CN112376533A

CN112376533A - Method for detecting roadbed deep filling quality by surface wave method

Info

Publication number: CN112376533A
Application number: CN202011348582.4A
Authority: CN
Inventors: 杨相如; 李林峰
Original assignee: Fujian Chuanzheng Communications College
Current assignee: Fujian Chuanzheng Communications College
Priority date: 2020-11-26
Filing date: 2020-11-26
Publication date: 2021-02-19

Abstract

The invention provides a method for detecting roadbed deep filling quality by a surface wave method, which comprises the following steps: s1, selecting a test road section to carry out backfill paving detection; s2, arranging different wave velocity test points at equal distances in a test road section; step S3, after the compactness is measured, the worker measures the moisture content of the giant soil; s4, analyzing and researching the relation between the unearthed depth, the water content of different fillers, the grain composition factor and the propagation characteristic of Rayleigh waves by analyzing the test data by adopting a genetic algorithm; step S5, calculating and analyzing the influence of the boundary condition on the wave propagation characteristic by adopting a finite element method; step S6, researching the relation between wet density, dry density and porosity ratio factors of different material types and Rayleigh wave velocity through a fitting formula; step S7, establishing the correlation between the wave velocity characteristic and the soil characteristic parameter by combining the Rayleigh wave method, the irrigation method and the surface settlement observation; the invention can detect the quality and the compactness of the backfill material.

Description

Method for detecting roadbed deep filling quality by surface wave method

Technical Field

The invention relates to the technical field of roadbed deep filling quality detection, in particular to a method for detecting roadbed deep filling quality by a surface wave method.

Background

At present, the construction of the highway in China is usually carried out in advance, and a gap is left behind the table backs at two ends of the construction after the general roadbed is formed in the construction. When the gap is reserved, the step reserved in a layered manner often does not reach the width of 0.20 multiplied by 1.5 m. When the structure is constructed on the gap of the platform back, the compaction is not in place due to the narrow working surface of the compaction machine, and particularly, the degree of compaction which is required by the standards of not less than 96% under the platform, on the rear side of the platform, on the inner side of the wing wall is difficult to achieve. In addition, the compaction layering is not obvious when part of construction units use the dumping filling construction, or a layering compaction method is not used at all, so that the compaction quality of the roadbed at the position is reduced, and the roadbed at the position is compressed and settled under the repeated action of the traffic load after traffic is passed. With the development of highways to mountainous areas, the proportion of structures such as culverts, channels, bridges and the like in highway mileage is larger and larger, and the phenomenon of jumping at the head of a bridge (culvert) caused by low backfill quality is expected to bring enough attention.

The currently common roadbed filling compactness detection methods and means comprise a cutting ring method, a water irrigation method, a sand irrigation method and the like. Although the methods are simple to operate, the structural layer is damaged in the testing process, the influence of human factors is large, the water content measuring time is long, and the compaction degree of the deep soil body cannot be obtained under the condition that backfilling is finished. Therefore, the methods are severely limited in practical use, and further discussion is needed for the representative conclusion. In the quality detection of the backfill, except for the method, for the condition that the number of coarse particles and large particles is large, because the maximum dry density is difficult to obtain, a surface sedimentation method, namely, sedimentation amount or sedimentation rate is often adopted as a control index, but an evaluation method for carrying out compaction evaluation by adopting two indexes of sedimentation amount and sedimentation rate in the surface sedimentation method is lacked in the specification, and the sedimentation amount or sedimentation rate is not properly controlled independently and needs to be jointly evaluated by combining construction process parameters.

Disclosure of Invention

In view of the above, the present invention provides a method for detecting the quality of deep filling of a roadbed by a surface wave method, which can detect the quality and compactness of backfill.

The invention is realized by adopting the following method:

a method for detecting the deep filling quality of a roadbed by a surface wave method is characterized by comprising the following steps:

s1, selecting a test road section to carry out backfill paving detection, and measuring and determining soil layer technical parameters through a rolling machine after the test road section is paved;

s2, arranging different wave speed test points at a middle distance in the test road section for testing the wave speed conditions of the backfill material under different compact states, and obtaining the compaction degree of the backfill material in the test road section by adopting a sand filling method or a water filling method at the positions of the wave speed test points;

step S3, after the compactness is measured, the worker measures the moisture content of the giant soil, the moisture content of the particles larger than 60mm and the moisture content of the particles smaller than 60mm are separately measured by taking 60mm as a boundary, and the overall moisture content is obtained through calculation;

s4, analyzing and researching the relation among the unearthed depth, the water content of different fillers, grain composition factors and Rayleigh wave propagation characteristics by adopting a genetic algorithm;

s5, calculating and analyzing the influence of the boundary condition on the wave propagation characteristic by adopting a finite element method based on a semi-infinite space through a Rayleigh wave theory;

s6, researching the relation between wet density, dry density and pore ratio factors of different material types and Rayleigh wave speed through a fitting formula, and analyzing the propagation and energy change characteristics of Rayleigh waves in different types of backfill materials;

and S7, combining the steps S2 to S6 and the surface settlement observation method, the relevance between the Rayleigh wave velocity characteristics and the soil body characteristic parameters can be established, and therefore quality detection of roadbed deep filling is achieved.

Further, in the step S3, the water holding capacity of the particles in the macro, coarse and fine particles of the backfill is different, and the water contents of the particles larger than 60mm and the particles smaller than 60mm are measured with 60mm as a boundary, respectively, and the overall water content is determined by the following equation:

w＝w_fp_f+w_c(1-p_f)

in the formula, w is the integral water content, and the percentage is calculated to be 0.01; w is a_fThe water content of the granules is less than 60mm,%; w is a_cIs in the form of granules larger than 60mmWater rate,%; p is a radical of_fIs the ratio of the dry mass of the particles smaller than 60mm to the dry mass of the total material.

Further, the rayleigh wave propagation characteristic in step S3 is that, in a homogeneous isotropic elastic medium below a free interface, real parts of a horizontal component Dx and a vertical displacement component Dz of the rayleigh wave vibration can be respectively represented by the following expressions:

in the formula, K_R、K_PAnd K_SThe number of circles of Rayleigh waves, longitudinal waves and transverse waves respectively; x and z are the propagation distance and depth, respectively; the attenuation coefficients a and b are related to the wave number respectively,

b is an energy-dependent constant; from the above formula, it can be seen that the particle displacement of the rayleigh surface wave is not only related to its frequency, propagation velocity, depth, but also closely related to the properties of the medium.

Further, the wave velocity test in step S2 is to convert the apparent velocity of rayleigh waves into the layer velocity of rayleigh waves and give the velocity of shear waves for calculation, and the calculation formula of converting the apparent velocity of rayleigh waves into the layer velocity is as follows: apparent velocity V_RAs the depth H increases and increases,

apparent velocity V_RAs the depth H decreases with an increase in depth,

in the formula, H_n: the depth of the nth dispersion point; h_n-1: depth of the nth-1 dispersion point; v_R，n: averaging Rayleigh wave velocity in a depth range above the nth frequency dispersion point;V_R，n-1: average Rayleigh wave velocity in the depth range above the nth-1 frequency dispersion point; v_R，m: h th_n－H_n-1The Rayleigh wave velocity of the rock-soil mass between depths is the Rayleigh wave layer velocity.

Further, the genetic algorithm in step S4 is a calculation model of a biological evolution process simulating natural selection and genetic mechanism of darwinian biological evolution theory, and is a method for searching an optimal solution by simulating a natural evolution process, and the genetic algorithm solving process includes the following steps:

step S40, model parameter coding, setting the coding length as k, equally dividing the variation interval of each parameter in the model into 2^k ^-1Sub-interval, then the model parameter variation space is discretized into (2)^k)^pA parameter grid point. Each grid point is regarded as an individual and represents one possible value state of p parameters of the model, p k-bit binary systems are used for representing, and the decimal number is converted into a binary string through coding;

step S41, generating the first individual, namely the initial population, from the step (2)^k)^pRandomly selecting n points from the grid points as initial parents;

step S42, calculating the fitness of the individual, taking the objective function value as the fitness value of the individual, judging whether the objective function value meets the optimization criterion, if yes, outputting the best individual and the optimal solution represented by the best individual, and finishing the calculation; if not, go to step S43;

s43, copying the individuals with high fitness and adding the copied individuals into a new group, and deleting the individuals with low fitness;

s44, randomly selecting individual pairs, and performing segment cross transposition to generate new individual pairs;

step S45, randomly changing a certain character of a certain individual so as to obtain a new individual;

step S46, the n sub-generation individuals obtained in the previous step are used as new parents, the step S32 to the step S34 are repeated, and the next generation → reevaluation → selection → crossing → variation is generated until the criterion function Q does not change any more or the minimum Q value in the new generation and the minimum Q value in the previous generation meet certain precision requirements, and the individual corresponding to the coding string with the minimum Q value in the last generation is the optimal individual;

step S47, converting the binary string vector into decimal to obtain the optimal solution, in the step S30-step S36, the code length k, the number n of parent individuals, the number t of excellent individuals and the copy probability p_aCross probability of p_c. And the probability of variation p_mIs a control parameter of the accuracy of the genetic algorithm. Usually, k is 10-20, n is more than or equal to 300, t is more than or equal to 10, and p is_a＝0.1～0.2， p_c＝0.5～0.8，p_m＝0.01～0.1。

Further, the fitting formula in step S6 is: g ═ p₁+p₂V_R

g＝p₁+p₂ logV_R

Wherein g is a parameter to be solved, such as compactness, void ratio, dry density and soil strength physical property parameter; p is a radical of₁、p₂To be a coefficient of undetermination, V_RThe corresponding rayleigh wave velocity.

Further, a relation model between the backfill compactness, the void ratio, the dry density, the soil strength physical property parameters and the Rayleigh wave velocity determines a best fitting formula by searching a global optimal solution through a genetic algorithm; the dry density and wave velocity of various backfill materials are correlated by the following formula: sandy soil rho_d＝1.320exp(1.973E-03V_R) (ii) a Gravel soil rho_d＝1.113exp(2.412E-03V_R) (ii) a Giant grained soil rho_d＝-0.5689+1.358logV_R。

Further, in step S1, the gravel material has an optimal coarse material content during compaction by the crushing machine, and when the coarse material content in the filler reaches this value, the corresponding maximum dry density is the largest, and the optimal water content is the smallest.

The invention has the beneficial effects that: the method deeply analyzes the influence factors of transient Rayleigh wave detection on the basis of a Rayleigh wave basic principle and a surface wave spectrum analysis method, combines a conventional test detection method, adopts a genetic algorithm to determine the best fitting mode of Rayleigh wave speed and physical parameters of backfill, researches the relation between the properties of the backfill and the propagation characteristics of Rayleigh waves through tests, adopts a finite element method to analyze the influence of boundary conditions on the wave propagation characteristics, and simultaneously analyzes the propagation characteristics of Rayleigh waves in a typical tape culvert back; the method provides a theoretical basis for formulation of the backfill quality detection regulation of the Rayleigh wave method, and provides support for popularization and application of Rayleigh waves in backfill quality detection.

Drawings

FIG. 1 is a flow chart of the operation of the present invention.

FIG. 2 is a flow chart of a genetic algorithm.

FIG. 3 is a plot of coarse particle content versus maximum dry density.

FIG. 4 is a graph of coarse particle content versus optimum water content.

FIG. 5 shows the standard penetration value and transverse wave velocity of various rock-soil masses.

FIG. 6 is a schematic diagram of the operation of the surface wave spectrum analysis method.

Fig. 7 is a layered ground plane wave record.

FIG. 8 is a graph showing the relationship between the physical parameters of the backfill and the number of passes of rolling.

FIG. 9 is a quality criterion of the backfill.

Figure 10 is a diagram of the basic properties of the soil mass.

FIG. 11 is a graph of clayey sand test relationships.

Detailed Description

The invention is further described below with reference to the accompanying drawings.

Referring to fig. 1 to 11, the present invention provides an embodiment: a method for detecting the filling quality of a deep layer of a roadbed by a surface wave method comprises the following steps:

s1, selecting a test road section to carry out backfill paving detection, and measuring and determining technical parameters through a rolling machine after the test road section is paved; widely collecting data, investigating several most representative backfill materials, carrying out corresponding indoor tests to obtain lithology, grading conditions and engineering classification of various materials, obtaining the maximum dry density and the optimal water content of the materials by a standard compaction or vibration table method, and searching a test method suitable for coarse-grained soil and giant-grained soil to prepare for deeply researching the Rayleigh wave frequency dispersion characteristics of the materials; analyzing the influence of factors such as the weight of the hammer, the track spacing, the offset distance and the like on the detection result by combining the field reality, and testing a reasonable detection parameter setting method aiming at different soil bodies;

s2, arranging different wave speed test points at a middle distance in the test road section for testing the wave speed conditions of the backfill material under different compact states, and obtaining the compaction degree of the backfill material in the test road section by adopting a sand filling method or a water filling method at the positions of the wave speed test points; simultaneously, determining the settlement conditions under different rolling times through leveling measurement, and analyzing the settlement amplitude of the filler under the condition of incomplete rolling;

s4, analyzing and researching the relation between the unearthed depth, the water content of different fillers, the grain composition factor and the propagation characteristic of Rayleigh waves by analyzing the test data by adopting a genetic algorithm;

s5, calculating and analyzing the influence of the boundary condition on the wave propagation characteristic by adopting a finite element method based on a semi-infinite space through a Rayleigh wave theory; the finite element calculation result shows that the influence of the roadbed boundary on the test result is small; when the arrangement direction of the measuring lines is parallel to the wall back, and the wall back is upright, the influence of the wall back boundary on wave propagation can be not considered. If the wall back is inclined, the influence of the wall body below the measuring line needs to be considered in combination with the actual engineering, and the misjudgment on the result is prevented;

The irrigation method, the surface settlement observation, the sand-filling method and the finite element method calculation in the invention are all the prior art, and the skilled person in the art can clearly understand that the detailed description is not given here, and the rolling machine model in the invention can be HH-880, but is not limited to the HH-880.

In the step S3, the water holding capacity of the particles in the macro, coarse and fine particles of the backfill is different, and the water contents of the particles larger than 60mm and the particles smaller than 60mm are measured with 60mm as a boundary, respectively, and the overall water content is determined according to the following formula:

w＝w_fp_f+w_c(1-p_f)

in the formula, w is the integral water content, and the percentage is calculated to be 0.01; w is a_fThe water content of the granules is less than 60mm,%; w is a_cWater content of particles larger than 60mm,%; p is a radical of_fThe ratio of the dry mass of the granules smaller than 60mm to the dry mass of the whole material is determined, and the test ensures that the weight of the 60mm sample is larger than 2 kg. The more the content of coarse particles in the soil body is, the less the content of fine particles is, the poorer the water holding capacity of the soil body is, and the lower the natural water content is. The water content variation test is carried out on coarse-grained soil-clayey sand with more fine particle content, the water content variation range is 9% -18%, the basic properties of soil bodies are shown in figures 10 and 11, the dry density, the compaction degree and the Rayleigh wave velocity corresponding to the soil bodies with different water contents can be obtained from the table, the dry density and the compaction degree corresponding to different water contents are relatively close, but the Rayleigh wave velocity V is relatively close_RThe difference is large, and the Rayleigh wave velocity V of the soil body can be obtained_RDecreases with increasing water content and shows a linear relationship. All in oneRayleigh wave velocity V of sample soil body_RDecreases with increasing saturation in a linear relationship. The method shows that in the soil body with high content of fine particles (particles smaller than 0.075 mm), the fine particles have strong water adsorption capacity, so that the change range of the water content of the soil body is large, and the change of the water content causes the physical and mechanical properties of the soil body to be greatly changed, so that the water saturation of the soil body has a propagation speed V on Rayleigh surface waves_RThe value impact is large.

For the giant-grained soil and the gravel soil, under natural conditions, the change range of the water content of the soil body is small, the influence of the change of the water content on the physical and mechanical properties of the soil body is small, and the influence of the change of the water content on the Rayleigh wave velocity of the soil body is small.

With continuing reference to fig. 5, in an embodiment of the present invention, the propagation characteristics of the rayleigh wave in step S3 are that in the elastic medium with uniform isotropy below the free interface, the real parts of the horizontal component Dx and the vertical displacement component Dz of the rayleigh wave vibration can be expressed by the following expressions:

b is an energy-dependent constant; from the above equation, the particle displacement of the rayleigh surface wave is not only related to the frequency, propagation speed, depth, but also closely related to the properties of the medium. However, in transient rayleigh wave detection, its dispersion curve reflects the rayleigh wave velocity (V)_R) According to the relationship between the two, the method can be obtained by solving the wave equation, namely:

wherein μ is the Poisson's ratio; ρ is the density.

Propagation velocity V of Rayleigh surface waves_RVelocity V of sum transverse wave_SAnd longitudinal wave velocity V_PIn a relationship of

V_R≈0.92V_S＝0.53V_P

From the above formula, the Rayleigh wave velocity (V)_R) Rayleigh velocity (V) can be used close in value to shear velocity (Vs)_R) And dividing the rock-soil mass properties instead of the transverse wave velocity (Vs).

The elastic properties of rock-soil media are variable, and not only different types of rock-soil media, but also the same type of rock-soil media have different elastic properties due to different densities, weathering degrees and burial depths. From the above equation, Rayleigh wave V_RThe wave velocity is determined by the medium characteristics, and the mineral composition, structure, density and porosity in rock and soil mass determine the propagation velocity V of Rayleigh waves_RThe major factors of (c); due to the velocity V of the surface wave_RVelocity V of transverse wave_SThe surface wave technology method is characterized in that the Rayleigh surface wave speed of the stratum is detected, the transverse wave speed of the stratum medium is obtained through mathematical calculation, and the physical and mechanical parameters of the rock and soil medium are obtained through a calculation formula or a related parameter table.

The wave velocity test in step S2 is to convert the apparent velocity of rayleigh waves into the layer velocity of rayleigh waves and to give the velocity of shear waves for calculation, and the calculation formula of the conversion of the apparent velocity of rayleigh waves into the layer velocity is as follows: apparent velocity V_RAs the depth H increases and increases,

apparent velocity V_RAs the depth H decreases with an increase in depth,

in the formula, H_n: the depth of the nth dispersion point; h_n-1: depth of the nth-1 dispersion point; v_R，n: averaging Rayleigh wave velocity in a depth range above the nth frequency dispersion point; v_R，n-1: average Rayleigh wave velocity in the depth range above the nth-1 frequency dispersion point; v_R，m: h th_n－H_n-1The Rayleigh wave velocity of the rock-soil mass between depths is the Rayleigh wave layer velocity. Layering is performed according to the characteristics of the dispersion curve, and the characteristics are generally as follows: the curvature and the slope of the curve change, and the density of the dispersion points change. In order to ensure the accuracy of the layering speed and the depth, forward calculation can be used according to the parameters of the stratum structure to calculate a theoretical dispersion curve and compare the theoretical dispersion curve with an actually measured curve, the layering result is repeatedly modified to carry out forward fitting until the two dispersion curves are best fitted, and the layering speed is the final interpretation speed. Surface waves are groups of surface waves of different composition that propagate at their respective wavelengths, characterizing the average response over a range of depths. The shear wave layer velocity and layer thickness result obtained by surface wave dispersion data inversion is a reaction of comprehensive information of the stratum under the arrangement of the detectors, and for the near-horizontal layered stratum, the inversion result is regarded as the wave velocity distribution of the stratum in the vertical direction at the midpoint position of the arrangement of the detectors; for inclined strata, the inversion result is regarded as the wave velocity distribution from the middle point of the detector arrangement to the normal depth of the stratum interface.

It should be noted that please refer to fig. 6, in an embodiment of the present invention, the rayleigh wave detection technique in the present invention analyzes the formation structure by collecting the underground information carried by the artificial seismic waves, and there are two methods at present: one is a surface wave frequency conversion detection method based on a frequency domain, which is also called a steady state method; the other is time domain based surface wave spectroscopy (SASW), also known as transient. In comparison, the development history and application time of the steady state method are longerThe method is technically mature, but has the defects that excitation equipment is heavy and is not beneficial to improving efficiency, the transient Rayleigh wave detection reflects geological conditions at different depths through surface waves with different wavelengths, and the physical and mechanical properties of different stratums are described through the propagation speed of the transient Rayleigh wave. The Rayleigh surface wave method mainly comprises two aspects, namely exciting and collecting Rayleigh surface wave signals, and obtaining the corresponding speeds V of surface waves with various frequencies from collected data through processing_RAnd wavelength lambda_RAnd drawing a discrete distribution curve of the surface layer rock-soil layering, and further obtaining a geological explanation related to the surface layer rock-soil layering through inversion. The surface wave spectrum analysis method generally adopts a two-dimensional F-K domain analysis method, can effectively utilize a plurality of seismic channel data, and is more accurate in extraction of Rayleigh wave phase velocity than a one-dimensional cross-correlation method. The main idea of the height method is to perform two-dimensional analysis on Rayleigh wave records collected in the field and related to time and distance, extract a dispersion curve of the Rayleigh wave according to the maximum energy characteristic of the Rayleigh wave, and then further process the dispersion curve.

Referring to fig. 7, in an embodiment of the present invention, the operation design steps are as follows:

extracting rayleigh waves in the time domain:

is a complete surface wave vibration record obtained on a horizontal stratum and comprises basic wave patterns of surface waves and other interference waves received on the earth surface on a laminated medium. The waveforms are shown for different apparent velocities and periods. The source is on the left, and the arrival time of wavelets from left to right is getting later, wherein three wave types are marked as follows:

a: the optical system has high visual speed (smooth in-phase axis) and short visual period (high main frequency), and belongs to wave modes of shallow refraction waves and reflected waves.

C: the visual velocity is small (the in-phase axis is steep), the visual period is changed from short to long (the main frequency is low), and the visual period belongs to the wave mode of the surface wave fundamental mode.

B: the apparent velocity is higher than C (the in-phase axis is slower) and the apparent period is shorter than C (the dominant frequency is higher), which belongs to several higher-order modes of the surface wave.

Referring to fig. 2, in an embodiment of the present invention, the genetic algorithm in step S4 is a calculation model of a biological evolution process simulating natural selection and genetic mechanism of darwinian biological evolution theory, and is a method for searching an optimal solution by simulating a natural evolution process, and the genetic algorithm solving process includes the following steps:

step S41, generating the first individual, namely the initial population, from the step (2)^k)^pRandomly selecting n points from the grid points as initial parents; in this analysis, the initial population is represented by p in binary₁And p₂A randomly generated subset of the set of possible solutions;

step S46, taking the n sub-generation individuals obtained in the previous step as new parents, repeating the step S32-step S34, and generating a next generation → reevaluation → selection → crossing → variation until the criterion function Q is not changed or the minimum Q value in the new generation and the minimum Q value in the previous generation meet a certain precision requirement, and then the individual corresponding to the coding string with the minimum Q value of the function in the last generation is the optimal individual;

step S47, converting the binary string vector into decimal to obtain the optimal solution, in the step S30-step S36, the code length k, the number n of parent individuals, the number t of excellent individuals and the copy probability p_aCross probability of p_c. And the probability of variation p_mIs a control parameter of the accuracy of the genetic algorithm. Usually, k is 10-20, n is more than or equal to 300, t is more than or equal to 10, and p is_a＝0.1～0.2， p_c＝0.5～0.8，p_m0.01 to 0.1. Genetic algorithms begin with a population representing a potential solution set to the problem, and a population is composed of a certain number of individuals that are genetically encoded. Each individual is in fact a chromosome-bearing entity, a chromosome being the main carrier of genetic material, i.e. a collection of genes, whose internal expression (i.e. genotype) is a certain combination of genes that determines the external expression of the individual's shape, e.g. black hair, is determined by a certain combination of genes in the chromosome that controls this characteristic. Therefore, mapping from phenotype to genotype, i.e., coding work, needs to be accomplished at the outset. Because the work of emulating gene coding is complicated, binary coding is often used for simplification. After the initial generation population is generated, better and better approximate solutions are generated by generation-by-generation evolution according to the principle of survival and elimination of suitable persons. At each generation, individuals are selected according to the fitness of the individuals in the problem domain, and combined crossover and mutation are carried out by means of genetic operators of natural genetics, so that a population representing a new solution set is generated. The process leads the population of the next generation like natural evolution to be more suitable for the environment than the population of the previous generation, and the optimal individual in the population of the last generation can be used as the approximate optimal solution of the problem after decoding.

The fitting formula in step S6 is: g ═ p₁+p₂V_R

g＝p₁+p₂ logV_R

Wherein g is a parameter to be solved, such as compactness, void ratio, dry density and soil strength physical property parameter; p is a radical of₁、p₂To be a coefficient of undetermination, V_RThe corresponding rayleigh wave velocity. The relation model between the physical property parameters of the roadbed soil sample such as compaction degree, void ratio, dry density, soil body strength and the like and the wave speed of the transient Rayleigh wave is subjected to regression analysis by adopting 4 common curve mathematical models such as a linear function, a logarithmic function, an exponential function and a power function. In the analysis, the mean square error and the correlation coefficient corresponding to each fitting formula are compared through calculation, and the fitting formula with small mean square error and large correlation coefficient is preferably selected.

Determining a best fit formula by searching a global optimal solution by adopting a genetic algorithm through a relation model between the backfill compactness, the void ratio, the dry density, the soil strength physical property parameters and the Rayleigh wave velocity; the dry density and wave velocity of various backfill materials are correlated by the following formula: sandy soil rho_d＝1.320exp(1.973E-03V_R) (ii) a Gravel soil rho_d＝1.113exp(2.412E-03V_R) (ii) a Giant grained soil rho_d＝-0.5689+1.358logV_R. Referring to fig. 8 and 9, in an embodiment of the present invention, according to the specification, the thickness of the loose layer is controlled within 20cm, and the filling layer compactness reaches 96% as the final control index. And (3) carrying out rolling tests on the three typical backfill materials in the pairs in four platform culvert backs (1 each of sand soil and gravel soil platform culvert backs and 2 large-grain soil platform culvert backs), and according to test results, judging standards of the backfill quality of the platform culvert backs and suitable rolling processes of various backfill materials under the condition that the compaction degree reaches 96%.

In step S1, the gravel material has an optimal coarse material content during compaction by the crushing machine, and when the coarse material content in the filler reaches this value, the corresponding maximum dry density is the largest, and the optimal water content is the smallest.

It should be noted that, with continuing reference to fig. 3 and fig. 4, in an embodiment of the present invention, since the most important factors affecting the grinding performance of the sand gravel soil material are intrinsic factors such as the soil material properties and the particle size distribution, the coarse particle content is an important index reflecting the soil properties and the particle composition, and the content of the coarse particles (the particle size d is greater than or equal to 5mm) of the sand gravel material has an effect on the maximum dry density and the optimum water content. Carrying out compaction tests according to different coarse particle contents to obtain the relationship between the coarse particle contents and the maximum dry density and the optimal water content, analyzing to obtain that the optimal coarse particle contents exist in the compaction process of the gravel materials, and when the coarse particle contents in the filler reach the value, the corresponding maximum dry density is the largest and the optimal water content is the smallest; the following method was used for the compaction test of the coarse grain content:

the method I is a standard compaction method, the maximum dry density and the optimal water content are a group of very important indexes in roadbed filling, and the two parameters of fine soil can be obtained through a Pouler compaction test (standard compaction). In the road soil engineering test regulation (JTG E40-2007), compaction tests are the method commonly used in actual engineering, wherein the most common is heavy II, and the tests are applicable to soil with the particle size not larger than 40 mm. When the particles larger than 40mm in the soil body are less than 5%, after the particles larger than 40mm are picked up, standard compaction is carried out to determine the maximum dry density and the optimal water content of the soil body; and a second method and a formula correction method, wherein when the mass percentage of the particles larger than 40mm in the soil body is larger than 5% but smaller than 30%, the standard compaction result of the fine-grained soil can be corrected by adopting a formula. Firstly, taking out particles larger than 40mm in a sample, calculating the mass percentage p of the particles, and performing compaction test by using a part smaller than 40mm, wherein the method is suitable for a soil sample with the content of the particles larger than 40mm smaller than 30 percent, and a maximum dry density correction formula:

in the formula: rho'_dmax-corrected maximum dry density (g/cm)³)；ρ_dmaxMaximum dry density (g/cm) obtained by testing a soil sample having a particle size of less than 40mm³) (ii) a p-percentage (%) of particles having a particle size of more than 40mm in the sample; g'_s-bulk specific gravity of particles having a particle size of greater than 40 mm;

the optimum moisture content is corrected using the following formula:

w’_o＝w_o(1-0.01p)+0.01pw₁

in the formula: w'_o-corrected optimal moisture content (%); w is a_o-obtaining the optimum water content by testing a soil sample with a particle size of less than 40 mm; p-percentage (%) of particles having a particle size of more than 40mm in the sample; w is a₁-water absorption (%) of coarse particles having a particle size of more than 40 mm; when the total content of coarse particles in the fine soil is more than 40 percent or the content of particles with the particle diameter of more than 0.005mm is more than 70 percent of the total mass of the soil (namely d)₃₀Not more than 0.005mm), a coarse particle maximum dry density test is also carried out, the result is compared with the heavy compaction test result, the maximum dry density is the maximum value of the two test results, and the optimal water content is correspondingly taken.

The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims

1. A method for detecting the deep filling quality of a roadbed by a surface wave method is characterized by comprising the following steps:

2. The method for detecting the quality of the deep filling of the roadbed by the surface wave method according to claim 1, wherein the method comprises the following steps: in the step S3, the water holding capacity of the particles in the macro, coarse and fine particles of the backfill is different, and the water contents of the particles larger than 60mm and the particles smaller than 60mm are measured with 60mm as a boundary, respectively, and the overall water content is determined according to the following formula:

w＝w_fp_f+w_c(1-p_f)

in the formula, w is the integral water content, and the percentage is calculated to be 0.01; w is a_fThe water content of the granules is less than 60mm,%; w is a_cWater content of particles larger than 60mm,%; p is a radical of_fIs the ratio of the dry mass of the particles smaller than 60mm to the dry mass of the total material.

3. The method for detecting the quality of the deep filling of the roadbed by the surface wave method according to claim 1, wherein the method comprises the following steps: the rayleigh wave propagation characteristic in said step S3 is that, in an elastic medium of uniform isotropy below the free interface, real parts of the horizontal component Dx and the vertical displacement component Dz of the rayleigh wave vibration can be respectively represented by the following expressions:

4. The method for detecting the quality of the deep filling of the roadbed by the surface wave method according to claim 1, wherein the method comprises the following steps: the wave velocity test in step S2 is to convert the apparent velocity of rayleigh waves into the layer velocity of rayleigh waves and to give the velocity of shear waves for calculation, and the calculation formula of the conversion of the apparent velocity of rayleigh waves into the layer velocity is as follows: apparent velocity V_RAs the depth H increases and increases,

apparent velocity V_RAs the depth H decreases with an increase in depth,

in the formula, H_n: the depth of the nth dispersion point; h_n-1: depth of the nth-1 dispersion point; v_R，n: averaging Rayleigh wave velocity in a depth range above the nth frequency dispersion point; v_R，n-1: average Rayleigh wave velocity in the depth range above the nth-1 frequency dispersion point; v_R，m: h th_n－H_n-1The Rayleigh wave velocity of the rock-soil mass between depths is the Rayleigh wave layer velocity.

5. The method for detecting the quality of the deep filling of the roadbed by the surface wave method according to claim 1, wherein the method comprises the following steps: the genetic algorithm in step S4 is a calculation model of a biological evolution process that simulates natural selection and genetic mechanism of darwinian biological evolution theory, and is a method for searching an optimal solution by simulating a natural evolution process, and the genetic algorithm solving process includes the following steps:

step S40, model parameter coding, setting the coding length as k, equally dividing the variation interval of each parameter in the model into 2^k-1Sub-interval, then the model parameter variation space is discretized into (2)^k)^pA parameter grid point. Each grid point is regarded as an individual and represents one possible value state of p parameters of the model, p k-bit binary systems are used for representing, and the decimal number is converted into a binary string through coding;

step S47, converting the binary string vector into decimal to obtain the optimal solution, in the step S30-step S36, encoding length k, number of parent individuals n and number of excellent individualst, replication probability p_aCross probability of p_c. And the probability of variation p_mIs a control parameter of the accuracy of the genetic algorithm. Usually, k is 10-20, n is more than or equal to 300, t is more than or equal to 10, and p is_a＝0.1～0.2，p_c＝0.5～0.8，p_m＝0.01～0.1。

6. The method for detecting the quality of the deep filling of the roadbed by the surface wave method according to claim 1, wherein the method comprises the following steps: the fitting formula in step S6 is: g ═ p₁+p₂V_R

g＝p₁+p₂logV_R

7. The method for detecting the quality of the deep filling of the roadbed by the surface wave method according to claim 6, wherein the method comprises the following steps: determining a best fit formula by searching a global optimal solution by adopting a genetic algorithm through a relation model between the backfill compactness, the void ratio, the dry density, the soil strength physical property parameters and the Rayleigh wave velocity; the dry density and wave velocity of various backfill materials are correlated by the following formula: sandy soil rho_d＝1.320exp(1.973E-03V_R) (ii) a Gravel soil rho_d＝1.113exp(2.412E-03V_R) (ii) a Giant grained soil rho_d＝-0.5689+1.358logV_R。

8. The method for detecting the quality of the deep filling of the roadbed by the surface wave method according to claim 1, wherein the method comprises the following steps: in step S1, the gravel material has an optimal coarse material content during compaction by the crushing machine, and when the coarse material content in the filler reaches this value, the corresponding maximum dry density is the largest, and the optimal water content is the smallest.