1. A method for quickly predicting the dynamic resilience modulus of graded crushed stone considering particle crushing is characterized by comprising the following steps:

step S1: determining physical parameters of multiple groups of graded broken stones under the conditions of different grades, different compactibility and different water contents, namely the thickness ratio G/S and the relative crushing potential B under different water contents_r(w) shape characteristic quantity parameter lambda of two-dimensional shape index_FShape characteristic quantity parameter lambda of gradient edge angle index_GAnd the shape characteristic quantity parameter lambda of the sphericity index_SAnd dry density gamma_dAnd the water content w;

step S2: respectively measuring the dynamic resilience modulus of the multiple groups of graded crushed stones in the step S1 according to a dynamic triaxial test, and predicting by adopting a three-parameter model of NCHRP 1-28A, as shown in a formula (1-1);

in the formula, E_yRepresents the modulus of resilience in the axial direction; theta_bsDenotes the body stress,. tau_octDenotes the octahedral shear stress, P_aIs a reference atmospheric pressure; based on the dynamic resilience modulus of each group of graded crushed stones obtained by dynamic triaxial test, fitting the three-parameter model to obtain a model fitting coefficient k₁、k₂And k₃；

Step S3: determining the fitting parameter k of all physical parameters of each group of graded crushed stones to the three-parameter model₁、k₂、k₃Determining a fitting parameter k of the model by adopting a stepwise multiple regression analysis method₁～k₃And respectively obtaining a quick prediction formula according to the correlation between the data and each physical property parameter.

2. The method for rapidly predicting the dynamic resilience modulus of graded crushed stone considering particle breakage as claimed in claim 1, wherein in step S3, the formula is rapidly predicted as shown in formulas (1-2) to (1-4):

k₁＝-1.296+3.082ln(γ_d)-0.434ln(w)+0.238ln(G/S)+0.811λ_S (1-2)

k₂＝1.175-0.069ln(G/S)+0.400ln(λ_F)-0.172ln(λ_G)-0.210λ_S (1-3)

k₃＝-1.348+0.453B_r(w)+0.024ln(G/S)+0.159ln(λ_G)-0.071λ_S (1-4)

wherein k is₁、k₂、k₃Fitting parameters, γ, for both NCHRP 1-28A models_dThe dry density of the corresponding graded broken stone is shown, w is the water content of the corresponding graded broken stone, G/S is the thickness ratio of the corresponding graded broken stone, and lambda is_SA shape characteristic quantity parameter, lambda, representing a sphericity index corresponding to graded crushed stone_FA shape characteristic quantity parameter, lambda, representing a two-dimensional shape index corresponding to graded crushed stone_GA shape characteristic quantity parameter representing a gradient edge index corresponding to graded crushed stone, B_r(w) represents the relative crushing potential of graded crushed stones corresponding to different water contents.

3. The method for rapidly predicting the dynamic resilience modulus of graded crushed stone considering particle crushing as claimed in claim 1, wherein in step S1, the thickness ratio G/S of each group of graded crushed stone is obtained according to the formula (1-5),

in the formula, p₄And p₂₀₀The percent passage of the 4.75mm pore size sieve and the 0.075mm pore size sieve, respectively, is indicated.

4. The method for rapidly predicting the dynamic rebound modulus of graded broken stone considering particle crushing as claimed in claim 1, wherein in step S1, the relative crushing potential B of each group of graded broken stones under different water content is_r(w) is obtained according to the following steps:

according to the optimum water content OMC and the maximum dry density gamma of the corresponding graded crushed stone_dmaxForming a cylindrical standard test piece by adopting a static pressure method aiming at each group of graded crushed stones with the compaction degrees of 93 percent, 95 percent and 98 percent respectively and the water contents of OMC-1 percent, OMC and OMC +1 percent respectively, counting the grain grading after the test piece is formed by a water washing method screening test, and calculating by adopting a formula (1-6) to obtain the relative crushing potential B of the corresponding test piece_r：

In the formula, B_tRepresents the total amount of crushing, B_pRepresenting the fracture potential; total amount of crushing B_tDetermined by the area enclosed by the initial particle grading curve and the formed particle grading curve, and the crushing potential B_pDetermined by the area enclosed by the initial grain composition curve and the dashed line with the maximum grain size of 0.075 mm; relative crushing potential B of test piece_rThe slope of the fitting straight line is k along with the linear change of the increase of the water content w; quickly determining the relative crushing potential B under different water contents according to the formula (1-7)_r(w)：

In the formula, B_r(OMC) represents the relative crushing potential at the optimum water content OMC.

5. The method for rapidly predicting the dynamic resilience modulus of graded macadam considering particle breakage as claimed in claim 1, wherein in step S1, the AIMS results of the multiple sets of graded macadams of step S1 are fitted by Weibull cumulative probability distribution to establish a model, as shown in formulas (1-8):

wherein F is the cumulative probability; x is a statistical parameter of the AIMS and represents any one index of a two-dimensional shape index, a gradient corner index or a sphericity; λ is a proportional parameter; alpha is a shape parameter; fitting by using a formula (1-8) according to the test results of the two-dimensional shape index, the gradient corner index or the sphericity, so as to obtain a proportion parameter lambda and a shape parameter a simultaneously; if x represents a two-dimensional shape index, the proportional parameter lambda is a shape characteristic quantity parameter of the two-dimensional shape index and is recorded as lambda_FIf x represents the gradient edge angle index, the proportional parameter lambda is the shape characteristic quantity parameter of the gradient edge angle index and is recorded as lambda_G(ii) a If x represents the sphericity index, the proportional parameter lambda is the shape characteristic quantity parameter of the sphericity index and is recorded as lambda_S。

6. The method for rapidly predicting the dynamic resilience modulus of graded crushed stones with particle breakage taken into consideration in claim 5, wherein in the step S1, the two-dimensional shape index, the gradient corner value and the sphericity index of each group of graded crushed stones are obtained through calculation by an AIMS system.

7. The method for rapidly predicting the dynamic resilience modulus of graded crushed stone considering particle crushing as claimed in claim 6, wherein the two-dimensional shape index, the gradient edge angle value and the sphericity index are calculated according to the following formulas (1-9), (1-10) and (1-11):

in the formula, theta is a measurement angle; r_θIs the radius of the aggregate in the theta angle direction; delta theta is the measurement angle increment; n is the total number of the edge points of the aggregate image; i is the ith point of the edge of the aggregate image; d_LThe length of the minimum external cuboid of the coarse aggregate; d_IIs the minimum width of the external rectangular solid; d_SIs the minimum height of the circumscribed cuboid, theta_iRepresenting the measured angle, theta, of the ith point_i+3Represents the measured angle at the i +3 th point.

Background

As the graded broken stone material of the transition layer and the cushion layer of the roadbed, the graded broken stone material has uniform grading and relatively small contact area among particles, and the particles are easy to break under the combined action of load stress and environmental factors, thereby causing the graded broken stone deterioration and the mechanical property attenuation, and reducing the deformation resistance. If the broken stone materials with unreasonable grading, poor edge and corner properties and serious crushing are selected, the subgrade structure after being paved will be caused to have settlement deformation in different degrees, but the performance of the subgrade cannot be improved, the service life of the road structure is greatly shortened, and the driving safety is damaged. Therefore, based on the strategic targets of stability and durability of road engineering, scientific evaluation of the rebound deformation performance of the graded broken stone material has important significance.

At present, the latest version of road subgrade design specification (JTG D30-2015) and road asphalt pavement design specification (JTG D50-2017) both adopt dynamic resilience modulus as a design parameter of coarse soil or gravel materials. Due to the essential difference between the static calculation and the dynamic response of the structure, the quick prediction results such as CBR conversion, table look-up method and the like which are provided based on the static modulus in the early stage are not suitable for the existing road design any more, and the accurate guidance on the site construction control can not be carried out. Meanwhile, the elastic modulus test method (appendix D) for the granular materials proposed by the new edition of specifications needs to adopt a high-price dynamic triaxial tester; in addition, the broken stone materials used for testing are loose, the test piece is difficult to prepare, and the result discreteness is large, so that the application of the dynamic triaxial tester in road engineering is limited by the factors. In the existing literature (such as a particulate material fragmentation evolution path mesoscopic thermodynamic mechanism, hypersensitiveness, and the like, 2019.1), researches on the evolution law of the dynamic rebound modulus of the graded crushed stone under different molding modes and water content conditions are not deep, and the dynamic rebound modulus of the graded crushed stone is difficult to accurately and rapidly predict.

Disclosure of Invention

In order to solve the problems, the invention provides a method for quickly predicting the dynamic resilience modulus of graded broken stones by considering particle crushing, which can conveniently and accurately obtain the dynamic resilience modulus of the graded broken stones, scientifically guide the design and construction of the graded broken stones in a pavement structure, ensure the engineering quality and solve the problems in the prior art.

The technical scheme adopted by the invention is that the method for quickly predicting the dynamic resilience modulus of the graded crushed stone considering particle crushing specifically comprises the following steps:

step S1: determining physical parameters of multiple groups of graded broken stones under the conditions of different grades, compactibility and water content, namely the thickness ratio G/S and the relative crushing potential B under different water contents_r(w) shape characteristic quantity parameter lambda of two-dimensional shape index_FShape characteristic quantity parameter lambda of gradient edge angle index_GAnd the shape characteristic quantity parameter lambda of the sphericity index_SAnd dry density gamma_dAnd the water content w;

step S2: according to the dynamic triaxial test, the dynamic resilience modulus of the multiple groups of graded crushed stones in the step S1 is respectively measured, and a three-parameter model of NCHRP 1-28A is adopted for prediction, wherein the specific formula is as follows:

in the formula, E_yRepresents the modulus of resilience in the axial direction; theta_bsDenotes the body stress,. tau_octDenotes the octahedral shear stress, P_aIs a reference atmospheric pressure; based on the dynamic resilience modulus of each group of graded crushed stones obtained by dynamic triaxial test, fitting the three-parameter model to obtain a model fitting coefficient k₁、k₂And k₃；

Step S3: determining the fitting parameter k of all physical parameters of each group of graded crushed stones to the three-parameter model₁、k₂、k₃Determining a fitting parameter k of the model by adopting a stepwise multiple regression analysis method₁～k₃And respectively obtaining a quick prediction formula according to the correlation between the data and each physical property parameter.

Further, in step S3, the fast prediction formula is shown in the following formula:

k₁＝-1.296+3.082ln(γ_d)-0.434ln(w)+0.238ln(G/S)+0.811λ_S

k₂＝1.175-0.069ln(G/S)+0.400ln(λ_F)-0.172ln(λ_G)-0.210λ_S

k₃＝-1.348+0.453B_r(w)+0.024ln(G/S)+0.159ln(λ_G)-0.071λ_S

wherein k is₁、k₂、k₃Fitting parameters, γ, for both NCHRP 1-28A models_dThe dry density of the corresponding graded broken stone is shown, w is the water content of the corresponding graded broken stone, G/S is the thickness ratio of the corresponding graded broken stone, and lambda is_SA shape characteristic quantity parameter, lambda, representing a sphericity index corresponding to graded crushed stone_FA shape characteristic quantity parameter, lambda, representing a two-dimensional shape index corresponding to graded crushed stone_GA shape characteristic quantity parameter representing a gradient edge index corresponding to graded crushed stone, B_r(w) represents the relative crushing potential of graded crushed stones corresponding to different water contents.

Further, in the step S1, the thickness ratio G/S of each group of graded crushed stones is obtained according to the following formula,

in the formula, p₄And p₂₀₀The percent passage of the 4.75mm pore size sieve and the 0.075mm pore size sieve, respectively, is indicated.

Further, in the step S1, the relative crushing potential B of each group of graded crushed stones under different water content_r(w) is obtained according to the following steps:

according to the optimum water content OMC and the maximum dry density gamma of the corresponding graded crushed stone_dmaxForming a cylindrical standard test piece by a static pressure method aiming at each group of graded crushed stones with the compaction degrees of 93 percent, 95 percent and 98 percent respectively and the water contents of OMC-1 percent, OMC and OMC +1 percent respectively, counting the grain grading after the test piece is formed by a water washing method screening test, and calculating by adopting the following formula to obtain the relative crushing potential B of the corresponding test piece_r：

In the formula, B_tRepresents the total amount of crushing, B_pRepresenting the fracture potential; total amount of crushing B_tFrom the initial particle grading curveDetermining the area surrounded by the grain composition curve after molding, and breaking potential B_pDetermined by the area enclosed by the initial grain composition curve and the dashed line with the maximum grain size of 0.075 mm; relative crushing potential B of test piece_rThe slope of the fitting straight line is k along with the linear change of the increase of the water content w; quickly determining the relative crushing potential B under different water contents according to the following formula_r(w)：

In the formula, B_r(OMC) represents the relative crushing potential at the optimum water content OMC.

Further, in step S1, fitting the AIMS results of the multiple sets of graded gravels in step S1 by Weibull cumulative probability distribution to build a model, which is as follows:

wherein F is the cumulative probability; x is a statistical parameter of the AIMS and represents any one index of a two-dimensional shape index, a gradient corner index or a sphericity; λ is a proportional parameter; alpha is a shape parameter; fitting according to the test results of the two-dimensional shape index, the gradient corner index or the sphericity, so as to obtain a proportion parameter lambda and a shape parameter a at the same time; if x represents a two-dimensional shape index, the proportional parameter lambda is a shape characteristic quantity parameter of the two-dimensional shape index and is recorded as lambda_FIf x represents the gradient edge angle index, the proportional parameter lambda is the shape characteristic quantity parameter of the gradient edge angle index and is recorded as lambda_G(ii) a If x represents the sphericity index, the proportional parameter lambda is the shape characteristic quantity parameter of the sphericity index and is recorded as lambda_S。

Further, in step S1, the two-dimensional shape index, the gradient edge angle value, and the sphericity index of each set of graded crushed stones are calculated and obtained by the AIMS system.

Further, the two-dimensional shape index, the gradient edge angle value and the sphericity index are calculated according to the following formulas:

in the formula, theta is a measurement angle; r_θIs the radius of the aggregate in the theta angle direction; delta theta is the measurement angle increment; n is the total number of the edge points of the aggregate image; i is the ith point of the edge of the aggregate image; d_LThe length of the minimum external cuboid of the coarse aggregate; d_IIs the minimum width of the external rectangular solid; d_SIs the minimum height of the circumscribed cuboid, theta_iRepresenting the measured angle, theta, of the ith point_i+3Represents the measured angle at the i +3 th point.

The invention has the beneficial effects that:

1. by adopting the method for rapidly predicting the dynamic resilience modulus of the graded crushed stone, the resilience modulus of the graded crushed stone material under different working conditions can be predicted more accurately only by testing the basic physical properties of the graded crushed stone material, so that the test time is greatly reduced, and the test difficulty is reduced; the dynamic triaxial test device can replace the dynamic triaxial test which is expensive, time-consuming and labor-consuming, greatly facilitates the design and construction inspection of graded broken stones, provides obvious engineering convenience for units without triaxial test conditions, and has higher market popularization value. Wherein, dry density (. gamma.) is_d) The determination of the moisture content (w), the thickness ratio (G/S) and the particle crushing condition are all conventional test methods, the test can be carried out in general production departments or construction sites, and for the aspect of the shape parameters of the aggregate, the invention adopts a statistical method to predict the aggregate population, does not need to carry out a large amount of AIMS tests, only needs to carry out periodical sampling inspection on the crushed stone of the material taking field, thereby not influencing the positive influenceThe construction of the regular graded broken stones is less in cost.

2. Compared with the conventional standard method, the method for rapidly predicting the dynamic resilience modulus of the graded broken stone can conveniently and accurately obtain the dynamic resilience modulus of the graded broken stone, conveniently guides the design and construction of the graded broken stone in a pavement structure, can be popularized to the design and detection of other granular materials, and has wide application value.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic diagram of the shape parameter fitting results.

FIG. 2 is a schematic diagram of relative fracture potential calculations.

FIG. 3 is a graph showing the relative crushing potential as a function of water content.

FIG. 4 is a graph illustrating the contribution of material parameters to model parameters.

FIG. 5 is a diagram of the fitting results of the fast predictive model.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The embodiment of the invention relates to a method for quickly predicting the dynamic resilience modulus of graded crushed stone considering particle crushing, which specifically comprises the following steps:

step S1: determining multi-group grading fragments under conditions of different grading, compaction degree and water contentPhysical parameters of the stone, i.e. the fineness ratio G/S, the relative crushing potential B at different water contents_r(w)、λ_F、λ_G、λ_SAnd dry density gamma_dAnd the water content w;

s1.1: three graded broken stone test pieces with continuous gradation are prepared by limestone aggregate, wherein the initial particle gradation of the test piece is shown in table 1, and the thickness ratio G/S of the three graded broken stone test pieces is calculated by adopting the formula (1) and is respectively 1.22, 1.56 and 1.97. Then, the optimum water content (OMC) and the maximum dry density (gamma) of the graded crushed stones are obtained through an indoor compaction test_dmax) OMC and gamma for three graded crushed stones_dmaxRespectively as follows: 4.96% and 2.261g/cm³4.81% and 2.307g/cm³4.61% and 2.331g/cm³。

In the formula, p₄And p₂₀₀The passage percentages of sieve No. 4 (4.75mm) and sieve No. 200 (0.075mm) are indicated, respectively.

TABLE 1 percent passage of particles of three continuous grades (%)

S1.2: before preparing graded macadam test pieces, an AIMS II system produced by American Pine company is utilized to test shape parameters of macadam aggregates, 2.0kg of samples are randomly weighed from the macadam materials of each test piece according to the principle of a quartering method, the samples are placed in plastic trays, the AIMS system is operated to automatically calculate and obtain a two-dimensional shape index (Form2D), a gradient edge angle value (GA) and a sphericity index (SP) of the aggregates in each sample, and the calculation formulas are shown in formulas (2) to (4).

In the formula, theta is a measurement angle; r_θIs the radius of the aggregate in the theta angle direction; delta theta is the measurement angle increment and is taken as 4 degrees; n is the total number of the edge points of the aggregate image; i is the ith point of the edge of the aggregate image; d_LThe length of the minimum external cuboid of the coarse aggregate; d_IIs the minimum width of the external rectangular solid; d_SIs the minimum height of the circumscribed cuboid, theta_iRepresenting the measured angle, theta, of the ith point_i+3Represents the measured angle at the i +3 th point.

In order to quantitatively evaluate the distribution of aggregate shape parameters, AIMS results of the three graded aggregates are subjected to fitting analysis by utilizing Weibull cumulative probability distribution, and a model is calculated as shown in a formula (5), wherein the change of the aggregate shape parameters is mainly related to a proportional parameter lambda, and the curve fullness degree corresponding to the shape parameter a is mainly influenced by the number of samples, so that the proportional parameter lambda is used as a shape characteristic quantity for evaluating the influence rule of the shape parameters on the resilience modulus performance of the graded macadam.

Wherein F is the cumulative probability; x is a two-dimensional shape index, a gradient edge angle index or a sphericity; λ is a proportional parameter; alpha is a shape parameter. And x is a general expression, and represents any one index of a two-dimensional shape index, a gradient corner angle index or sphericity, wherein x is a solved statistical parameter. When x is a two-dimensional shape index (Form2D), the fitting result is shown in fig. 1; when x is the gradient edge angle index or sphericity, the fitting result is substantially the same as that of fig. 1. And fitting by using the formula (5) according to the test results of the two-dimensional shape index, the gradient corner index or the sphericity, so that the proportional parameter lambda and the shape parameter a can be obtained simultaneously. If x represents a two-dimensional shapeThe shape index, the proportional parameter lambda is the shape characteristic quantity parameter of the two-dimensional shape index, and is recorded as lambda_FThe shape parameter alpha is a curve fullness parameter a_F(ii) a If x represents the gradient edge angle index, the proportional parameter lambda is the shape characteristic quantity parameter of the gradient edge angle index and is recorded as lambda_G(ii) a The shape parameter alpha is a curve fullness parameter a_G(ii) a If x represents the sphericity index, the proportional parameter lambda is the shape characteristic quantity parameter of the sphericity index and is recorded as lambda_SThe shape parameter alpha is a curve fullness parameter a_S。

S1.3: according to the optimum water content (OMC) and maximum dry density (gamma) of graded crushed stone_dmax) For each group of graded crushed stones with different compactedness (93%, 95% and 98%) and water content (OMC-1%, OMC and OMC + 1%), a cylindrical standard test piece with the size of 100mm multiplied by 200mm is formed by a static pressure method. The particle composition of the formed test piece is counted by adopting a water washing method screening test in Highway engineering aggregate test regulation (JTG E42-2005), as shown in figure 2; calculating to obtain the relative crushing potential B of the corresponding test piece by adopting the formula (6)_r：

In the formula, B_tRepresents the total amount of crushing, B_pRepresenting the fracture potential; total amount of crushing B_tThe crushing potential B is determined by the area enclosed by the initial particle size distribution curve and the particle size distribution curve after forming shown in FIG. 2_pDetermined by the area enclosed by the initial particle size distribution curve shown in fig. 2 and the dotted line having a maximum particle size of 0.075 mm.

According to the crushing condition of the particles under different water contents, as shown in FIG. 3, the relative crushing potential B_rAnd (3) linearly changing along with the increase of the water content w, and establishing the relation between the working conditions of different water contents and the working condition of the optimal water content by adopting an equation (7), so that the influence of the water content on the particle crushing is quickly predicted.

Wherein k is the slope of the fitted straight line, and the value of k in this embodiment is 0.042; b is_r(w) represents the relative crushing potential at different water contents, B_rAnd (OMC) represents relative crushing potential under the water content of the OMC, and the influence of the water content on the crushing of the particles is rapidly predicted according to the crushing conditions of the particles under different water contents.

Step S2: according to JTG D50-2017 'design specification for road asphalt pavement' and a dynamic triaxial test, the dynamic resilience modulus of graded crushed stones under the conditions of 3 grades (grades A, B and C), 3 compactibility (93%, 95% and 98%) and 3 water contents (OMC-1%, OMC and OMC + 1%) are respectively obtained by testing, and a three-parameter model of American NCHRP 1-28A is adopted for prediction, as shown in a formula (8).

In the formula, E_yThe modulus of resilience in the axial direction (test piece loading direction); theta_bsThe first invariant, which represents the body stress, or called the stress tensor, is the algebraic sum of the three principal stresses; tau is_octRepresents the octahedral shear stress; p_aIs a reference atmospheric pressure; k is a radical of₁、k₂And k₃Fitting coefficients for the model; from the experimental data, k can be obtained by fitting equation (8) in excel₁、k₂And k₃。

Step S3: obtaining material parameters related to the rebound modulus performance of the graded macadam, such as thickness ratio (G/S) and relative crushing potential (B) according to the step S1_r) The results of Weibull fitting of the shape of AIMS aggregates, and the dry density (. gamma.) obtained by physical Property tests_d) And (w) obtaining three fitting parameters k of the material parameters to the NCHRP 1-28A model through a Bootstrap forest method model in JMP statistical software₁、k₂And k₃The contribution ratio of (a) as shown in fig. 4; model parameter (k) detection by stepwise multiple regression analysis₁、k₂And k₃) The correlation with physical property parameters of various materials finally obtains the dynamic resilience modulus of the graded crushed stone considering particle crushingThe fast prediction formulas are shown in formulas (9) to (11):

k₁＝-1.296+3.082ln(γ_d)-0.434ln(w)+0.238ln(G/S)+0.811λ_S (9)

k₂＝1.175-0.069ln(G/S)+0.400ln(λ_F)-0.172ln(λ_G)-0.210λ_S (10)

k₃＝-1.348+0.453B_r(w)+0.024ln(G/S)+0.159ln(λ_G)-0.071λ_S (11)

wherein, the values of the parameter range which is not between 0 and 1.0 all adopt the form of natural logarithm; the material physical indexes of the multiple groups of graded broken stones and the fitting result of the three parameters are shown in tables 2-4.

TABLE 2 aggregate physical index and three parameter fitting results (grading A)

TABLE 3 aggregate physical Properties index and three parameter fitting results (grading B)

TABLE 4 aggregate physical Properties index and three parameter fitting results (grading C)

The equations (9) to (11) were checked for consistency, and the coefficient R was determined as shown in FIG. 5²Are all more than 85 percent, and the fitting effect can meet the requirements of general engineering.

The method for rapidly estimating the resilience modulus can comprehensively consider the resilience modulus change of the graded crushed stone under the conditions of different grades, water content, particle crushing and aggregate shapes, and has definite physical significance for describing the influence of material parameters on the resilience modulus behavior of the graded crushed stone. By combining the rapid pre-estimation formulas (9) to (11), the modulus of resilience of the graded broken stone is positively correlated with the maximum dry density and negatively correlated with the water content, which is consistent with the actual test result and the engineering experience, and the increase of the coarse particle content (G/S) can improve the framework performance of the graded broken stone in a certain grading range, so that the resilience performance of the test piece is improved; the two-dimensional shape index From2D of the aggregate is improved, so that the sensitivity of the graded macadam to the body stress can be obviously improved; the shear strain of the graded broken stone is greatly related to the grading of materials and particle crushing, the more obvious the particle crushing phenomenon is, and the larger the influence of the shear stress on the rebound deformation is. These conclusions can further guide the grading design and quality optimization of on-site graded crushed stones to achieve higher dynamic modulus of resilience of the graded crushed stones.

The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.