109] Table 14 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer of pavement structure design scheme 4

[0110]

[0111] Table 15 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer of pavement structure design scheme 5

[0112]

[0113]

[0114] Table 16 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer in pavement structure design scheme 6

[0115]

[0116] Table 17 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer of pavement structure design scheme 7

[0117]

[0118] Table 18 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer in pavement structure design scheme 8

[0119]

[0120] Table 19 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer in pavement structure design scheme 9

[0121]

[0122] Table 20 Dimension values ​​corresponding to the permanent deformation dimension of the asphalt layer in pavement structure design scheme 10

[0123] According to the existing highway asphalt pavement design specifications, it can be known that the preset dimension requirement corresponding to the permanent deformation dimension of the corresponding asphalt layer is that the allowable permanent deformation is 15 (mm).

[0124] According to the corresponding analysis:

[0125] Pavement structure design scheme 1 The permanent deformation of each layer is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=10.1 (mm); the proposed pavement structure meets the requirements.

[0126] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 5139 times / mm.

[0127] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 3622 times / mm.

[0128] The technical requirement for dynamic stability of the third layer of asphalt mixture in rutting test is 3924 times / mm.

[0129] The permanent deformation of each layer in pavement structure design scheme 2 is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=5.4 (mm), and the proposed pavement structure meets the requirements.

[0130] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 5139 times / mm.

[0131] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 17916 times / mm.

[0132] The permanent deformation of each layer in pavement structure design scheme three is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=8.4 (mm), and the proposed pavement structure meets the requirements.

[0133] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 5139 times / mm.

[0134] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 5543 times / mm.

[0135] The permanent deformation of each layer in pavement structure design scheme 4 is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=10.6 (mm), and the proposed pavement structure meets the requirements.

[0136] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 4000 times / mm.

[0137] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 3622 times / mm.

[0138] The technical requirement for dynamic stability of the third layer of asphalt mixture in rutting test is 3924 times / mm.

[0139] The permanent deformation of each layer in pavement structure design scheme 5 is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=5.9 (mm), and the proposed pavement structure meets the requirements.

[0140] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 4000 times / mm.

[0141] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 17916 times / mm.

[0142] The permanent deformation of each layer in pavement structure design scheme 6 is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=8.8 (mm), and the proposed pavement structure meets the requirements.

[0143] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 4000 times / mm.

[0144] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 5543 times / mm.

[0145] The permanent deformation of each layer in pavement structure design scheme seven is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=5.8 (mm), and the proposed pavement structure meets the requirements.

[0146] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 4000 times / mm.

[0147] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 3622 times / mm.

[0148] Pavement structure design scheme 8 The permanent deformation of each layer is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=5.7 (mm), and the proposed pavement structure meets the requirements.

[0149] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 4000 times / mm.

[0150] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 3622 times / mm.

[0151] The permanent deformation of each layer in pavement structure design scheme 9 is accumulated to obtain the total permanent deformation of the asphalt mixture layer Ra=8.8 (mm). The proposed pavement structure meets the requirements.

[0152] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 5139 times / mm.

[0153] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 3622 times / mm.

[0154] The technical requirement for dynamic stability of the third layer of asphalt mixture in rutting test is 2300 times / mm.

[0155] The total permanent deformation of the asphalt mixture layer Ra=9.3 (mm) is obtained by adding up the permanent deformation of each layer in the pavement structure design plan. The proposed pavement structure meets the requirements.

[0156] The technical requirement for dynamic stability of the first layer of asphalt mixture in rutting test is 4000 times / mm.

[0157] The technical requirement for dynamic stability of the second layer of asphalt mixture in rutting test is 3622 times / mm.

[0158] The technical requirement for dynamic stability of the third layer of asphalt mixture in rutting test is 2300 times / mm.

[0159] In another dimension, such as the fatigue cracking dimension of the inorganic binder stabilization layer, according to the theory of elastic layered systems, the tensile stress σt at the bottom of each structure inorganic binder layer is calculated to be 0.162, 0.156, 0.160, 0.170, 0.179, 0.183, 0.330, 0.342, 0.138 and 0.141 MPa respectively.

[0160] The calculated fatigue life Nf2 of the inorganic binder stabilization layer of each structure is 10,948,685,049, 11,819,540,394, 11,268,911,365, 10,417,647,590, 9,062,198,011, 8,637,531,853, 2,648,675,603, 2,180,563,534, 14,461,661,280 and 13,849,630,451 respectively. According to the analysis of traffic parameters, the cumulative number of equivalent design axle loads corresponding to fatigue of the inorganic binder layer within the design service life of each structure are 902,752,798, 902,752,798, 902,752,798, 902,752,798, 902,752,798, 902,752,798, 6,446,474, 6,446,474, 6,418,501, and 6,418,501 respectively. The proposed pavement structures all meet the requirements.

[0161] The theoretical deflection values ​​of each layer of each structure calculated according to the elastic layered system theory are shown in Table 21.

[0162]

[0163] Table 21 Summary of theoretical deflection values

[0164] In this embodiment, in order to better check the verification of each dimension corresponding to each pavement structure design scheme, a supplementary verification summary will be performed. For example, the verification summary corresponding to the ten pavement structure design schemes proposed in the example is shown in Table 22.

[0165]

[0166]

[0167] Table 22 Verification Summary

[0168] It can be seen from the above table that the selected pavement structure and materials can meet the requirements of various verification contents.

[0169] The server also includes:

[0170] The backup storage module is used to determine the dimensional level corresponding to each pavement structure verification dimension corresponding to each gray wolf individual according to the dimensional value corresponding to each pavement structure verification dimension corresponding to each gray wolf individual eliminated by the preliminary screening module, and calculate the design feasibility corresponding to each gray wolf individual based on the weight value corresponding to each pavement structure verification dimension;

[0171] It is also used to sort the design feasibility of each gray wolf individual in the order of design feasibility from large to small, form a corresponding gray wolf design feasibility table, and store the gray wolf design feasibility table;

[0172] The fitness calculation module is used to determine the expression of the corresponding fitness function according to the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, and calculate the fitness corresponding to all gray wolf individuals through the fitness function, and find the top three gray wolves in fitness as α, β, and γ wolves respectively; in this embodiment, the expression of the corresponding fitness function is:

[0173] f=q1C tot +q2P rec +q3Ra

[0174] In the formula, f is the fitness of the corresponding gray wolf individual, C tot is the total cost of the corresponding individual gray wolf, which is equal to the sum of material cost, construction cost and environmental cost, P rec is the proportion of recycled materials, Ra is the total permanent deformation of the asphalt mixture layer corresponding to the gray wolf individual; q1, q2 and q3 are the corresponding weighting coefficients respectively.

[0175] The hunting module is used to update the location of gray wolves based on the hunting behavior of the gray wolf population;

[0176] The hunting module includes:

[0177] A first updating module is used to update the positions of other gray wolves in the gray wolf population according to the positions of α, β, and γ wolves in the population and based on a first updating formula;

[0178] The first update formula is:

[0179] D α =|C1×X α -X|

[0180] D β =|C2×X β -X|

[0181] D γ =|C3×X γ -X|

[0182] In the formula, X α , X β and X γ Represent the positions of α wolf, β wolf, and γ wolf respectively, X represents the position of the current gray wolf individual; C1, C2, and C3 are position coefficient vectors, and each element value in C1, C2, and C3 is a random number between 0 and 2; D1, D2, and D3 represent the distances between α wolf, β wolf, and γ wolf and other individuals in the population respectively;

[0183] The first calculation module is used to update the initial positions X1, X2 and X3 of the gray wolf according to the second update formula based on the information feedback of the α wolf, the β wolf and the γ wolf respectively;

[0184] The second update formula is:

[0185] X1=X α -A1×D α

[0186] X2=X β -A2×D β

[0187] X3=X γ -A3×D γ

[0188] Where A1, A2, A3 are distance coefficient vectors;

[0189] The expressions of A1, A2 and A3 are:

[0190] A b =2×a×ra

[0191] Where r is a vector of the same dimension as the distance coefficient vector, whose elements are random numbers between 0 and 1, and a is a convergence factor, whose value decreases linearly from 2 to 0 as the number of iterations increases.

[0192] The second updating module is used to update the position of the gray wolf at the t+1th iteration according to the third updating formula;

[0193] The third update formula is:

[0194]

[0195] Where X1, X2 and X3 are the updated initial positions of the gray wolf.

[0196] The recalculation module, after updating the position of the gray wolf, recalculates the fitness of all the gray wolves based on the fitness function and sorts them from high to low according to the fitness;

[0197] The judgment module is used to select the gray wolf with the largest fitness, and judge whether the corresponding α wolf is re-elected according to the preset alpha wolf re-election mechanism strategy. If so, the α wolf will not be updated, and only the fitness and position of the β and γ wolves will be updated. Otherwise, the fitness and position of the α, β, and γ wolves will be updated;

[0198] In this embodiment, the strategy of the alpha wolf re-election mechanism is as follows: first, a random number between 0 and 1 is randomly generated. When the random number is greater than a preset random threshold, the alpha wolf is not updated, and only the fitness and position of the beta and gamma wolves are updated. Otherwise, the absolute value of the fitness difference between the current alpha wolf and the selected grey wolf is calculated based on the grey wolf with the largest fitness selected in this iteration and the current corresponding alpha wolf, and it is determined whether the absolute value of the corresponding difference is less than the preset threshold. If so, the alpha wolf is not updated, and only the fitness and position of the beta and gamma wolves are updated. Otherwise, the fitness and position of the alpha, beta and gamma wolves are updated.

[0199] At the same time, when the α wolf is not updated, the number of consecutive elections of the current α wolf will be counted. When the number of consecutive elections is greater than the preset number, as the number of consecutive elections increases, the probability of updating the current α wolf will also increase. That is, when it is determined that the α wolf is not updated, the corresponding α wolf will still have a certain probability of being updated.

[0200] The processing module is used to select the fitness corresponding to the α, β, and γ wolves of this iteration, the previous iteration, and the previous iteration after the judgment module completes the corresponding alpha wolf update, and calculate the corresponding gray wolf update average stability based on the preset gray wolf update average stability calculation formula, and judge whether the gray wolf update average stability is greater than the preset stability threshold. If so, select the gray wolf individuals with preset rankings from the gray wolf design feasible table to supplement the gray wolf population corresponding to this iteration, otherwise, no supplementation is performed;

[0201] The gray wolf update average stability calculation formula is:

[0202]

[0203] Where W i is the gray wolf update stability corresponding to gray wolf i, f it is the fitness of the gray wolf i corresponding to this iteration, and w is the average stability of the gray wolf update corresponding to this iteration.

[0204] The output module is used to determine whether the current number of iterations reaches the preset number of iteration convergences. If not, the preliminary screening module continues to be executed. Otherwise, the corresponding position of the α wolf is output as the optimal driving path plan.

[0205] like Figure 2 As shown, this embodiment also discloses a method for integrated design of all-solid waste asphalt pavement structure and materials, using the above-mentioned integrated design system of all-solid waste asphalt pavement structure and materials, including the following steps:

[0206] S1. Obtaining historical traffic data corresponding to the road to be constructed and basic roadbed information, wherein the basic roadbed information includes roadbed soil quality information, roadbed top surface equivalent rebound modulus data, and roadbed temperature and humidity data;

[0207] S2. According to the asphalt pavement design scheme corresponding to the road to be constructed, determine each pavement structure layer corresponding to the pavement to be paved, and randomly generate a number of pavement structure design schemes, wherein the pavement structure design schemes include the paving material type and paving thickness corresponding to each pavement structure layer;

[0208] S3. Based on the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, several pavement structure design plans are initially formulated;

[0209] S4. According to the corresponding pavement structure design schemes, based on the preset improved optimization algorithm, each pavement structure design scheme is optimized, the optimal pavement structure design scheme is selected and the optimal pavement result design scheme is fed back to the user end.

[0210] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is described too much here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. An integrated design system for all-solid waste asphalt pavement structure and materials, characterized by: Including server and user side; The server includes: A data acquisition module is used to acquire historical traffic data corresponding to the road to be constructed, and basic roadbed information, wherein the basic roadbed information includes roadbed soil quality information, roadbed top surface equivalent rebound modulus data, and roadbed temperature and humidity data; A scheme generation module is used to determine the various pavement structure layers corresponding to the pavement to be paved according to the asphalt pavement design scheme corresponding to the road to be constructed, and randomly generate a number of pavement structure design schemes, wherein the pavement structure design schemes include the paving material type and paving thickness corresponding to each pavement structure layer; It is also used to preliminarily formulate several pavement structure design plans based on the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed; The optimization module is used to optimize each pavement structure design scheme according to the corresponding pavement structure design scheme based on the preset improved optimization algorithm, select the best pavement structure design scheme and feed back the best pavement result design scheme to the user end.

2. The all-solid waste asphalt pavement structure and material integrated design system according to claim 1 is characterized by: The optimization module includes: A gray wolf initialization module is used to generate a plurality of pavement structure design schemes as corresponding gray wolf populations, wherein the gray wolf individuals of the gray wolf population correspond to a certain pavement structure design scheme; The preliminary screening module is used to perform preliminary screening and judgment on each individual of the gray wolf population based on a preset preliminary screening strategy, to determine whether each individual gray wolf meets the corresponding conditions, and if not, the corresponding gray wolf individual is eliminated; The fitness calculation module is used to determine the expression of the corresponding fitness function according to the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, and calculate the fitness corresponding to all gray wolf individuals through the fitness function, and find the top three gray wolves in fitness as α, β, and γ wolves respectively; The hunting module is used to update the location of gray wolves based on the hunting behavior of the gray wolf population; The recalculation module, after updating the position of the gray wolf, recalculates the fitness of all the gray wolves based on the fitness function and sorts them from high to low according to the fitness; The judgment module is used to select the gray wolf with the largest fitness, and judge whether the corresponding α wolf is re-elected according to the preset alpha wolf re-election mechanism strategy. If so, the α wolf will not be updated, and only the fitness and position of the β and γ wolves will be updated. Otherwise, the fitness and position of the α, β, and γ wolves will be updated; The output module is used to determine whether the current number of iterations reaches the preset number of iteration convergences. If not, the preliminary screening module continues to be executed. Otherwise, the corresponding position of the α wolf is output as the optimal driving path plan.

3. The all-solid waste asphalt pavement structure and material integrated design system according to claim 2 is characterized by: The hunting module includes: A first updating module is used to update the positions of other gray wolves in the gray wolf population according to the positions of α, β, and γ wolves in the population and based on a first updating formula; The first update formula is: D α =|C1×X α -X| D β =|C2×X β -X| D γ =|C3×X γ -X| In the formula, X α , X β and X γ Represent the positions of α wolf, β wolf, and γ wolf respectively, X represents the position of the current gray wolf individual; C1, C2, and C3 are position coefficient vectors, and each element value in C1, C2, and C3 is a random number between 0 and 2; D1, D2, and D3 represent the distances between α wolf, β wolf, and γ wolf and other individuals in the population respectively; The first calculation module is used to update the initial positions X1, X2 and X3 of the gray wolf according to the second update formula based on the information feedback of the α wolf, the β wolf and the γ wolf respectively; The second update formula is: X1=X α -A1×D α X2=X β -A2×D β X3=X γ -A3×D γ Where A1, A2, A3 are distance coefficient vectors; The second updating module is used to update the position of the gray wolf at the t+1th iteration according to the third updating formula; The third update formula is: Where X1, X2 and X3 are the updated initial positions of the gray wolf.

4. The all-solid waste asphalt pavement structure and material integrated design system according to claim 3 is characterized by: The expressions of A1, A2 and A3 are: A b =2×a×r-a Where r is a vector of the same dimension as the distance coefficient vector, whose elements are random numbers between 0 and 1, and a is a convergence factor, whose value decreases linearly from 2 to 0 as the number of iterations increases.

5. The all-solid waste asphalt pavement structure and material integrated design system according to claim 4 is characterized by: The initial screening strategy is: According to the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, based on the preset pavement structure verification dimensions, the dimensional values ​​corresponding to each pavement structure verification dimension corresponding to each gray wolf individual are calculated, and it is determined whether all dimensional values ​​meet the preset dimensional requirements. If so, the corresponding gray wolf individual is a feasible solution, otherwise it is an infeasible solution; the pavement structure verification dimensions include the permanent deformation dimension of the asphalt layer, the vertical compressive strain dimension of the roadbed top surface, the low-temperature cracking index dimension, the fatigue cracking dimension of the inorganic binder stabilization layer, and the deflection value dimension of the roadbed top surface and road surface acceptance.

6. The all-solid waste asphalt pavement structure and material integrated design system according to claim 5 is characterized by: The server also includes: The backup storage module is used to determine the dimensional level corresponding to each pavement structure verification dimension corresponding to each gray wolf individual according to the dimensional value corresponding to each pavement structure verification dimension corresponding to each gray wolf individual eliminated by the preliminary screening module, and calculate the design feasibility corresponding to each gray wolf individual based on the weight value corresponding to each pavement structure verification dimension; It is also used to sort the design feasibility of each gray wolf individual in the order of design feasibility from large to small, form a corresponding gray wolf design feasibility table, and store the gray wolf design feasibility table; The processing module is used to select the fitness corresponding to the α, β, and γ wolves of this iteration, the previous iteration, and the previous iteration after the judgment module completes the corresponding alpha wolf update, and calculate the corresponding gray wolf update average stability based on the preset gray wolf update average stability calculation formula, and judge whether the gray wolf update average stability is greater than the preset stability threshold. If so, select the gray wolf individuals with preset rankings from the gray wolf design feasible table to supplement the gray wolf population corresponding to this iteration, otherwise, no supplementation is performed; The gray wolf update average stability calculation formula is: Where W i is the gray wolf update stability corresponding to gray wolf i, f it is the fitness of the gray wolf i corresponding to this iteration, and w is the average stability of the gray wolf update corresponding to this iteration.

7. A method for integrated design of all-solid waste asphalt pavement structure and materials, using an integrated design system of all-solid waste asphalt pavement structure and materials according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Obtaining historical traffic data corresponding to the road to be constructed and basic roadbed information, wherein the basic roadbed information includes roadbed soil quality information, roadbed top surface equivalent rebound modulus data, and roadbed temperature and humidity data; S2. According to the asphalt pavement design scheme corresponding to the road to be constructed, determine each pavement structure layer corresponding to the pavement to be paved, and randomly generate a number of pavement structure design schemes, wherein the pavement structure design schemes include the paving material type and paving thickness corresponding to each pavement structure layer; S3. Based on the historical traffic data corresponding to the road to be constructed and the basic information of the roadbed, several pavement structure design plans are initially formulated; S4. According to the corresponding pavement structure design schemes, based on the preset improved optimization algorithm, each pavement structure design scheme is optimized, the optimal pavement structure design scheme is selected and the optimal pavement result design scheme is fed back to the user end.

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