Method and device for optimizing gradation of block stone roadbed construction

Abstract

The present invention proposes a method and device for optimizing gradation of block stone roadbed construction, the method comprising: establishing a statistical calculation model of block stone pile porosity based on fractal theory, determining the relationship curve between the fractal dimension and gradation of the block stone pile, and determining the settlement rate under the compaction conditions through indoor model compaction tests under multiple gradations corresponding to different fractal dimensions, and combining the statistical calculation model of the block stone pile porosity to determine the change of theoretical porosity before and after compaction to obtain a porosity correction coefficient; based on the porosity correction coefficient, setting a porosity range, determining the first gradation range of the block stone pile under the condition that the fractal dimension is not greater than the critical fractal dimension, and screening the gradation range corresponding to the porosity index under the settlement rate index requirement in the first gradation range as the target gradation. The method solves the contradiction between the compaction quality and porosity requirements of block stone roadbed construction, and improves the construction quality of the block stone roadbed and the natural convection cooling efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of road engineering, and in particular relates to a method and a device for optimizing gradation of block stone roadbed construction. Background Art

[0002] The block stone roadbed refers to a roadbed structure in which a block stone material of a certain thickness and a certain range of particle sizes is filled in the roadbed. The negative temperature difference between the top plate and the bottom plate is used to drive the pore air to produce natural convection to achieve the purpose of "actively cooling" the underlying frozen soil. It has the outstanding advantages of high cooling efficiency, convenient construction, and environmental friendliness, and has become one of the most widely used active cooling measures in road projects in permafrost areas at home and abroad.

[0003] In order to ensure that the block stone roadbed can play a role in natural convection cooling, it must have a certain porosity; but at the same time, in order to ensure the construction quality of the block stone roadbed, the block stone pile needs to be compacted, which will inevitably lead to a decrease in the porosity of the block stone and affect its natural convection cooling efficiency. How to find a block stone grading setting that meets the porosity and construction compaction quality requirements for cooling is one of the issues that restrict the further development of the current block stone roadbed design theory and construction methods. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a method and device for optimizing the grading of block stone roadbed construction, which solves the contradiction between the compaction quality and porosity requirements of block stone roadbed construction, and improves the construction quality of block stone roadbed and the natural convection cooling efficiency.

[0005] In one aspect, the present invention provides a method for optimizing gradation of block stone roadbed construction, comprising:

[0006] Based on fractal theory, a statistical calculation model for porosity of rock piles is established, which is used to characterize the relationship between fractal dimension and porosity in natural accumulation state.

[0007] Determine the relationship curve between the fractal dimension and gradation of the block rock pile, and prepare samples according to the filling and compaction standards of the block rock roadbed construction under multiple gradations corresponding to different fractal dimensions;

[0008] Through indoor model compaction test, the settlement rate under compaction conditions is determined, and combined with the porosity statistical calculation model of the rock pile, the change of theoretical porosity before and after compaction is determined to obtain the porosity correction coefficient;

[0009] Based on the porosity correction coefficient, the porosity range is set, and the first gradation range of the rock pile is determined under the condition that the fractal dimension is not greater than the critical fractal dimension;

[0010] The porosity index and the settlement rate index of the block stone roadbed are set, and the porosity index satisfies the porosity range, and the gradation range corresponding to the porosity index that meets the settlement rate index requirement in the first gradation range is selected as the target gradation.

[0011] In one embodiment of the present invention, determining a relationship curve between the fractal dimension and the gradation of the rock pile comprises:

[0012] According to the gradation form of the block pile used in the historical block roadbed construction, the initial gradation curve of the indoor model compaction test is set;

[0013] According to the initial grading curve, different gradations are set;

[0014] The actual porosity corresponding to each gradation under natural accumulation state was measured by water injection method;

[0015] According to the statistical calculation model of the porosity of the rock pile, the fractal dimension corresponding to the actual porosity of each gradation is obtained by reverse calculation;

[0016] The relationship curve between the fractal dimension and the gradation is obtained.

[0017] In one embodiment of the present invention, when preparing samples according to the filling and compaction standard for block stone roadbed construction, the block stone piles of different particle size ranges are mixed to obtain different test samples;

[0018] For each test sample, the indoor model compaction test is conducted to simulate the working pressure range of rolling during the construction of the block rock roadbed and measure the final settlement difference of the block rock pile.

[0019] The settlement rate is calculated based on the final settlement difference and the loose thickness of the test sample before compaction;

[0020] Combined with the sedimentation rate, the total volume change of the test sample before and after compaction is calculated to obtain the theoretical porosity after compaction. The theoretical porosity after compaction is: ; Among them, the volume of all rocks excluding pores can be determined by the statistical calculation model of the porosity of the rock pile, that is, the theoretical porosity of the rock pile in the natural accumulation state is: ; The volume of the rock pile including the pore volume after compaction can be determined in combination with the settlement rate;

[0021] According to the statistical calculation model of the porosity of the rock pile, the theoretical porosity of the test sample in the natural accumulation state is calculated;

[0022] According to the theoretical porosity in the natural stacking state and the theoretical porosity after compaction, the porosity correction coefficient is obtained. The porosity correction coefficient is used to represent the change of porosity before and after compaction, which is the ratio of the theoretical porosity after compaction to the theoretical porosity in the natural stacking state;

[0023] Obtain the relationship curve between the fractal dimension and porosity considering the porosity correction coefficient under the sedimentation rate;

[0024] Each test sample was summarized to obtain the relationship curve between fractal dimension and porosity considering the porosity correction coefficient under different sedimentation rates.

[0025] In one embodiment of the present invention, it also includes:

[0026] According to the porosity statistical calculation model of the rock pile, the theoretical porosity corresponding to each gradation under the natural accumulation state is calculated;

[0027] The actual porosity corresponding to each gradation under natural accumulation state was measured by water injection method;

[0028] If there is at least one primary configuration whose difference between the theoretical porosity and the actual porosity in the natural stacking state does not satisfy the allowable porosity error range, the corresponding fractal dimension is adjusted.

[0029] In one embodiment of the present invention, the method for setting the allowable range of porosity error is:

[0030] For the same graded rock pile, the average value and standard deviation of the fractal dimension are calculated by using the rock pile images collected multiple times.

[0031] According to the average value and standard deviation of the fractal dimension, the average value and standard deviation of the corresponding theoretical porosity are calculated using the porosity statistical calculation model of the rock pile;

[0032] The average value and standard deviation of the theoretical porosity corresponding to the block piles with different gradations are calculated to determine the allowable range of the porosity error.

[0033] In one embodiment of the present invention, based on the multifractal theory, the theoretical porosity of the rock pile under different graded particle size combinations is calculated, and the statistical calculation model of the rock pile porosity is determined, including:

[0034] Based on fractal theory, the particle number density function at different particle sizes that conforms to fractal characteristics is determined;

[0035] Based on the particle number density function, the solid volume of each particle size component is calculated as:

[0036]

[0037] Among them, the fixed volume of the particle size component corresponding to the gradation i is V solid,i , the fractal dimension is D i , the characteristic scale is L i , r min is the minimum size of the particle, r max is the maximum size of the particle;

[0038] Determine the total volume of all the blocks excluding the pores as:

[0039]

[0040] Among them, V solid,total It represents the total volume of all rocks excluding pores;

[0041] The volume of the rock pile including the void volume before compaction is determined as:

[0042]

[0043] Among them, V total It represents the volume of the rock pile including the void volume before compaction;

[0044] Based on the total volume of all the rocks excluding pores and the volume of the rock pile including pores before compaction, the theoretical porosity of the rock pile is determined as: , the theoretical porosity is expressed as:

[0045]

[0046] in, The theoretical porosity of the rock pile under natural accumulation, L x is the characteristic scale of the entire rock pile, D x is the fractal dimension of the entire rock pile.

[0047] In one embodiment of the present invention, the maximum porosity of the block stone roadbed filling is determined based on the loose paving condition construction site verification; the minimum porosity of the block stone roadbed filling is determined based on the block stone roadbed cooling efficiency requirements;

[0048] According to the maximum porosity and the minimum porosity, an initial porosity range is set;

[0049] According to the porosity correction coefficient, the initial porosity range is corrected to obtain a final porosity range;

[0050] The critical fractal dimension is obtained according to the fractal dimensions corresponding to the corrected maximum porosity and the corrected minimum porosity in the final porosity range.

[0051] In one embodiment of the present invention, according to the initial grading curve, the method of setting different gradations is:

[0052] Different gradations are set according to the curvature coefficient and uneven coefficient corresponding to the initial gradation curve, where the uneven coefficient is expressed as: ; The curvature coefficient is expressed as: ; Where: C urepresents the non-uniformity coefficient, C c represents the curvature coefficient; the larger the unevenness coefficient, the smoother the curve; the curvature coefficient reflects the overall continuity of the curve. When the curvature coefficient does not exceed a curvature range, it means that the curve is continuous; when the curvature coefficient exceeds the curvature range, it means that the curve is discontinuous; where, d 60 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 60%, which is the limited particle size, d 10 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 10%, which is the effective particle size; d 30 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 30%; where d 60 ,d 30 ,d 10 The method is to obtain the particle size by sieving the particles through a standard sieve with the preset particle size as the aperture, and calculate the percentage of the mass of the particles passing through the sieve aperture to the total mass of the particles, or calculate the percentage of the volume of the particles passing through the sieve aperture to the total volume of the particles;

[0053] According to the non-uniform coefficient and the curvature coefficient, the initial grading curve is changed in a discontinuous and uneven direction to set different gradations.

[0054] In one embodiment of the present invention, before screening the first grading range of the rock pile according to the porosity index and the sedimentation rate index, the first grading range is pre-screened to obtain the grading range of the rock pile corresponding to the target particle size interval, so as to further screen the grading range corresponding to the porosity index that meets the sedimentation rate index requirement from the grading range of the rock pile corresponding to the target particle size interval as the target grading.

[0055] On the other hand, the present invention also discloses a block stone roadbed construction gradation optimization device, adopting the block stone roadbed construction gradation optimization method, the block stone roadbed construction gradation optimization device comprises:

[0056] A porosity statistical calculation model building module is used to establish a block rock pile porosity statistical calculation model based on fractal theory. The block rock pile porosity statistical calculation model is used to characterize the relationship between the fractal dimension and the porosity under the natural accumulation state;

[0057] A module for determining the relationship curve between fractal dimension and gradation is used to determine the relationship curve between the fractal dimension and gradation of the block stone pile, and to prepare samples according to the filling and compaction standards for block stone roadbed construction under multiple gradations corresponding to different fractal dimensions;

[0058] The test module is used to determine the settlement rate under the compaction conditions through indoor model compaction test, and to determine the change of theoretical porosity before and after compaction in combination with the porosity statistical calculation model of the rock pile to obtain the porosity correction coefficient;

[0059] A primary grading screening module is used to set a porosity range based on the porosity correction coefficient, and determine a first grading range of the rock pile under the condition that the fractal dimension is not greater than the critical fractal dimension;

[0060] The target gradation screening module is used to set the porosity index and settlement index of the block stone roadbed, and the porosity index meets the porosity range, and the gradation range corresponding to the porosity index that meets the settlement index requirement in the first gradation range is screened as the target gradation. Under the condition of ensuring that the porosity meets the requirements, the initial gradation curve is changed to a discontinuous and uneven direction according to the uneven coefficient and curvature coefficient, and different gradations are set.

[0061] It can be seen from the above scheme that the advantages of the present invention are:

[0062] The method for optimizing the gradation of block stone roadbed construction disclosed by the present invention establishes a statistical calculation model for block stone porosity based on fractal theory, and obtains the relationship between the porosity and fractal dimension of block stone piles of different gradations under natural accumulation state; secondly, through indoor large-scale compression tests, the gradation corresponding to different fractal dimensions is established, and the porosity correction coefficient under the standard compaction conditions of the construction filling quality requirements is determined. The settlement rate, a construction quality control indicator, is selected as the target, and the relationship between the porosity and fractal dimension of the block stone pile is combined to obtain the target gradation that satisfies both the settlement rate and the porosity. Based on fractal theory, the present invention constructs a theoretical calculation method for the porosity of block stone roadbed, determines the block stone gradation that satisfies both the porosity and the settlement rate, and thereby controls the construction quality of the block stone roadbed filling. This method can conveniently determine the relationship between the gradation and porosity of the block stone roadbed through the fractal dimension, and solves the contradiction between the compaction quality of the block stone roadbed construction and the porosity requirement. At the same time, through fractal theory, the influence of block stone grading and compaction degree on the overall deformation performance and porosity of the roadbed is comprehensively considered, providing a reliable basis for the overall stability and cooling efficiency evaluation of the block stone roadbed, ensuring the construction quality and service performance of the block stone roadbed, avoiding the problem of thermal melt subsidence caused by insufficient cooling efficiency of the block stone roadbed, and improving the construction quality of the block stone roadbed and the natural convection cooling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The overall process diagram of the block stone roadbed construction gradation optimization method provided by one embodiment of the present invention is shown;

[0064] Figure 2 Shows Figure 1 Detailed flow diagram of step S2;

[0065] Figure 3 A schematic structural diagram of a block stone roadbed construction gradation optimization device provided by an embodiment of the present invention is shown.

[0066] Wherein, the accompanying drawings are marked as follows:

[0067] 200: gradation optimization device for block stone roadbed construction;

[0068] 201: porosity statistical calculation model construction module;

[0069] 202: module for determining fractal dimension and gradation relationship curve;

[0070] 203: test module;

[0071] 204: primary grading screening module;

[0072] 205: Target gradation screening module. DETAILED DESCRIPTION

[0073] In order to make the above features and effects of the present invention more clearly and understandably described, embodiments are given below and described in detail with reference to the accompanying drawings. This specification discloses one or more embodiments that include the features of the present invention. The disclosed embodiments are only for illustration. The scope of protection of the present invention is not limited to the disclosed embodiments, and the present invention is defined by the attached claims.

[0074] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0075] Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0076] Please refer to Figure 1 , which shows a schematic flow chart of a method for optimizing gradation of block stone roadbed construction provided by an embodiment of the present invention.

[0077] A method for optimizing gradation of block stone roadbed construction, comprising at least the following steps:

[0078] Step S1: establishing a statistical calculation model for porosity of a rock pile based on fractal theory, wherein the statistical calculation model for porosity of a rock pile is used to characterize the relationship between the fractal dimension and porosity of the rocks in a natural accumulation state.

[0079] In this embodiment, a statistical calculation model for the porosity of a rock pile is established based on fractal theory. The statistical calculation model for the porosity of a rock pile is used to characterize the relationship between the fractal dimension and porosity of the rocks in a natural stacking state. The calculated porosity and fractal dimension are both the theoretical porosity and theoretical fractal dimension corresponding to the natural stacking state.

[0080] Specifically, firstly, based on the fractal theory, a calculation formula for the theoretical porosity of a rock pile with a single graded particle size under natural stacking conditions is given; then, combined with the multi-fractal theory, an expression for the theoretical porosity of a rock pile with different graded particle size combinations is derived.

[0081] The specific process of calculating the theoretical porosity of a rock pile with a single graded particle size under natural stacking conditions will be introduced in detail below.

[0082] For a line segment of unit length (DT=1), if it is divided into N=2 segments, the length of each segment is r=1 / 2; if it is divided into N=3 segments, the length of each segment is r=1 / 3, and it is obvious that N×r=1. From the perspective of measurement, it is equivalent to using a ruler with a length of r to measure the length of the line segment. Then the measured scale number N and the scale r have the following relationship: N(r)=r -1 = (1 / r) 1 .

[0083] For a two-dimensional square plane with a unit face (DT=2), divide it into N=4 equal parts, then the area of ​​the divided small square is r 2 =1 / 4; if it is divided into N=9 equal parts, the area of ​​the divided small squares is r 2 =1 / 9. Obviously, N×r 2 =1. Then the number of small square measurements N(r) in the two-dimensional plane is: N(r)=r -2 = (1 / r) 2 .

[0084] Similarly, for a unit cube (DT=3), it is divided into N=8 equal parts, and the volume of the divided small cubes is r 3 =1 / 8; if it is divided into N=27 equal parts, the volume of the divided small cube is r 3 =1 / 27. Obviously, N×r 2 = 1. The number of three-dimensional measurements N(r) is: N(r)=r -3 = (1 / r) 3 .

[0085] Given that the dimensions of lines, surfaces, and volumes are 1, 2, and 3 respectively, the above three examples can be summarized as: N(r)=r -D = (1 / r) DThis is one of the basic concepts of fractals. Further generalization, the above formula can be expressed as: N(r)=(L / r) D ; Where L is the characteristic scale of the fractal object and r is the observation scale. Based on this expression, for a particle accumulation body, if it meets the fractal characteristics, then: N(r)=(L / r) D .

[0086] In fractal theory, N(r) is used to describe the number of features (such as particles) observed at a given observation scale r. For an ideal self-similar fractal particle stack, N(r) describes how the number at the observation scale r changes with the scale. Based on N(r) = (L / r) D Then N(r) can be interpreted as the particle number density function at the observation scale r, where each r actually represents a series of particle sizes. In this understanding, N(r) describes the relationship between the number of particles and particle size at scale r.

[0087] Therefore, if r is regarded as the size of the particle, then within the framework of fractal theory, N(r) can indeed be regarded as a function of the number density of particle sizes. This function increases as r decreases, which is consistent with the property of fractal structures that smaller sizes have richer features.

[0088] Here, the volume of the rock is equivalent to that of a sphere, so the rock pile can be equivalent to a granular pile. solid When calculating N(r), the volume V of the particle of size p (r) and then integrate all possible sizes r. In this way, N(r) connects the size distribution of particles with the macroscopic physical properties of the accumulation. That is, N(r) is the particle number density function under different particle sizes d, from which the total solid volume of the particle accumulation is obtained: ;in, ; The final calculation is: , where r min is the minimum size of the particle, r max is the maximum size of the particle, and D is the fractal dimension.

[0089] The volume of the rock pile including the void volume before compaction is: The theoretical porosity of a rock pile with a single graded particle size in a natural accumulation state is obtained as follows: for: , we can calculate: .

[0090] For a rock pile with different graded particle size combinations, the calculation of porosity needs to consider the relative contribution of each particle size component. Multifractal theory can describe the complexity of particle grading. Therefore, in this embodiment, after determining the porosity calculation method of a rock pile with a single graded particle size through the above process, the theoretical porosity of the rock pile with different graded particle size combinations is further calculated based on multifractal theory to determine the statistical calculation model of the porosity of the rock pile.

[0091] Specifically, based on fractal theory, the particle number density function N at different particle sizes that conform to fractal characteristics is determined. i (r). Based on the particle number density function, the solid volume of each particle size component is calculated as: , and then determine the total volume of all blocks excluding pores as: ; Among them, the fixed volume of the particle size component corresponding to the gradation i is V solid,i , the fractal dimension is D i , the characteristic scale is L i , r min is the minimum size of the particle, r max is the maximum size of the particle, V solid,total Represents the total volume of all rocks excluding pores.

[0092] The volume of the rock pile including the pore volume before compaction with different combinations of graded particle sizes is determined as: ; Among them, V total Represents the volume of the rock pile including the void volume before compaction.

[0093] Based on the total volume of all rocks excluding pores and the volume of the rock pile including pores before compaction, the theoretical porosity of the rock pile in the natural accumulation state is determined as: , the theoretical porosity is expressed as: , further simplified to: ;in, The theoretical porosity of the rock pile under natural accumulation, L x is the characteristic scale of the entire rock pile, D x is the fractal dimension of the entire rock pile.

[0094] In this embodiment, through the construction process of the above-mentioned block pile porosity statistical calculation model, the functional relationship between the fractal dimension and the porosity can be obtained, and then the corresponding relationship between different gradations and the fractal dimension function can be obtained.

[0095] Step S2, determining the relationship curve between the fractal dimension and gradation of the block stone pile, and preparing samples according to the filling and compaction standard for block stone roadbed construction under multiple gradations corresponding to different fractal dimensions.

[0096] In this embodiment, after the porosity statistical calculation model of the rock pile is constructed in step S1, the porosity statistical calculation model of the rock pile is further used to determine the relationship curve between the fractal dimension and the gradation of the rock pile. Figure 2 As shown in Figure 2 A specific flow chart of step S2 is shown.

[0097] Specifically, step S21: first, according to the gradation form of the block stone pile used in the historical block stone roadbed construction, the initial gradation curve of the indoor model compaction test is set. Specifically, by statistically analyzing the gradation form of the block stone pile used in the historical block stone roadbed construction, the common gradation form of the block stone used in the early block stone roadbed construction is clarified, and this is used as the initial gradation set for the indoor model compaction test to obtain the initial gradation curve.

[0098] Step S22: According to the initial grading curve, different gradations are set. In practice, according to the relevant knowledge in soil mechanics, the particle grading curve can roughly judge the uniformity of particles. If the curve is steep, it means that the particle sizes are similar, the particles are relatively uniform, and the grading is poor; on the contrary, if the curve is flat, it means that the particle sizes are very different, the particles are uneven, and the grading is good. The grading can be expressed as the non-uniformity coefficient C. u and the curvature coefficient C c To measure, therefore, in one embodiment, the continuity and smoothness of the initial grading curve are determined according to the curvature coefficient and the unevenness coefficient corresponding to the initial grading curve, and then the initial grading curve is changed in the discontinuous and uneven directions to set different gradations.

[0099] Among them: The non-uniformity coefficient is expressed as: ; The curvature coefficient is expressed as: ; Where: C u represents the non-uniformity coefficient, C c represents the curvature coefficient; d 60 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 60%, which is the limited particle size, d 10 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 10%, which is the effective particle size; d 30 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 30%; where d 60 ,d 30 ,d 10It is obtained by sieving, specifically, the granules can be sieved through a standard sieve with the preset particle size as the aperture, and the percentage of the mass of the particles passing through the sieve holes to the total mass of the granules is calculated, or the percentage of the volume of the particles passing through the sieve holes to the total volume of the granules is calculated. For example, the total mass of the granules is 100g, and the granules are passed through a sieve with a preset particle size of 10cm, and the mass of the particles passing through the sieve holes is weighed to be 60g, that is, the mass of the particles with a particle size less than 10cm is 60g, accounting for 60% of the total mass of the granules.

[0100] Unevenness coefficient C u Reflects the distribution of different particle sizes. If the grading curve is continuous, the non-uniformity coefficient C u The larger the d 60 With d 10 The farther apart, the flatter the curve, indicating that the particle group variation range is wider, the particle body is uneven, and the gradation is better. u < 5 is considered as uniform particles with poor gradation; C u >10 is considered as uneven particles with good gradation.

[0101] Curvature coefficient C c It reflects the continuity of the curve as a whole. If the slope of the grading curve is discontinuous, a horizontal segment appears at a certain position on the curve, indicating that the distribution of each particle group is uneven and particles in the particle group range of the horizontal segment are missing. At this time, some intermediate particle sizes are missing, and the particle size grading curve is step-shaped. Under the same compaction conditions, the density is not as high as that of the continuous grading. In one embodiment, when the curvature coefficient does not exceed a curvature range (for example, the curvature range is [1,3]), it indicates that the curve is continuous. When the curvature coefficient exceeds the curvature range (for example, when C c <1 or C c >3), it means the curve is discontinuous.

[0102] In view of the fact that for the block stone roadbed, the block stone gradation cannot be too good, because a certain porosity must be guaranteed to exert its cooling effect; at the same time, the block stone gradation cannot be too poor, because it must also ensure a certain filling strength. Therefore, in one embodiment, under the condition of ensuring that the porosity meets the requirements, according to the uneven coefficient and the curvature coefficient, the initial gradation curve is changed in a discontinuous and uneven direction to set different gradations.

[0103] Then, step S23, further set different gradations, and configure samples of block stone accumulation bodies with different gradations. The actual porosity corresponding to each gradation under the natural accumulation state is measured by the water injection method. It should be noted that, in this embodiment, for the porosity under the natural accumulation state, the actual porosity is measured by the water injection method; the theoretical porosity is measured by the statistical calculation model of the block stone pile porosity, and the two correspond to each other. At the same time, based on the aforementioned statistical calculation model of the block stone pile porosity, the fractal dimension corresponding to the actual porosity of each gradation is obtained by inverse calculation, thereby obtaining the relationship curve between different gradations of the block stone pile and the fractal dimension.

[0104] Step S3: Determine the settlement rate under compaction conditions through indoor model compaction test, and determine the change of theoretical porosity before and after compaction in combination with the porosity statistical calculation model of the rock pile to obtain the porosity correction coefficient.

[0105] In one embodiment of the present invention, when preparing samples according to the filling and compaction standard for block stone roadbed construction, the proportions of block stone piles in different particle size intervals are carried out to obtain different test samples. For each test sample, the working pressure range of the block stone roadbed construction rolling is simulated through the indoor model compaction test, and the final settlement difference of the block stone pile is measured, wherein the final settlement difference is the distance that the block stone pile sinks relative to its original position after compaction. In practice, the indoor model compaction test can be carried out according to the relevant provisions of the "Technical Specifications for Design and Construction of Highways in Permafrost Areas" (JTGT3331-04-2023) and the "Highway Roadbed Design Specifications" (JTGD30-2015). Then, based on the surface settlement method, the settlement rate is calculated according to the final settlement difference and the loose paving thickness of the test sample before compaction, that is, the settlement rate is the ratio of the final settlement difference to the loose paving thickness before compaction. The settlement rate is a control indicator of construction quality and affects the final stability and service life of the block stone roadbed. Further, based on the sedimentation rate, the total volume change of the test sample before and after compaction is calculated to obtain the theoretical porosity after compaction. The theoretical porosity after compaction is: The volume of all rocks excluding pores can be determined by the porosity statistical calculation model of the rock pile, that is, the theoretical porosity of the rock pile under natural accumulation is: ; The volume of the rock pile containing pore volume after compaction can be determined in combination with the settlement rate, that is, the height after compaction is determined according to the settlement rate, and the volume of the rock pile containing pore volume after compaction is determined by the product of the height after compaction and the cross-sectional area of ​​the rock pile. At the same time, according to the aforementioned statistical calculation model for the porosity of the rock pile, the theoretical porosity during natural accumulation before compaction is calculated, and then the porosity correction coefficient is obtained. The porosity correction coefficient is used to represent the change in porosity before and after compaction, which is the ratio of the theoretical porosity after compaction to the theoretical porosity in the natural accumulation state before compaction. It takes into account the settlement effect in actual construction, and ensures that the designed porosity can be achieved under actual construction conditions. In addition, it should be noted that the theoretical porosity calculated in this article according to the aforementioned statistical calculation model for the porosity of the rock pile is the theoretical porosity during natural accumulation before compaction, which is different from the theoretical porosity after compaction.

[0106] Thus, through the above process, the relationship curve between the fractal dimension and porosity considering the porosity correction coefficient under the settlement rate is obtained. Finally, each test sample is summarized to obtain the relationship curve between the fractal dimension and porosity considering the porosity correction coefficient under different settlement rates. The relationship curve between the fractal dimension and porosity considering the porosity correction coefficient under different settlement rates obtained at this time is a curve obtained by correcting the relationship curve between the fractal dimension and porosity determined by the statistical calculation model of the porosity of the rock pile according to the porosity correction coefficient.

[0107] In addition, in one embodiment of the present invention, during the indoor model compaction test, it is necessary to clarify the influence of the stacking method of the same graded stone accumulation body on the fractal dimension through experiments. Due to the slight differences in the stone stacking method, the value of the fractal dimension will be affected, but the actual porosity under the natural accumulation state measured based on the water irrigation method will not change. Therefore, for the same graded stone accumulation body, parallel samples should be set up, the theoretical fractal dimension value of the accumulation body should be calculated using the stone pile image, and the theoretical porosity under the natural accumulation state should be calculated based on the aforementioned stone pile porosity statistical calculation model. If the error between the theoretical porosity and the actual porosity under the natural accumulation state is within the relevant prescribed range, it is considered that the influence of the stone stacking method on the porosity can be ignored. Specifically, based on the statistical calculation model of the porosity of the rock pile, the theoretical porosity corresponding to each gradation under the natural stacking state is calculated; the actual porosity corresponding to each gradation under the natural stacking state is measured by the water injection method; if there is at least one gradation under the natural stacking state, and the difference between the theoretical porosity and the actual porosity does not meet the allowable range of porosity error, the corresponding fractal dimension is adjusted.

[0108] For the allowable range of porosity error, for the same graded rock pile, the average value and standard deviation of the fractal dimension are calculated using the rock pile images collected multiple times. Further, based on the average value and standard deviation of the fractal dimension, the corresponding average value and standard deviation of the theoretical porosity are calculated using the rock pile porosity statistical calculation model. The average value and standard deviation of the theoretical porosity corresponding to the rock piles with different gradations are statistically analyzed to determine the allowable range of the porosity error, and at the same time, the allowable range of the fractal dimension error can be determined.

[0109] Step S4: Based on the porosity correction coefficient, the porosity range is set, and the first gradation range of the rock pile is determined under the condition that the fractal dimension is not greater than the critical fractal dimension.

[0110] In this embodiment, the maximum porosity of the block stone roadbed filling is further determined based on the loose paving condition construction site verification; based on the cooling efficiency requirements of the block stone roadbed, the minimum porosity of the block stone roadbed filling is determined. At this time, the initial porosity range is set according to the maximum porosity and the minimum porosity. Then, according to the porosity correction coefficient, the initial porosity range is corrected to obtain the final porosity range, that is, the final porosity range is the range determined by the corrected maximum porosity and the corrected minimum porosity. In this embodiment, the final porosity range takes into account the porosity correction coefficient obtained under the compaction condition, so that the porosity meets the compaction standard requirements. Then, according to the fractal dimensions corresponding to the corrected maximum porosity and the corrected minimum porosity in the final porosity range, the critical fractal dimension can be obtained. At this time, under the condition that the fractal dimension is not greater than the critical fractal dimension, the first grading range of the block stone pile is determined.

[0111] Step S5, setting the porosity index and the settlement rate index of the block stone roadbed, and the porosity index meets the porosity range, and selecting the gradation range corresponding to the porosity index that meets the settlement rate index requirement in the first gradation range as the target gradation.

[0112] In this embodiment, after the first gradation range is preliminarily screened out through the above step S4, it is necessary to further screen the first gradation range according to the porosity index and the settlement rate index to determine the target gradation. In practice, according to the "Technical Specifications for Design and Construction of Highways in Permafrost Areas" (JTGT3331-04-2023) and the "Highway Roadbed Design Specifications" (JTGD30-2015), combined with the on-site construction test section, the porosity index and settlement rate index of the block stone roadbed are set. Among them, the porosity index satisfies the porosity range determined above, and finally the gradation range corresponding to the porosity index that meets the settlement rate index requirement is screened out from the first gradation range as the target gradation.

[0113] In addition, in practice, there may be a situation where the target gradation determined based on the porosity may not meet the required target particle size range. Therefore, in some embodiments, before the first gradation range of the rock pile is screened according to the porosity index and the sedimentation rate index, the gradation range of the rock pile corresponding to the target particle size range is pre-screened for the first gradation range, and then the gradation range corresponding to the porosity index under the sedimentation rate index requirement is further screened from the gradation range of the rock pile corresponding to the target particle size range as the target gradation. In practice, the target particle size range is designed in accordance with the "Technical Specifications for Design and Construction of Highways in Permafrost Areas" (JTGT3331-04-2023). For example, for the integral roadbed of expressways and first-class highways, the stone particle size range is 300mm~400mm; for other highway-level roadbeds, the stone particle size range is 200mm~300mm. In the current design methods and construction, there are only particle size intervals, and the application of particle size intervals in grading settings is not considered. In this embodiment, the porosity index, particle size intervals and grading settings are combined to further improve the accuracy of grading settings in practical applications, which is sufficiently original.

[0114] In summary, the gradation optimization method for block stone roadbed construction disclosed by the present invention establishes a statistical calculation model for block stone porosity based on fractal theory, and obtains the relationship between the porosity and fractal dimension of block stone piles with different gradations under natural accumulation state; secondly, through indoor large-scale compression tests, the gradation corresponding to different fractal dimensions is established, and the porosity correction coefficient under the standard compaction conditions of construction filling quality requirements is determined. The settlement rate, a construction quality control indicator, is selected as the target, and the relationship between the porosity and fractal dimension of the block stone pile is combined to obtain the target gradation that satisfies both the settlement rate and the porosity. Based on fractal theory, the present invention constructs a theoretical calculation method for the porosity of block stone roadbed, determines the block stone gradation that satisfies both the porosity and the settlement rate, and thereby controls the construction quality of the block stone roadbed filling. This method can conveniently determine the relationship between the gradation and porosity of the block stone roadbed through the fractal dimension, and solves the contradiction between the compaction quality of the block stone roadbed construction and the porosity requirement. At the same time, through fractal theory, the influence of block stone grading and compaction degree on the overall deformation performance and porosity of the roadbed is comprehensively considered, providing a reliable basis for the overall stability and cooling efficiency evaluation of the block stone roadbed, ensuring the construction quality and service performance of the block stone roadbed, avoiding the problem of thermal melt subsidence caused by insufficient cooling efficiency of the block stone roadbed, and improving the construction quality of the block stone roadbed and the natural convection cooling efficiency.

[0115] In addition, the present invention further combines the porosity index, the particle size range and the grading setting, which can provide a more sufficient scientific theoretical basis for the current block stone roadbed design and improve the accuracy of the design calculation.

[0116] In addition, corresponding to the aforementioned embodiment of optimizing the gradation of block stone roadbed construction, the present invention also provides an embodiment of a device for optimizing the gradation of block stone roadbed construction.

[0117] See also Figure 3 , Figure 3 The schematic diagram of the structure of the gradation optimization device for block stone roadbed construction is shown.

[0118] The block stone roadbed construction gradation optimization device 200 at least comprises a porosity statistical calculation model building module 201, a fractal dimension and gradation relationship curve determination module 202, a test module 203, a primary gradation screening module 204, and a target gradation screening module 205.

[0119] The porosity statistical calculation model building module 201 is used to establish a block rock pile porosity statistical calculation model based on fractal theory, and the block rock pile porosity statistical calculation model is used to characterize the relationship between the fractal dimension and the porosity under the natural accumulation state.

[0120] The fractal dimension and gradation relationship curve determination module 202 is used to determine the fractal dimension and gradation relationship curve of the block stone pile, and prepare samples according to the filling and compaction standards of the block stone roadbed construction under multiple gradations corresponding to different fractal dimensions.

[0121] The test module 203 is used to determine the settlement rate under the compaction conditions through indoor model compaction test, and determine the change of theoretical porosity before and after compaction in combination with the porosity statistical calculation model of the rock pile to obtain the porosity correction coefficient.

[0122] The primary gradation screening module 204 is used to set the porosity range based on the porosity correction coefficient, and determine the first gradation range of the rock pile under the condition that the fractal dimension is not greater than the critical fractal dimension.

[0123] The target gradation screening module 205 is used to set the porosity index and the settlement rate index of the block stone roadbed, and the porosity index satisfies the porosity range, and screens the gradation range corresponding to the porosity index that meets the settlement rate index requirement in the first gradation range as the target gradation.

[0124] In addition, those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of each module of the described device can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0125] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for optimizing gradation of block stone roadbed construction, characterized in that: Include: The theoretical porosity of the rock pile under different graded particle size combinations is calculated based on the multi-fractal theory, and a statistical calculation model for the porosity of the rock pile is established. The statistical calculation model for the porosity of the rock pile is used to characterize the relationship between the fractal dimension and the porosity under the natural accumulation state, including: Based on fractal theory, the particle number density function at different particle sizes that conforms to fractal characteristics is determined. The particle number density function is used to characterize the relationship between the particle number and the particle size. Calculate the solid volume of each particle size component based on the particle number density function to determine the total volume of all blocks excluding pores; Based on the total volume of all the rocks without pores and the volume of the rock pile including pores before compaction, the theoretical porosity of the rock pile in the natural accumulation state is determined as: , the theoretical porosity is expressed as: Among them, the fixed volume of the particle size component corresponding to the gradation i is V solid,i ,and ; then the total volume of all the blocks excluding pores is V solid,total ,and ; The volume of the rock pile including the pore volume before compaction is V total ,and ; The fractal dimension corresponding to gradation i is D i , the characteristic scale is L i , r min is the minimum size of the particle, r max is the maximum size of the particle; where Indicates the theoretical porosity of the rock pile under natural accumulation; L x is the characteristic scale of the entire rock pile, D x is the fractal dimension of the entire rock pile; Determine the relationship curve between the fractal dimension and gradation of the block rock pile, and prepare samples according to the filling and compaction standards of the block rock roadbed construction under multiple gradations corresponding to different fractal dimensions; Through indoor model compaction test, the settlement rate under the compaction condition is determined, and combined with the porosity statistical calculation model of the rock pile, the change of theoretical porosity before and after compaction is determined to obtain the porosity correction coefficient; Based on the porosity correction coefficient, a porosity range is set, and a first gradation range of the rock pile is determined under the condition that the fractal dimension is not greater than the critical fractal dimension; The porosity index and the settlement rate index of the block stone roadbed are set, and the porosity index satisfies the porosity range, and the gradation range corresponding to the porosity index that meets the settlement rate index requirement in the first gradation range is selected as the target gradation.

2. The method for optimizing gradation of block stone roadbed construction according to claim 1, characterized in that: Determine the relationship curve between the fractal dimension and gradation of the rock pile, including: According to the gradation form of the block pile used in the historical block roadbed construction, the initial gradation curve of the indoor model compaction test is set; According to the initial grading curve, different gradations are set; The actual porosity corresponding to each gradation under natural accumulation state was measured by water injection method; According to the statistical calculation model of the porosity of the rock pile, the fractal dimension corresponding to the actual porosity of each gradation is obtained by back-calculation; A relationship curve between the fractal dimension and the gradation is obtained.

3. The method for optimizing gradation of block stone roadbed construction according to claim 1, characterized in that: When preparing samples according to the filling and compaction standards for block stone roadbed construction, the proportions of block stone piles in different particle size ranges are carried out to obtain different test samples; For each test sample, the indoor model compaction test is conducted to simulate the working pressure range of rolling during the construction of the block rock roadbed, and the final settlement difference of the block rock pile is measured; Calculate the settlement rate based on the final settlement difference and the loose thickness of the test sample before compaction; Combined with the sedimentation rate, the total volume change of the test sample before and after compaction is calculated to obtain the theoretical porosity after compaction. The theoretical porosity after compaction is: ; Calculate the theoretical porosity of the test sample in a natural stacking state according to the statistical calculation model of the porosity of the rock pile; According to the theoretical porosity in the natural stacking state and the theoretical porosity after compaction, the porosity correction coefficient is obtained. The porosity correction coefficient is used to represent the change of porosity before and after compaction, which is the ratio of the theoretical porosity after compaction to the theoretical porosity in the natural stacking state; Obtaining a relationship curve between the fractal dimension and the porosity considering the porosity correction coefficient under the sedimentation rate; Each test sample was summarized to obtain the relationship curve between fractal dimension and porosity considering the porosity correction coefficient under different sedimentation rates.

4. The method for optimizing gradation of block stone roadbed construction according to claim 1, characterized in that: Also includes: According to the porosity statistical calculation model of the rock pile, the theoretical porosity corresponding to each gradation under the natural accumulation state is calculated; The actual porosity corresponding to each gradation under natural accumulation state was measured by water injection method; If there is at least one primary configuration whose difference between the theoretical porosity and the actual porosity in the natural stacking state does not satisfy the allowable porosity error range, the corresponding fractal dimension is adjusted.

5. The method for optimizing gradation of block stone roadbed construction according to claim 4, characterized in that: The method for setting the allowable range of porosity error is: For the same graded rock pile, the average value and standard deviation of the fractal dimension are calculated by using the rock pile images collected multiple times. According to the average value and standard deviation of the fractal dimension, the average value and standard deviation of the corresponding theoretical porosity are calculated using the statistical calculation model of the porosity of the rock pile; The average value and standard deviation of the theoretical porosity corresponding to the block rock piles with different gradations are calculated to determine the allowable range of the porosity error.

6. The method for optimizing gradation of block stone roadbed construction according to claim 1, characterized in that: Based on the loose paving condition construction site verification, the maximum porosity of the block stone roadbed filling is determined; based on the cooling efficiency requirements of the block stone roadbed, the minimum porosity of the block stone roadbed filling is determined; According to the maximum porosity and the minimum porosity, an initial porosity range is set; According to the porosity correction coefficient, the initial porosity range is corrected to obtain a final porosity range; The critical fractal dimension is obtained according to the fractal dimensions corresponding to the corrected maximum porosity and the corrected minimum porosity in the final porosity range.

7. The method for optimizing gradation of block stone roadbed construction according to claim 2, characterized in that: According to the initial grading curve, the method of setting different gradations is: Different gradations are set according to the curvature coefficient and the unevenness coefficient corresponding to the initial gradation curve, wherein: The non-uniformity coefficient is expressed as: ; The curvature coefficient is expressed as: ; Where: C u represents the non-uniformity coefficient, C c represents the curvature coefficient; the larger the unevenness coefficient, the flatter the curve; the curvature coefficient reflects the overall continuity of the curve. When the curvature coefficient does not exceed a curvature range, it indicates that the curve is continuous; when the curvature coefficient exceeds the curvature range, it indicates that the curve is discontinuous; where, d 60 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 60%, which is the limited particle size, d 10 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 10%, which is the effective particle size; d 30 Indicates that the percentage of particles with a particle size smaller than the preset particle size is 30%; where d 60 d 30 d 10 The method is to obtain the particle size by sieving the particles through a standard sieve with the preset particle size as the aperture, and calculate the percentage of the mass of the particles passing through the sieve aperture to the total mass of the particles, or calculate the percentage of the volume of the particles passing through the sieve aperture to the total volume of the particles; According to the non-uniform coefficient and the curvature coefficient, the initial grading curve is changed in a discontinuous and uneven direction to set different gradations.

8. The method for optimizing gradation of block stone roadbed construction according to claim 1, characterized in that: Before screening the first grading range of the rock pile according to the porosity index and the sedimentation rate index, the first grading range is screened in advance to obtain the grading range of the rock pile corresponding to the target particle size interval, so as to further screen the grading range corresponding to the porosity index that meets the sedimentation rate index requirement from the grading range of the rock pile corresponding to the target particle size interval as the target grading.

9. A device for optimizing gradation of block stone roadbed construction, characterized in that: The block stone roadbed construction gradation optimization method according to any one of claims 1 to 8 is adopted, and the block stone roadbed construction gradation optimization device comprises: The porosity statistical calculation model construction module is used to calculate the theoretical porosity of the rock pile under different graded particle size combinations based on the multi-fractal theory, and establish a rock pile porosity statistical calculation model. The rock pile porosity statistical calculation model is used to characterize the relationship between the fractal dimension and porosity under the natural accumulation state, and specifically includes: Based on fractal theory, the particle number density function at different particle sizes that conforms to fractal characteristics is determined. The particle number density function is used to characterize the relationship between the particle number and the particle size. Calculate the solid volume of each particle size component based on the particle number density function to determine the total volume of all blocks excluding pores; Based on the total volume of all the rocks without pores and the volume of the rock pile including pores before compaction, the theoretical porosity of the rock pile in the natural accumulation state is determined as: , the theoretical porosity is expressed as: Among them, the fixed volume of the particle size component corresponding to the gradation i is V solid,i ,and ; then the total volume of all the blocks excluding pores is V solid,total ,and ; The volume of the rock pile including the pore volume before compaction is V total ,and ; The fractal dimension corresponding to gradation i is D i , the characteristic scale is L i , r min is the minimum size of the particle, r max is the maximum size of the particle; where Indicates the theoretical porosity of the rock pile under natural accumulation; L x is the characteristic scale of the entire rock pile, D x is the fractal dimension of the entire rock pile; A module for determining the relationship curve between fractal dimension and gradation is used to determine the relationship curve between the fractal dimension and gradation of the block stone pile, and to prepare samples according to the filling and compaction standards for block stone roadbed construction under multiple gradations corresponding to different fractal dimensions; The test module is used to determine the settlement rate under the compaction condition through indoor model compaction test, and determine the change of theoretical porosity before and after compaction in combination with the porosity statistical calculation model of the rock pile to obtain the porosity correction coefficient; A primary grading screening module is used to set a porosity range based on the porosity correction coefficient, and determine a first grading range of the rock pile under the condition that the fractal dimension is not greater than the critical fractal dimension; The target gradation screening module is used to set the porosity index and the settlement rate index of the block stone roadbed, and the porosity index satisfies the porosity range, and the gradation range corresponding to the porosity index that meets the settlement rate index requirement in the first gradation range is screened as the target gradation.

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