Method for optimizing double-layer pa pavement structure into triple-layer structure based on equal permeability rate
By optimizing the double-layer PA pavement into a three-layer structure and combining the XGBoost algorithm to optimize the void ratio and emulsified asphalt dosage, the problem of poor road performance of the double-layer PA pavement was solved, and more efficient permeability and road performance were improved.
Patent Information
- Application Number
- CN202410891311.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-03
- Publication Date
- 2025-10-21
- Estimated Expiration
- 2044-07-03
AI Technical Summary
While maintaining drainage and noise reduction functions, the existing double-layer porous asphalt pavement has the problem of poor road performance. In particular, the upper PA pavement is easily damaged when subjected to environmental factors such as load, temperature changes, solar radiation and rain erosion, resulting in poor functional characteristics and road performance.
The double-layer PA pavement was optimized to a three-layer structure. By studying the relationship between paving thickness and nominal maximum particle size, a permeation rate prediction model was established in combination with the XGBoost algorithm. The void ratio and emulsified asphalt dosage were optimized to ensure permeability and bonding ability, thus forming an initial three-layer PA pavement structure.
By reducing the redundant void ratio of the structure and increasing the connected void ratio, the overall service quality of the pavement is improved, ensuring the road performance while maintaining excellent drainage and noise reduction effects, and enhancing the durability and bonding capacity of the pavement.
Smart Images
Figure CN118761319B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of double-layer porous asphalt pavement structure optimization, and in particular to a method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability rates. Background Art
[0002] Double-layer porous asphalt (PA) pavements, with their high drainage efficiency and superior noise reduction, have been gradually adopted in recent years. Limited by the pavement structure in my country, double-layer PA pavements currently have a relatively large thickness and void ratio. Practical feedback indicates that this larger thickness not only hinders void recovery after blockage, but also requires a larger void ratio to maintain functional properties, which in turn reduces road performance. This ultimately leads to a "lose-lose" situation with poor functional and road performance. Therefore, designing double-layer PA pavements that balance road performance and functional characteristics is a major challenge facing current road science and technology researchers.
[0003] The upper PA pavement, in particular, not only bears loads, performs drainage and noise reduction functions, but is also directly affected by environmental factors such as temperature fluctuations, solar radiation, rain erosion, and tire friction, resulting in a more demanding service environment. Therefore, optimizing the upper PA pavement structure and improving its road performance are even more urgent.
[0004] To this end, this study focused on a double-layer PA pavement, conducting research on the structural reorganization and optimized design of the pavement based on thinning, while maintaining its functional properties. In particular, the porosity of the upper PA pavement layer was significantly reduced, significantly improving its road performance. Summary of the Invention
[0005] The purpose of the present invention is to provide a method for optimizing a double-layer PA pavement structure into a three-layer structure based on equal permeability rates, so as to solve the problems mentioned in the above background technology.
[0006] To achieve the above object, the present invention provides a method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability rate, comprising the following steps:
[0007] S1. Study the relationship between pavement structure and material composition segregation for different nominal maximum particle sizes and different paving thicknesses, determine the multiple relationship between paving thickness and nominal maximum particle size, and compress the thickness of the upper and lower layers of the original two-layer PA pavement. At the same time, based on the principle of keeping the total pavement thickness unchanged before and after optimization, obtain the thickness of the added layer and the nominal maximum particle size to form the initial three-layer PA pavement;
[0008] S2. The effects of void ratio, paving thickness, and nominal maximum particle size on infiltration rate were studied, and an infiltration rate prediction model based on the XGBoost algorithm was established. Based on the principle of equal infiltration rates before optimization and combined with the infiltration rate of the upper layer of the double-layer PA pavement before optimization, the infiltration rate of the three-layer PA pavement was obtained.
[0009] The dependent variable infiltration rate and the independent variable void ratio in the infiltration rate prediction model based on the XGBoost algorithm were interchanged to obtain a void ratio prediction model that determines void ratio based on infiltration rate, paving thickness, and nominal maximum particle size. The corresponding void ratio was determined by combining the infiltration rates of the top and middle layers of the three-layer PA pavement, and the optimal design of the material composition of the top and middle layers was completed.
[0010] S3. Based on the feasibility of the actual construction process of the three-layer PA pavement, the effect of different spreading amounts of emulsified asphalt between the surface layer and the lower layer on the permeability and interlayer bonding capacity of the lower layer was studied. The amount of emulsified asphalt between the middle surface layer and the lower layer was determined based on the effect of different dosages of emulsified asphalt on the interlayer shear strength of the middle surface layer and the lower layer and the permeability of the lower layer.
[0011] S4. Study the relationship between the void ratio and permeability of the lower PA pavement layer at a given emulsified asphalt dosage. Based on the principle that the permeability of each layer of the three-layer PA pavement is equal, determine the void ratio of the lower layer and determine the thickness and material composition of the final three-layer PA pavement.
[0012] Preferably, S1 specifically includes:
[0013] S11. Quantitatively evaluate the internal structural distribution and segregation characteristics of double-layer PA pavements of different thicknesses based on the degree of vertical structural segregation, the coefficient of particle uneven distribution, and the coefficient of variation of particle vertical distribution, and determine the multiple relationship between paving thickness and nominal maximum particle size;
[0014] S12. Based on the multiple relationship between paving thickness and nominal maximum particle size, the nominal maximum particle size of the upper and lower layers of the double-layer PA pavement is kept unchanged, and the thickness of the upper and lower layer structures is compressed. At the same time, combined with the principle of keeping the total thickness of the pavement unchanged before and after optimization, the thickness and nominal maximum particle size of the added layer are obtained to form the initial three-layer PA pavement.
[0015] Preferably, the specific steps of S2 are:
[0016] S21. Establish a permeation rate prediction model based on the XGBoost algorithm based on the relationship between void ratio, paving thickness, and nominal maximum particle size and permeation rate. At the same time, obtain the permeation rate of the upper layer of the original double-layer PA pavement based on real experiments.
[0017] S22. Swap the independent variable void ratio and the dependent variable infiltration rate of the infiltration rate prediction model in the XGBoost algorithm to form a void ratio prediction model that predicts the void ratio based on the infiltration rate, the nominal maximum particle size and the paving thickness. According to the principle that the infiltration rate remains unchanged before and after the pavement optimization, the infiltration rate of the upper layer of the double-layer PA pavement, the paving thickness and the nominal maximum particle size of the upper and middle layers of the initial three-layer PA pavement obtained in S1 are input as input data into the void ratio prediction model to obtain the void ratio of the upper and middle layers of the initial three-layer PA pavement.
[0018] Preferably, in S3, the relationship between the penetration rate and different dosages of emulsified asphalt is obtained using the initial penetration rate of the three-layer PA pavement, and the relationship between different dosages of emulsified asphalt and interlayer shear strength is also obtained to determine the optimal emulsified asphalt dosage;
[0019] The calculation formula of interlayer shear strength is as follows:
[0020]
[0021] Among them, τ is the shear strength; F is the maximum failure load; A is the interlayer contact area.
[0022] Preferably, S4 specifically includes:
[0023] Based on the principle that the permeability of each layer of the three-layer PA pavement is equal, the relationship between the permeability and the established emulsified asphalt is obtained, and then the relationship between the emulsified asphalt dosage and the void ratio is obtained. The paving thickness, nominal maximum particle size, permeability and the established emulsified asphalt dosage in S2 are input as input data into the void ratio prediction model to determine the void ratio of the underlying layer.
[0024] Preferably, the accuracy of the permeability prediction model is R 2 , MSE, RMSE, MAE and MAPE for evaluation.
[0025] Therefore, the present invention adopts the above-mentioned method of optimizing the double-layer PA pavement structure into a three-layer structure based on equal permeability rate, which has the following beneficial effects:
[0026] During the design process of the three-layer PA pavement, the effects of void ratio, paving thickness and particle size composition are comprehensively considered to reduce the waste of structural redundant void ratio. Combined with the principle of equal permeability rate before and after optimization, the overall void ratio can be reduced, the connected void ratio can be effectively increased, the permeability performance can be guaranteed, and at the same time, the pavement bonding ability can be ensured to achieve an improvement in its road performance, which is of great significance to improving the overall service quality of the pavement.
[0027] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 Schematic diagram of the overall process of an embodiment of the present invention;
[0029] Figure 2 This is a flow chart of the degree of segregation analysis according to an embodiment of the present invention;
[0030] Figure 3 The diagram of the multi-layer pavement structure before and after optimization according to the embodiment of the present invention;
[0031] Figure 4 This is a schematic structural diagram of a bidirectional permeameter according to an embodiment of the present invention;
[0032] Figure 5 The actual test value and the predicted result value of the permeation rate model of the embodiment of the present invention are verified;
[0033] Figure 6 The experimental results of the permeation rate and time at different porosities of the embodiment of the present invention are as follows;
[0034] Figure 7 The experimental results of penetration rate and time at different paving thicknesses according to the embodiment of the present invention are as follows;
[0035] Figure 8 The experimental results of penetration rate and time at different nominal maximum particle sizes of the embodiments of the present invention are as follows;
[0036] Figure 9 This is a schematic diagram of spreading emulsified asphalt between layers according to an embodiment of the present invention;
[0037] Figure 10 This is a comparison chart of water permeability results of the embodiments of the present invention;
[0038] Figure 11 This is a comparison chart of shear strength results of the embodiments of the present invention;
[0039] Figure 12 This is a schematic diagram of the structural optimization of an embodiment of the present invention;
[0040] Figure 13 Graph showing the permeability test results of porous asphalt mixture before and after optimization according to an embodiment of the present invention. DETAILED DESCRIPTION
[0041] Example
[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0043] Reference Figure 1The present invention discloses a method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability rate, comprising the following steps (the specific embodiment takes the Sui-Zi-Mei Expressway in Sichuan Province as an example):
[0044] S1. Study the relationship between pavement structure and material composition segregation for different nominal maximum particle sizes and different paving thicknesses, determine the multiple relationship between paving thickness and nominal maximum particle size, and compress the thickness of the upper and lower layers of the original two-layer PA pavement. At the same time, combined with the principle of keeping the total thickness of the pavement unchanged before and after optimization, obtain the thickness of the added layer and the nominal maximum particle size to form the initial three-layer PA pavement.
[0045] The thickness of the traditional two-layer porous pavement was optimized to achieve an appropriate paving thickness. A uniform asphalt pavement structure requires a uniform distribution of coarse aggregate particles in both the horizontal and vertical planes. As shown in Table 1, PAC-10, PAC-13, and PAC-16, as well as porous asphalt mixtures with particle sizes of 2.0, 2.5, and 3.0 times the nominal maximum particle size, were selected to reflect their impact on asphalt mixture uniformity. Marshall specimens with a diameter of 152.4 mm were prepared for each mixture.
[0046] Table 1 Preparation of samples for segregation characteristics test
[0047]
[0048] The degree of vertical structural segregation and various coefficients are calculated according to existing technology. The particle uneven distribution coefficient U x To evaluate the uniformity of coarse aggregate distribution in a plane, the coefficient of variation of vertical distribution of particles V is used. x Evaluate the degree of vertical structural segregation of the pavement at that location. Use the above-mentioned coefficients to quantitatively evaluate the internal structural distribution and segregation characteristics of different specimens, and derive the relationship between the overall uniformity of the specimen and the paving thickness.
[0049] Core samples (100 mm in diameter) were drilled from large Marshall specimens of varying thicknesses, and the core samples were scanned to obtain information about the internal particles, resulting in a set of continuous cross-sectional slices. Ten scanned sections were evenly selected from different locations on the core sample for binarization and digital image processing. The sections were analyzed using the sector method, with the positive x-axis as the starting point and the center of the circle as the axis of rotation. The graph was divided into 24 sector-shaped areas at 15° in the counterclockwise direction. The ratio of the sum of the cumulative coarse aggregate particle areas of each block to the sector area was defined as the particle area ratio S. x , calculate the particle area ratio in each unit area respectively. Through statistical analysis of the particle area ratio, calculate the particle uneven distribution coefficient to evaluate the uniformity of asphalt mixture aggregate distribution. Through statistical analysis of the changes in the coarse aggregate particle area ratio index between the vertical sections selected for the same specimen, calculate the particle vertical distribution variation coefficient. The process is as follows Figure 2 shown.
[0050] Table 2 shows the vertical distribution coefficient of variation for the three gradation types PAC-10, PAC-13, and PAC-16 at different depths. The mean particle area ratios for each gradation type at different thicknesses were similar, indicating that the overall particle composition at different thicknesses was approximately consistent. However, the mean square deviation of the particle area ratios between sections showed significant differences between the mean square deviations for 2.5 times the nominal maximum particle size and other thicknesses. Combined with the analysis of the particle inhomogeneity distribution coefficient, it can be seen that the high use of coarse aggregate significantly affected the spatial distribution and segregation of the core samples.
[0051] Table 2 Vertical distribution variation coefficient of different thicknesses at each level
[0052]
[0053] It can be seen from Table 2 that the vertical distribution variation coefficient of each level of paving thickness is 2.5 times the nominal maximum particle size is smaller than that of 2.0 times and 3.0 times the thickness, and the uniformity is better.
[0054] Therefore, when the paving thickness is 2.5 times the nominal maximum particle size, the overall uniformity of the specimen is the best.
[0055] The thickness of the traditional double-layer PAC pavement structure is designed to be 2.5 times the nominal maximum particle size. The optimized upper layer is 2.5 cm PAC-10 and the lower layer is 4 cm PAC-16, ensuring that the design thickness of the pavement structure remains unchanged. A layer of PAC-13 is added in the middle of the double-layer PAC pavement structure layer as a "thickness filling layer" with a thickness of 3.5 cm, which is exactly 2.5 times its maximum nominal particle size.
[0056] The upper layer uses a PAC-10 structure with a porosity of 22% and a thickness of 3.5 cm, while the lower layer uses a PAC-16 structure with a porosity of 22% and a thickness of 6.5 cm. The infiltration rate is obtained through the seepage prediction model. While keeping the maximum nominal particle size of the upper and lower layers unchanged, the thickness of the two layers is compressed. According to the most suitable paving thickness, in order to ensure that the design thickness of the pavement structure remains unchanged before and after optimization, an additional layer is added to the optimized double-layer PAC pavement.
[0057] PAC-13 thickness padding layer.
[0058] S2. The effects of void ratio, paving thickness, and nominal maximum particle size on infiltration rate were studied, and an infiltration rate prediction model based on the XGBoost algorithm was established. Based on the principle of equal infiltration rates before optimization and combined with the infiltration rate of the upper layer of the double-layer PA pavement before optimization, the infiltration rate of the three-layer PA pavement was obtained.
[0059] The dependent variable infiltration rate and the independent variable void ratio in the infiltration rate prediction model based on the XGBoost algorithm are interchanged to obtain a void ratio prediction model that determines the void ratio based on the infiltration rate, paving thickness, and nominal maximum particle size. Combined with the infiltration rates of the top and middle layers of the three-layer PA pavement, the corresponding void ratio is determined, and the optimal design of the material composition of the top and middle layers is completed.
[0060] The porous asphalt mixtures with different porosity, thickness and particle size were studied, and the effects of the three different factors on the seepage characteristics were shown in Table 3.
[0061] Table 3 Test specimens used for seepage characteristics test
[0062]
[0063] The self-developed bidirectional permeameter can measure vertical and horizontal permeability simultaneously to reflect the seepage characteristics under real conditions, such as Figure 4 Weigh a certain amount of seepage water mass m i (g), record the seepage time t (s), and calculate the respective seepage rates (vertical seepage rate v1, horizontal seepage rate v2) by formula (1).
[0064]
[0065] Where ρ is the density of water (g / cm 3 ), d is the diameter of the test instrument, which is 150 mm.
[0066] The XGBoost algorithm was used to construct a permeability prediction model, and the effects of porosity, specimen thickness, and particle size composition on the permeability characteristics were analyzed. Using porosity, specimen thickness, and nominal maximum particle size as input features for the XGBoost model, 36 groups of specimens with different specifications were selected. Four parallel specimens were formed for each specification, and 144 sets of sample data were used for training, as shown in Table 4. The sum of the horizontal and vertical permeability rates was used as the predicted value, and the prediction model accuracy was calculated using R 2 , MSE, RMSE, MAE and MAPE were used for evaluation, as shown in Table 5.
[0067] Table 4 Prediction model input data
[0068]
[0069] Table 5 Description of model prediction accuracy evaluation indicators
[0070]
[0071] After training 144 sets of samples, a training model was established. The penetration rate results of some data in the training set were randomly predicted as shown in Table 6 to test the accuracy of the model. The evaluation results of each evaluation index are shown in Table 7. The coefficient of determination R of the test set 2 It has high accuracy compared with other relevant evaluation indicators, indicating that the training model has a good fitting effect on the data in the training set.
[0072] Table 6 Prediction results of some test data
[0073]
[0074] Table 7 Permeation rate model evaluation results
[0075]
[0076] To further verify the precision and accuracy of the permeability prediction model, the traditional double-layer PAC pavement structure "5cm+5cm" (the upper layer uses PAC-13 with a void ratio of 20%, and the lower layer uses PAC-16 with a void ratio of 20%) was used as a reference. Two groups of PAC-13 and PAC-16 gradations with a void ratio of 20% and a thickness of 5cm were selected to measure the permeability of standard rutting plate specimens. The results were compared with the model prediction results. Figure 5 This shows that the prediction model has high accuracy.
[0077] Void ratio is the most important factor affecting the infiltration rate. For PAC mixtures, adjusting the void ratio is the most effective way to obtain the target void characteristics, but at the same time, adopting appropriate paving thickness and changing the mixture particle size composition can also significantly change the infiltration rate.
[0078] The independent variable void ratio and the dependent variable infiltration rate of the infiltration rate prediction model in the XGBoost algorithm are swapped to form a model for predicting the void ratio based on the infiltration rate, nominal maximum particle size and paving thickness. According to the principle that the infiltration rate remains unchanged before and after the pavement optimization, the infiltration rate of the upper layer of the double-layer PA pavement, the paving thickness and nominal maximum particle size of the upper and middle layers of the initial three-layer PA pavement obtained in S1 are input as input data into the void ratio prediction model to obtain the void ratio of the upper and middle layers of the initial three-layer PA pavement.
[0079] The permeation rate and permeation time at different porosity and thickness are as follows: Figure 6 、 7 As shown in the figure, the changes in the permeation rate and seepage time under different nominal maximum particle sizes at the same thickness of 4 cm are shown in the figure. Figure 8 As shown in the figure, the bars represent the permeation rate, V and H represent the vertical and horizontal permeation rates, respectively. The dotted line graph represents the time it takes to flow through different specimens.
[0080] Depend on Figure 6 As can be seen, the infiltration rate of PA mixtures with varying nominal maximum particle sizes increased with increasing void fraction, indicating that increasing void fraction significantly improves the connectivity of the voids within the specimens, enhancing the permeability of porous asphalt pavements. Vertical infiltration rate significantly increased, while lateral infiltration rate slowly decreased, indicating that water tended to infiltrate along vertical seepage paths. Furthermore, infiltration time was shortened to a certain extent, with the PAC-16 gradation exhibiting a faster rate of decline. The improvement in drainage efficiency was most pronounced within the void fraction range of 15%-21%, but the increase in drainage efficiency slowed after exceeding 21%. This result is consistent with the current consensus that the preferred void fraction for PAC is mostly between 20% and 22%. Although lateral infiltration rate slowly decreases with increasing void fraction, it still dominates the infiltration process, accounting for 40.3%-46.7% of the total infiltration rate at a void fraction of 24%.
[0081] Figure 7 It can be seen that as the specimen thickness increases, the total permeability rate and vertical permeability rate of the PAC specimen show a downward trend, while the lateral permeability rate increases, and the permeability time also gradually increases. Comparing the various figures, under the same void conditions, the permeability rate of PAC-16 is significantly reduced compared to other gradations. When the specimen thickness is thin (2cm-3cm), due to the small or even no voids on the side of the specimen, the main permeability is vertical permeation. As the specimen thickness continues to increase, the vertical connected voids decrease, and the lateral voids increase. The permeability performance of the specimen changes from vertical permeation to lateral permeation, and therefore the lateral permeability rate also increases. For example, when the thickness of the PAC-10 gradation increases from 2cm to 3cm, the vertical permeability rate decreases by 37.3%, while the lateral permeability rate increases by 56.5%.
[0082] Depend on Figure 8 It can be seen that as the maximum nominal particle size increases, the permeation rate increases, and the corresponding permeation time decreases accordingly. At the same time, both the vertical and lateral permeation rates increase, and the increase in the vertical permeation rate is more significant. When the void ratio is greater than 18%, it can be clearly seen that the permeation rate of PAC-16 is much greater than that of PAC-13, and the rate of increase is faster. This shows that as the maximum nominal particle size of the PAC specimen increases, its permeation rate is more affected, while when the maximum nominal particle size is smaller, the effect of gradation may not be so obvious.
[0083] In summary, the total permeability of PA pavement significantly increases with increasing porosity and nominal maximum particle size, while increasing thickness actually reduces it. The permeability of the PAC-16 gradation is most significantly affected by porosity, while the lateral and vertical permeability of the PAC-10 specimen are more significantly affected by thickness. Furthermore, increasing the maximum nominal particle size and porosity significantly impacts the permeability rate.
[0084] S3. Based on the feasibility of the actual construction process of the three-layer PA pavement, the study examined the effects of different spreading amounts of emulsified asphalt between the middle surface layer and the lower layer on the permeability of the lower layer and the interlayer bonding ability. The amount of emulsified asphalt between the middle surface layer and the lower layer was determined based on the effects of different doses of emulsified asphalt on the interlayer shear strength of the middle surface layer and the lower layer and the permeability of the lower layer.
[0085] For the three-layer PA pavement in this embodiment, the bonding performance between the surface layers is improved by spreading an emulsified asphalt adhesive layer. Figure 9 As shown in the figure, the effect of spreading different doses of emulsified asphalt on interlayer performance must be compared and analyzed through seepage test and shear test.
[0086] The porous asphalt mixture was prepared using Marshall specimens with a diameter of 152.4 mm, based on the thickness design described above. The following steps were used: After the lower layer of asphalt mixture was formed, different doses of modified emulsified asphalt (0, 0.15, 0.3, 0.45, and 0.6 kg / m²) were evenly spread on the upper surface. The technical specifications of the modified emulsified asphalt are shown in Table 8. The asphalt mixtures for the middle and upper layers were formed simultaneously. A control group was set up where all three layers were paved simultaneously.
[0087] Table 8 Technical indicators of modified emulsified asphalt
[0088]
[0089] The interlaminar shear strength of porous asphalt mixtures was studied using an MTS universal testing machine. First, core samples were drilled to obtain cylindrical specimens with a diameter of 100 mm. The specimens were placed in a 25°C constant temperature chamber for at least 3 hours. Afterwards, the specimens were placed in a mold and fixed. The MTS universal testing machine was then loaded at a controlled loading rate of 50 mm / min until the specimens failed. The maximum failure load was obtained, and the interlaminar shear strength of the specimens was calculated using Equation (2).
[0090]
[0091] Where: τ is the shear strength (MPa); F is the maximum failure load (N); A is the contact area between the specimen layers (mm 2 ).
[0092] At the same time, a self-made bidirectional permeameter was used to test the seepage performance. The test method was consistent with that of S2.
[0093] from Figure 10 、 Figure 11 It can be seen that the water permeability coefficient shows a downward trend with the increase of the amount of emulsified asphalt in the tack coat. When the amount of emulsified asphalt in the tack coat is 0.45kg / m2, the decrease is greater, and its water permeability coefficient is only 77.76% of that when no tack coat oil is applied. When the amount of emulsified asphalt in the tack coat is 0.3kg / m2, its water permeability coefficient is 0.625cm / s, which is 90.31% of that when no tack coat oil is applied.
[0094] With the increase in the amount of tack coat oil, the shear strength between layers shows an upward trend. When the amount of emulsified asphalt tack coat oil spread is 0.3kg / m2, the shear strength is 0.67MPa, which is 28.8% higher than the shear strength without emulsified asphalt tack coat oil. When the amount of tack coat oil spread exceeds 0.45kg / m2, the trend of increasing shear strength is no longer obvious.
[0095] Taking all factors into consideration, it is recommended to use tack coat with emulsified asphalt at a rate of 0.3 kg / m 2 The shear strength is greatly improved compared to that without spreading, while also ensuring good permeability.
[0096] Based on the principle of constant permeability rate, taking the upper layer of the double-layer porous structure as a reference, the optimized three-layer PA structure has an upper layer permeability rate of 0.6791cm / s and a middle layer permeability rate of 0.6791cm / s; and after considering the effect of interlayer emulsified asphalt on the permeability rate, the lower layer has an equivalent permeability rate of 0.8279cm / s. The optimized three-layer PA pavement structure is shown in the figure below. Figure 3 The void ratio of the optimized three-layer PA pavement structure is reduced.
[0097] S4. Study the relationship between the void ratio and permeability of the lower PA pavement layer at a given emulsified asphalt dosage. Based on the principle that the permeability of each layer of the three-layer PA pavement is equal, determine the void ratio of the lower layer and determine the thickness and material composition of the final three-layer PA pavement.
[0098] To determine the void ratio of the optimized three-layer PA pavement structure, a prediction model was established using the maximum nominal particle size, thickness, and infiltration rate as independent variables and the void ratio as the dependent variable. Using the infiltration rate prediction model, the original sample data was kept unchanged, and the independent variable, void ratio, and the dependent variable, infiltration rate, were swapped to establish a void ratio prediction model.
[0099] After optimization, most of the pore blockage is concentrated in the surface layer, which is thinner and has a higher repair efficiency after blockage; the interconnected porosity of the three-layer structure can be improved, and the overall porosity is reduced, which can better maintain the durability of the optimized three-layer PA pavement pore structure. Figure 12 .
[0100] The porosity, thickness, and maximum nominal particle size of each structural layer of the traditional double-layer PAC pavement are known. The permeation rate of each structural layer in the highway test section is predicted based on the permeation rate prediction model, as shown in Table 9.
[0101] Table 9 Prediction results of permeability of double-layer PAC pavement structure
[0102]
[0103]
[0104] For the void ratio prediction model, the void ratio results of some data in the training set are randomly predicted as shown in Table 10, and the evaluation results of each evaluation index are shown in Table 11. As shown in Table 11, the prediction result determination coefficient R of the void ratio model is 2 The prediction result is 0.907, which is relatively accurate.
[0105] Table 10 Prediction results of some test data with void ratio as dependent variable
[0106]
[0107] Table 11 Model evaluation results with void ratio as dependent variable
[0108]
[0109] Performance evaluation of three-layer porous asphalt mixture
[0110] The permeability of porous asphalt mixture is evaluated by permeameter. A larger permeability coefficient means higher permeability. Figure 13 The permeability rates of the porous asphalt mixture before and after optimization are 3809.2 mL / min and 4083.3 mL / min, respectively. This is a three-layer porous asphalt pavement structure designed based on constant permeability rates, which reduces the void ratio while maintaining permeability and improving the pavement performance of the surface layer.
[0111] The key road performance of the optimized porous asphalt mixture, such as high temperature, low temperature, water stability and anti-scattering, has been improved to varying degrees.
[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability, characterized in that: The following steps are involved: S1. Study the relationship between pavement structure and material composition segregation for different nominal maximum particle sizes and different paving thicknesses, determine the multiple relationship between paving thickness and nominal maximum particle size, and compress the thickness of the upper and lower layers of the double-layer PA pavement. At the same time, based on the principle of keeping the total pavement thickness unchanged before and after optimization, obtain the added layer thickness and nominal maximum particle size to form the initial three-layer PA pavement; S2. Study the effects of void ratio, paving thickness, and nominal maximum particle size on infiltration rate, and establish an infiltration rate prediction model based on the XGBoost algorithm. Calculate the void ratios of the initial three-layer PA pavement's top and middle layers based on the infiltration rate of the top layer before optimization, and optimize the material composition of the top and middle layers. S3. Based on the feasibility of the actual construction process of the three-layer PA pavement, the effect of different spreading amounts of emulsified asphalt between the surface layer and the lower layer on the permeability and interlayer bonding capacity of the lower layer was studied. The amount of emulsified asphalt between the middle surface layer and the lower layer was determined based on the effect of different dosages of emulsified asphalt on the interlayer shear strength of the middle surface layer and the lower layer and the permeability of the lower layer. S4. Study the relationship between the void ratio and permeability of the lower PA pavement layer at a given emulsified asphalt dosage. Based on the principle that the permeability of each layer of the three-layer PA pavement is equal, determine the void ratio of the lower layer and determine the thickness and material composition of the final three-layer PA pavement.
2. The method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability rate according to claim 1, characterized in that: S1 specifically includes: S11. Quantitatively evaluate the internal structural distribution and segregation characteristics of double-layer PA pavements of different thicknesses based on the degree of vertical structural segregation, the coefficient of particle uneven distribution, and the coefficient of variation of particle vertical distribution, and determine the multiple relationship between paving thickness and nominal maximum particle size; S12. Based on the multiple relationship between paving thickness and nominal maximum particle size, the nominal maximum particle size of the upper and lower layers of the double-layer PA pavement is kept unchanged, and the thickness of the upper and lower layer structures is compressed. At the same time, combined with the principle of keeping the total thickness of the pavement unchanged before and after optimization, the thickness and nominal maximum particle size of the added layer are obtained to form the initial three-layer PA pavement.
3. The method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability rate according to claim 2, characterized in that: The specific steps of S2 are: S21. Establish a permeation rate prediction model based on the XGBoost algorithm based on the relationship between void ratio, paving thickness, and nominal maximum particle size and permeation rate. At the same time, obtain the permeation rate of the upper layer of the original double-layer PA pavement based on real experiments. S22. Swap the independent variable void ratio and the dependent variable infiltration rate of the infiltration rate prediction model in the XGBoost algorithm to form a void ratio prediction model that predicts the void ratio based on the infiltration rate, the nominal maximum particle size and the paving thickness. According to the principle that the infiltration rate remains unchanged before and after the pavement optimization, the infiltration rate of the upper layer of the double-layer PA pavement, the paving thickness and the nominal maximum particle size of the upper and middle layers of the initial three-layer PA pavement obtained in S1 are input as input data into the void ratio prediction model to obtain the void ratio of the upper and middle layers of the initial three-layer PA pavement.
4. The method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability according to claim 1, characterized in that: In S3, the initial permeation rate of the three-layer PA pavement was used to obtain the relationship between the permeation rate and different dosages of emulsified asphalt. The relationship between different dosages of emulsified asphalt and interlayer shear strength was also obtained to determine the optimal emulsified asphalt dosage. The calculation formula of interlayer shear strength is as follows: Among them, τ is the shear strength; F is the maximum failure load; A is the interlayer contact area.
5. The method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability rate according to claim 4, characterized in that: S4 specifically includes: Based on the principle that the permeability of each layer of the three-layer PA pavement is equal, the relationship between the permeability and the established emulsified asphalt is obtained, and then the relationship between the emulsified asphalt dosage and the void ratio is obtained. The paving thickness, nominal maximum particle size, permeability and the established emulsified asphalt dosage in S2 are input as input data into the void ratio prediction model to determine the void ratio of the underlying layer.
6. The method for optimizing a double-layer PA pavement structure into a three-layer structure based on constant permeability according to claim 1, characterized in that: The accuracy of the permeability prediction model was calculated using R 2 , MSE, RMSE, MAE and MAPE for evaluation.