WO2022083786A2 - Méthode de prédiction rapide du module d'élasticité dynamique de pierres concassées calibrées tenant compte du broyage des particules - Google Patents

Méthode de prédiction rapide du module d'élasticité dynamique de pierres concassées calibrées tenant compte du broyage des particules Download PDF

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WO2022083786A2
WO2022083786A2 PCT/CN2021/131843 CN2021131843W WO2022083786A2 WO 2022083786 A2 WO2022083786 A2 WO 2022083786A2 CN 2021131843 W CN2021131843 W CN 2021131843W WO 2022083786 A2 WO2022083786 A2 WO 2022083786A2
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crushed stone
graded crushed
index
parameter
formula
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PCT/CN2021/131843
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WO2022083786A3 (fr
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张军辉
李崛
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长沙理工大学
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Publication of WO2022083786A3 publication Critical patent/WO2022083786A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N3/08—Investigating strength properties of solid materials by application of mechanical stress by applying steady tensile or compressive forces
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

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  • the invention belongs to the technical field of road engineering, and relates to a rapid prediction method for the dynamic resilience modulus of graded crushed stone considering particle crushing.
  • the particles are prone to crushing under the combined action of load stress and environmental factors, resulting in the deterioration of the crushed stone gradation and mechanical properties.
  • the properties decay, and its resistance to deformation decreases. If the crushed stone material with unreasonable gradation, poor angularity and serious crushing is selected, it will lead to different degrees of settlement and deformation of the roadbed structure after overlaying, which will not only fail to improve the performance of the roadbed, but also greatly reduce the service life of the road structure. Endanger driving safety. Therefore, based on the strategic goals of road engineering stability and durability, it is of great significance to scientifically evaluate the rebound deformation properties of graded crushed stone materials.
  • the test method for the elastic modulus of granular materials proposed in the new version of the specification requires the use of an expensive dynamic triaxial tester; in addition, the crushed stone material used for the test is loose, the specimen preparation is difficult, and the results are highly discrete. These factors limit the application of dynamic triaxial tester in road engineering.
  • the existing literature such as the meso-thermodynamic mechanism of granular material crushing evolution path, Shen Chaomin et al., 2019.1
  • the research on the evolution law of the elastic modulus of graded crushed stone under different molding methods and moisture content conditions is not in-depth, and it is difficult to be accurate and fast. Prediction of dynamic elastic modulus of graded crushed stone.
  • the present invention provides a method for rapidly predicting the dynamic resilience modulus of graded crushed stone considering particle crushing, which can conveniently and accurately obtain the dynamic resilience modulus of graded crushed stone, and scientifically guide the graded crushed stone.
  • the design and construction of the pavement structure ensures the quality of the project and solves the problems existing in the prior art.
  • the technical scheme adopted in the present invention is a method for rapidly predicting the dynamic elastic modulus of graded crushed stone considering particle crushing, which is specifically carried out according to the following steps:
  • Step S1 Determine the physical parameters of multiple groups of graded crushed stone under the conditions of different gradation, compaction and moisture content, that is, the thickness ratio G/S, the relative crushing potential B r (w) under different moisture content, and the two-dimensional shape
  • Step S2 According to the dynamic triaxial test, the dynamic elastic modulus of multiple groups of graded crushed stones in step S1 are respectively measured, and the NCHRP 1-28A three-parameter model is used for prediction.
  • the specific formula is as follows:
  • Step S3 Determine the contribution ratio of all the physical parameters of each group of graded crushed stone to the fitting parameters k 1 , k 2 and k 3 of the three-parameter model, and use the stepwise multiple regression analysis method to determine the fitting parameters k 1 to k of the model 3 and the correlations with various physical parameters, respectively, to obtain a fast prediction formula.
  • the determination of dry density ( ⁇ d ), moisture content (w), thickness ratio (G/S) and particle breakage are all conventional test methods, which can be tested in general production departments or construction sites, while for the collection
  • the present invention adopts the statistical method to predict the overall aggregate, and does not need to carry out a large number of AIMS tests, but only needs to carry out regular sampling inspection of the crushed stone in the reclaiming field, so it does not affect the normal grading. Crushed stone construction and less expensive.
  • the method for rapidly predicting the dynamic resilience modulus of graded crushed stone of the present invention can conveniently and accurately obtain the dynamic resilience modulus of graded crushed stone, and guides the grading more conveniently.
  • the design and construction of crushed stone in the pavement structure, and the method can be extended to the design and detection of other granular materials, and has broad application value.
  • Figure 1 is a schematic diagram of the shape parameter fitting results.
  • Figure 2 is a schematic diagram of the calculation of the relative crushing potential.
  • Figure 3 is a schematic diagram of the relationship between relative crushing potential and moisture content.
  • Figure 4 is a schematic diagram of the contribution rate of material parameters to model parameters.
  • FIG. 5 is a schematic diagram of the fitting result of the fast prediction model.
  • the embodiment of the present invention considers a method for rapidly predicting the dynamic elastic modulus of graded crushed stone with particle crushing, and is specifically carried out according to the following steps:
  • Step S1 Determine the physical parameters of multiple groups of graded crushed stone under the conditions of different gradation, compaction, and moisture content, that is, the thickness ratio G/S, the relative crushing potential B r (w) under different moisture content, ⁇ F , ⁇ G , ⁇ S and dry density ⁇ d , moisture content w;
  • S1.1 Three kinds of continuously graded graded crushed stone specimens are prepared from limestone aggregates. The initial particle gradation of the specimens is shown in Table 1, and the thickness ratio G of the three grades is calculated by formula (1). /S, 1.22, 1.56, and 1.97, respectively. Then, the optimal water content (OMC) and maximum dry density ( ⁇ dmax ) of the graded crushed stone were obtained through the indoor compaction test. The OMC and ⁇ dmax corresponding to the three graded crushed stones were: 4.96% and 2.261g, respectively. /cm 3 , 4.81% and 2.307 g/cm 3 , 4.61% and 2.331 g/cm 3 .
  • p 4 and p 200 represent the passing percentage of No. 4 sieve (4.75 mm) and No. 200 sieve (0.075 mm), respectively.
  • ⁇ is the measurement angle
  • R ⁇ is the radius of the aggregate in the ⁇ angle direction
  • ⁇ is the measurement angle increment, which is 4°
  • n is the total number of edge points of the aggregate image
  • i is the edge of the aggregate image.
  • d L is the length of the minimum circumscribed cuboid of coarse aggregate
  • d I is the width of the minimum circumscribed cuboid
  • d S is the height of the minimum circumscribed cuboid
  • ⁇ i represents the measurement angle of the ith point
  • ⁇ i+3 Indicates the measurement angle of the i+3th point.
  • the Weibull cumulative probability distribution was used to fit and analyze the AIMS results of the above three graded aggregates.
  • the proportion parameter ⁇ is related, and the fullness of the curve corresponding to the shape parameter a is mainly affected by the number of samples. Therefore, the proportion parameter ⁇ is used as the shape characteristic quantity to evaluate the influence law of the shape parameter on the elastic modulus of graded crushed stone.
  • F is the cumulative probability
  • x is the two-dimensional shape index, gradient edge index or sphericity
  • ⁇ is the scale parameter
  • ⁇ is the shape parameter.
  • x is a general expression, x is a statistical parameter to be solved, and represents any one of the two-dimensional shape index, gradient edge index, or sphericity index.
  • x is a two-dimensional shape index (Form2D)
  • the fitting results are shown in Figure 1; when x is a gradient edge index or sphericity, the fitting results are basically consistent with Figure 1.
  • the proportional parameter ⁇ and the shape parameter ⁇ can be obtained at the same time by fitting with the formula (5).
  • the scale parameter ⁇ is the shape feature parameter of the two-dimensional shape index, denoted as ⁇ F
  • the shape parameter ⁇ is the curve fullness parameter ⁇ F
  • the scale parameter ⁇ is the gradient
  • the shape feature parameter of the edge index is denoted as ⁇ G
  • the shape parameter ⁇ is the curve fullness parameter ⁇ G
  • the proportional parameter ⁇ is the shape feature parameter of the sphericity index, denoted as ⁇ S
  • the shape The parameter ⁇ is the curve fullness parameter ⁇ S .
  • B t represents the total amount of crushing
  • B p represents the crushing potential
  • the total amount of crushing B t is determined by the area enclosed by the initial particle gradation curve shown in Figure 2 and the particle gradation curve after molding
  • the crushing potential B p is determined by The initial particle gradation curve shown in Figure 2 and the area enclosed by the dashed line with the maximum particle size of 0.075 mm are determined.
  • the relative crushing potential B r changes linearly with the increase of moisture content w. Therefore, the influence of moisture content on particle breakage can be quickly predicted.
  • k is the slope of the fitted straight line, and the value of k in this example is 0.042;
  • B r (w) represents the relative crushing potential under different moisture contents, and
  • B r (OMC) represents the relative crushing potential under OMC moisture content.
  • Step S2 According to JTG D50-2017 "Specification for Design of Highway Asphalt Pavement” and dynamic triaxial test, 3 kinds of gradations (gradation A, B and C) and 3 kinds of compaction degrees (93%, 95% and 98%) and three moisture contents (OMC-1%, OMC and OMC+1%), and the dynamic resilience modulus of graded crushed stone was predicted by the American NCHRP 1-28A three-parameter model, see formula ( 8).
  • E y represents the elastic modulus in the axial direction (the loading direction of the specimen);
  • ⁇ bs represents the bulk stress, or the first invariant of the stress tensor, which is the algebraic sum of the three principal stresses;
  • ⁇ oct represents the eight Surface shear stress;
  • Pa is the reference atmospheric pressure;
  • k 1 , k 2 and k 3 are model fitting coefficients; based on the experimental data, k 1 , k 2 and k 3 can be obtained by fitting formula (8) through excel.
  • Step S3 According to Step S1, obtain the material parameters related to the elastic modulus performance of graded crushed stone, such as thickness ratio (G/S), relative crushing potential (B r ), AIMS aggregate shape Weibull fitting result, and pass
  • the dry density ( ⁇ d ) and water content (w) obtained from the physical property test were obtained through the Bootstrap forest model in the JMP statistical software to obtain the three fitting parameters k 1 , k 2 and the NCHRP 1-28A model.
  • the contribution ratio of k 3 is shown in Figure 4; the stepwise multiple regression analysis method is used to detect the correlation between the model parameters (k 1 , k 2 and k 3 ) and the physical parameters of various materials, and finally the gradation crushing considering the particle crushing is obtained.
  • the quick prediction formulas for the dynamic elastic modulus of rock are shown in equations (9) to (11):
  • the values of the parameters that are not between 0 and 1.0 are in the form of natural logarithms.
  • Consistency test is carried out on formulas (9) to (11), as shown in Figure 5, the determination coefficient R 2 is all greater than 85%, and the fitting effect can meet the needs of general engineering.
  • the method for rapidly predicting the resilience modulus adopted in the present invention can comprehensively consider the variation of resilience modulus of graded crushed stone under different gradation, moisture content, particle crushing and aggregate shape, and has clear physical meaning to Describe the effect of material parameters on the behavior of the elastic modulus of graded crushed stone. Combined with the quick prediction formulas (9) to (11), it can be seen that the elastic modulus of graded crushed stone is positively correlated with the maximum dry density and negatively correlated with the moisture content, which is consistent with the actual test results and engineering experience.

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Abstract

L'invention concerne une méthode de prédiction rapide du module d'élasticité dynamique de pierres concassées calibrées tenant compte du broyage des particules, ladite méthode comprenant en particulier les étapes consistant à : déterminer des paramètres de propriété physique d'une pluralité de groupes de pierres concassées calibrées dans différents calibres, différents degrés de compacité et différentes conditions de teneurs en eau ; sur la base d'un essai triaxial dynamique, mesurer respectivement les modules d'élasticité dynamiques de la pluralité de groupes de pierres concassées calibrées, à l'aide d'un modèle à trois paramètres pour effectuer une prédiction, et sur la base des modules d'élasticité dynamiques de chaque groupe de pierres concassées calibrées obtenus lors de l'essai triaxial dynamique, ajuster le modèle à trois paramètres pour obtenir des coefficients d'ajustement de modèle k1, k2 et k3 ; et déterminer des rapports de contribution de tous les paramètres de propriété physique de chaque groupe de pierres concassées calibrées sur les paramètres d'ajustement k1, k2 et k3 du modèle à trois paramètres, et utiliser une méthode d'analyse par régression multiple pas à pas pour déterminer la corrélation entre les paramètres d'ajustement k1-k3 du modèle et chaque paramètre de propriété physique, et obtenir ainsi une formule de prédiction rapide. La présente invention permet d'obtenir de manière pratique et précise des modules d'élasticité dynamiques de pierres concassées calibrées, ce qui permet de guider de manière scientifique la conception et la fabrication de pierres concassées calibrées dans des structures de revêtement routier, et de garantir la qualité de l'ingénierie.
PCT/CN2021/131843 2021-06-28 2021-11-19 Méthode de prédiction rapide du module d'élasticité dynamique de pierres concassées calibrées tenant compte du broyage des particules WO2022083786A2 (fr)

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