CN110826807A - Method for rapidly predicting dynamic resilience modulus of roadbed filler in seasonal frozen region - Google Patents

Method for rapidly predicting dynamic resilience modulus of roadbed filler in seasonal frozen region Download PDF

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CN110826807A
CN110826807A CN201911086157.XA CN201911086157A CN110826807A CN 110826807 A CN110826807 A CN 110826807A CN 201911086157 A CN201911086157 A CN 201911086157A CN 110826807 A CN110826807 A CN 110826807A
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张军辉
张安顺
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Changsha University of Science and Technology
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Abstract

The invention discloses a method for quickly predicting dynamic resilience modulus of roadbed filling in a frozen season area, which specifically comprises the following steps: performing a freeze-thaw cycle test on the roadbed filler test piece, and then obtaining dynamic resilience modulus values of the roadbed filler test piece subjected to freeze-thaw cycles of different times under various working conditions through a dynamic triaxial test; establishing a roadbed filling dynamic resilience modulus estimation model comprehensively considering repeated freeze-thaw cycle action, state variables and stress variables; fitting according to the test data to obtain a pre-estimated model parameter and establishing a relation equation between the pre-estimated model parameter and the roadbed filling basic physical property parameter; and predicting the dynamic rebound modulus values of different roadbed fillers subjected to freeze-thaw cycles for different times under various working conditions through a prediction model. The method simultaneously considers the influence of repeated freezing and thawing cycle action, state variables and stress variables on the dynamic resilience modulus of the roadbed filler in the freezing and thawing area, improves the estimation precision, and greatly improves the estimation efficiency by using the moisture content to replace the suction force of the substrate to reflect the humidity state of the filler.

Description

Method for rapidly predicting dynamic resilience modulus of roadbed filler in seasonal frozen region
Technical Field
The invention belongs to the technical field of road engineering, and particularly relates to a method for quickly predicting dynamic resilience modulus of roadbed fillers in a frozen season area.
Background
As the third frozen soil big country in the world, the seasonal frozen soil area in China has the area of 514 ten thousand square kilometers and occupies about 53.5 percent of the total area of the national soil, wherein the area of the deep seasonal frozen soil area with the freezing depth of more than 1m reaches 367 ten thousand square kilometers and the frozen soil area is widely distributed. According to the planning goal of national highway network planning (2013-. A large number of engineering practices show that the complex heat-force effect formed by the repeated freezing and thawing circulation action in the freezing and thawing area and the vehicle load can cause the repeated change of the internal structure of the filler, so that the roadbed engineering performance is degraded, the service life of a road is shortened, and the driving safety is damaged. Nowadays, the rapid increase of traffic volume and the continuous increase of vehicle running speed have high requirement on the deformation resistance of the roadbed under the action of load, which makes the roadbed filling stiffness pay attention in the aspects of road design and the like.
As an important parameter for representing the rigidity of the filler, the dynamic resilience modulus reflects the nonlinear stress-strain characteristic of the roadbed filler under the action of traffic load, and is also an index which must be considered during the design of an asphalt pavement structure. Because the roadbed is not built during the design of the road, the accurate dynamic resilience modulus value of the roadbed filling is difficult to obtain. For this reason, the current "road bed design code" (JTG D30-2015) presents three methods for determining the dynamic modulus of resilience: the first method is a table look-up method, but the change range of the rebound modulus value of each given roadbed filler is large, so that the rebound modulus value of the used filler cannot be accurately determined through a table; the second method is to calculate the rebound modulus value of the filler according to an empirical formula of California Bearing Ratio (CBR) and the rebound modulus, and the method has poor applicability to different types of roadbed fillers; the third method is to estimate the resilience modulus of the roadbed filling by a dynamic triaxial test and further adopting a three-parameter NCHRP 1-28A model, and although the three-parameter NCHRP 1-28A model has the characteristics of few model parameters, wide application range and the like, the model cannot directly reflect the influence of the state variables, the stress variables and the physical parameters of the roadbed filling on the resilience modulus.
In the prior art 1, Remingxuan of Changan university provides a method for determining the resilience modulus of roadbed soil in a seasonal freezing region in Master thesis 'experiment research on the resilience modulus of roadbed in seasonal freezing region', and the method adopts a lever pressure instrument to measure the static resilience modulus of a filler and establishes a regression equation between the freeze-thaw reduction coefficient of the static resilience modulus of the roadbed soil and the actual moisture content of the roadbed soil. However, the static rebound modulus of the roadbed soil researched by the prior art 1 cannot embody the action of the dynamic loading of the running vehicle on the roadbed filler like the dynamic rebound modulus, meanwhile, the proposed freeze-thaw reduction coefficient is only established under the condition that the roadbed compactness is 96% and is not directly embodied in a pre-estimation model, so that the prediction of the static rebound modulus of the roadbed filler under different compactabilities is limited, and in addition, the static rebound modulus value which is selected by looking up the recommended value in the road surface design specification according to the natural division and the proposed average consistency of the roadbed soil and is not subjected to the freeze-thaw cycle action cannot be accurately matched with different types of roadbed fillers.
In the prior art 2, (invention patent, publication No. CN109142118A) discloses a roadbed soil dynamic resilience modulus estimation method based on state variables and stress variables, which comprehensively considers the state variables and the stress variables and obtains a roadbed filler dynamic resilience modulus estimation model of model parameters through filler basic physical property indexes, but does not consider the influence of repeated freeze-thaw cycling action in a freezing zone on a roadbed filler dynamic resilience modulus, so that the roadbed filler dynamic resilience modulus value predicted during pavement design is higher, that is, the supporting action of a roadbed on a pavement structure is highly estimated, a pavement structure layer is thinner, and the service level of a road is reduced. If the estimation method is considered under the condition of the worst season, the design of the pavement structure is more conservative, the pavement structure layer is thicker, and the fund waste is caused. In addition, although the matrix suction accurately reflects the humidity state of the filler from the viewpoint of unsaturated soil mechanics, the determination process of the matrix suction is complex and time-consuming, needs special equipment, is not easily accepted by a front-line construction and designer and applied to engineering practice, and meanwhile, the correlation between the matrix suction and the compactness limits the use of the estimation model, so that the dynamic resilience modulus of the roadbed filler in the seasonal frozen region is accurately and quickly predicted.
Disclosure of Invention
The invention aims to provide a method for quickly predicting the dynamic resilience modulus of roadbed fillers in a freezing zone, which considers the influence of repeated freezing and thawing cycle action, state variables and stress variables on the dynamic resilience modulus of roadbed fillers in the freezing zone, improves the prediction precision, replaces the substrate suction with the water content to reflect the humidity state of the fillers, greatly improves the prediction efficiency, and solves the problems that the conventional prediction method has limited consideration on the repeated freezing and thawing cycle action and the prediction process is complex and time-consuming.
The technical scheme adopted by the invention is that the method for rapidly predicting the dynamic resilience modulus of the roadbed filler in the frozen season area specifically comprises the following steps:
s1: performing a freeze-thaw cycle test on the roadbed filler test piece, and then obtaining dynamic resilience modulus values of the roadbed filler test piece subjected to freeze-thaw cycles of different times under various working conditions through a dynamic triaxial test;
s2: establishing a roadbed filling dynamic resilience modulus estimation model comprehensively considering repeated freeze-thaw cycle action, state variables and stress variables:
Figure BDA0002265474830000021
wherein: mRThe dynamic resilience modulus of the roadbed filling material is set; n is the number of freeze-thaw cycles; c is the degree of compaction; w is the actual water content of the filler, womcThe optimum water content of the filler is obtained; thetamTo minimize body stress, θm＝θ-σdTheta is the bulk stress, sigmadIs the bias stress; theta ═ sigma1+σ2+σ3，σ1Is the principal vertical stress, σ2Is the median principal stress, σ3For confining pressure, sigma in dynamic triaxial test2＝σ3；τoctIs the shear stress of an octahedron,
Figure BDA0002265474830000031
Pa101.3kPa, atmospheric pressure; k is a radical of1To correct the coefficient, k2Reflecting the influence of the number of freeze-thaw cycles, k3Reflecting the influence of compactness, k4Reflecting the influence of the actual water cut, k5Reflecting the influence of the minimum body stress, k6Reflecting the influence of the octahedral shear stress;
s3: experimental data according to step S1Fitting to obtain a pre-estimated model parameter k1、k2、k3、k4、k5、k6And establishing a pre-estimated model parameter k1、k2、k3、k4、k5、k6A relation equation between the basic physical property parameters of the roadbed filling;
s4: obtaining the estimated model parameter k of the given roadbed filling by the relational equation established in the step S31、k2、k3、k4、k5、k6Therefore, the dynamic rebound modulus values of different roadbed fillers subjected to different times of freeze-thaw cycles under various working conditions are predicted through the prediction model established in the step S2.
Further, the step S1 is specifically: according to the maximum dry density and the optimal water content value determined by the compaction test result, roadbed filler test pieces are prepared by the selected roadbed fillers according to the target compaction degrees of 93% and 96% and the target water contents of OMC-2% and OMC + 2%, the OMC is the optimal water content obtained by the compaction test, a split film is adopted for static pressure forming under a universal hydraulic testing machine, the load form of repeated loading dynamic triaxial test after the freeze-thaw cycle test is half sine wave, the frequency is 1Hz, the loading time is 0.2s, the pause time is 0.8s, and after the stress level of each loading sequence is finished, the test result of the last 5 times of loading cycle is selected to calculate the dynamic rebound modulus value.
Further, in step S1, the freeze-thaw cycle test: and investigating the negative temperature extreme value of the area of the actual engineering of the roadbed filler to be estimated in nearly ten years, wherein the freezing negative temperature of the test is smaller than the negative temperature extreme value so as to include a possible negative temperature change range, the absolute values of the melting positive temperature and the freezing negative temperature of the test are equal, the freezing time and the melting time are both set to be 12h, and the times of the freezing-thawing cycle are set to be 0 time, 1 time, 3 times, 6 times and 10 times according to the rule that the backward difference increases progressively.
Further, in the step S3, three basic physical parameters of the roadbed filling material are: liquid limit wLPlastic limit wPAnd plasticity index PI, establishing a derived variable H, i.e.Fitting according to the test data of the step S1 to obtain a pre-estimated model parameter k1、k2、k3、k4、k5、k6And establishing a derivative variable H and a pre-estimated model parameter k1、k2、k3、k4、k5、k6The relation equation between them.
Further, in the step S3, the derivative variable H and the pre-estimated model parameter k are established by the method of successive polynomial regression using EXCEL software according to the test data of the step S11、k2、k3、k4、k5、k6The relation equation between the following formulas:
wherein R is2Is the correlation coefficient.
Further, in step S2, the repeated freeze-thaw cycle acts on the determination of the representation form in the pre-estimation model:
providing a freeze-thaw damage factor D of the dynamic resilience modulus of the roadbed filling, which is shown as the following formula:
wherein D is a freeze-thaw damage factor; mR(0)Is a dynamic rebound modulus value not subjected to freeze-thaw cycling; mR(N)Is the dynamic resilience modulus value after N times of freeze-thaw cycles, and N is respectively 0, 1, 3, 6 and 10;
according to the development trend of the freeze-thaw damage factor D along with the number of freeze-thaw cycles, determining the embodiment form of the freeze-thaw cycles in the estimation model as
The invention has the beneficial effects that:
1. the invention makes the dynamic resilience modulus of the roadbed filling material follow the times of freeze-thaw cyclesLaw of decay
Figure BDA0002265474830000044
The form of the method is directly embodied in the estimation model, the estimation model of the dynamic resilience modulus of the roadbed filler comprehensively considering the repeated freeze-thaw cycle effect, the state variable and the stress variable is established, the influence of the repeated freeze-thaw cycle effect, the state variable and the stress variable on the dynamic resilience modulus of the roadbed filler in the seasonal freezing area is considered, the dynamic resilience modulus after freeze-thaw cycles of any times can be predicted, the limitation of compaction degree is avoided, and the estimation precision is improved.
2. The moisture content of the roadbed filler is reflected by selecting and measuring the simple and rapid actual moisture content to replace the matrix suction in the prior art, the minimum body stress and the octahedral shear stress which reflect rigidity hardening and rigidity softening of the roadbed filler are selected as stress variables, and all the variables and parameters of the model have definite meanings and are convenient and rapid to obtain.
3. Establishes a pre-estimated model parameter k1、k2、k3、k4、k5、k6The relation equation between the dynamic resilience modulus and the roadbed filler basic physical property parameters realizes that the dynamic resilience modulus can be rapidly predicted accurately only through a boundary water content test of the roadbed filler, provides obvious engineering convenience for units without dynamic triaxial and freeze-thaw cycle test conditions, and has high market popularization value.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an estimation method according to an embodiment of the invention.
FIG. 2 is a graph of a half-sinusoidal loading stress waveform in an embodiment of the present invention.
FIG. 3a shows the dynamic rebound modulus values of the roadbed filling material of the construction waste under different confining pressures when the freeze-thaw cycle is 0 times, the degree of compaction is 93%, and the water content is OMC-2%.
Fig. 3b is the dynamic rebound modulus values of the construction waste roadbed filling under different confining pressures when the freezing and thawing cycle is 0 times, the degree of compaction is 93%, and the water content is OMC.
Fig. 3c is the dynamic rebound modulus values of the construction waste roadbed filling under different confining pressures when the freeze-thaw cycle is 0 times, the degree of compaction is 93%, and the water content is OMC + 2%.
FIG. 3d shows the dynamic rebound modulus values of the roadbed filling material of the construction waste under different confining pressures when the freeze-thaw cycle is 0 times, the degree of compaction is 96%, and the water content is OMC-2%.
Fig. 3e is the dynamic rebound modulus values of the construction waste roadbed filling under different confining pressures when the freezing and thawing cycle is 0 times, the degree of compaction is 96%, and the water content is OMC.
Fig. 3f is the dynamic rebound modulus values of the construction waste roadbed filling under different confining pressures when the freeze-thaw cycle is 0 times, the degree of compaction is 96%, and the water content is OMC + 2%.
FIG. 4a is a graph showing the relationship between the number of freeze-thaw cycles and the "freeze-thaw damage factor" when the degree of compaction is 93% and the water content is OMC-2%.
FIG. 4b is a graph showing the relationship between the number of freeze-thaw cycles and the "freeze-thaw damage factor" when the compaction degree is 93% and the water content is OMC.
FIG. 4c is a graph showing the relationship between the number of freeze-thaw cycles and the "freeze-thaw damage factor" when the degree of compaction is 93% and the water content is OMC + 2%.
FIG. 4d is a graph showing the relationship between the number of freeze-thaw cycles and the "freeze-thaw damage factor" when the degree of compaction is 96% and the water content is OMC-2%.
FIG. 4e is the relationship between the number of different freeze-thaw cycles and the "freeze-thaw damage factor" for a compaction of 96% and a moisture content of OMC.
FIG. 4f is the relationship between the number of different freeze-thaw cycles and the "freeze-thaw damage factor" when the degree of compaction is 96% and the water content is OMC + 2%.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for quickly predicting dynamic resilience modulus of a roadbed filler in a frozen season area, which specifically comprises the following steps:
s1: investigating the negative temperature extreme value of the area of the researched actual project in nearly ten years, setting the freezing negative temperature of the test to be less than the negative temperature extreme value so as to include the possible negative temperature change range, setting the positive and negative temperatures with equal absolute values, setting the freezing time and the melting time to be 12h, setting the number of freeze-thaw cycles to be 0, 1, 3, 6 and 10 according to the rule of increasing the backward difference value, and facilitating the definition of the test rule; performing a freeze-thaw cycle test on the roadbed filler test piece; then obtaining dynamic resilience modulus values of the roadbed filling test piece subjected to different times of freeze-thaw cycles under various working conditions through a dynamic triaxial test; according to the maximum dry density and the optimal water content value determined by the compaction test result, roadbed filler test pieces are prepared by the selected roadbed fillers according to the target compaction degrees of 93% and 96% and the target water contents of OMC-2% and OMC + 2%, the OMC is the optimal water content obtained by the compaction test, a split film is adopted for static pressure forming under a universal hydraulic testing machine, the load form of repeated loading dynamic triaxial test after the freeze-thaw cycle test is half sine wave, the frequency is 1Hz, the loading time is 0.2s, the pause time is 0.8s, and after the stress level of each loading sequence is finished, the test result of the last 5 times of loading cycle is selected to calculate the dynamic rebound modulus value.
S2: in order to visually reflect the influence of repeated freeze-thaw cycling on the dynamic resilience modulus, from the viewpoint of macroscopic damage, the self-defined dynamic resilience modulus 'freeze-thaw damage factor' subjected to freeze-thaw cycling for different times under various working conditions is determined according to the formula (1):
Figure BDA0002265474830000061
wherein: d is freeze-thaw damageA factor; mR(0)Is a dynamic rebound modulus value not subjected to freeze-thaw cycling; mR(N)Is the dynamic rebound modulus value after N times of freeze-thaw cycles, and N is 0, 1, 3, 6 and 10 respectively.
According to the development trend of the freeze-thaw damage factor D along with the number of freeze-thaw cycles, determining the embodiment form of the freeze-thaw cycles in the estimation model as
Figure BDA0002265474830000062
Selecting the compactness and the actual water content as state variables of the estimation model, selecting the minimum body stress and the octahedral shear stress as stress variables of the estimation model, and establishing a roadbed filler dynamic resilience modulus estimation model comprehensively considering repeated freeze-thaw cycle action, the state variables and the stress variables, wherein the formula (2) is as follows:
wherein: mRThe dynamic resilience modulus of the roadbed filling material is set; n is the number of freeze-thaw cycles; c is the degree of compaction; w is the actual water content of the filler, womcThe optimum water content of the filler is obtained; thetamTo minimize body stress, θm＝θ-σdTheta is the bulk stress, sigmadIs the bias stress; theta ═ sigma1+σ2+σ3，σ1Is the principal vertical stress, σ2Is the median principal stress, σ3For confining pressure, sigma in dynamic triaxial test2＝σ3；τoctIs the shear stress of an octahedron,
Figure BDA0002265474830000072
Pa101.3kPa, atmospheric pressure; k is a radical of1To correct the coefficient, k2Reflecting the influence of the number of freeze-thaw cycles, k3Reflecting the influence of compactness, k4Reflecting the influence of the actual water cut, k5Reflecting the influence of the minimum body stress, k6Reflecting the influence of the octahedral shear stress;
s3: fitting according to the test data of the step S1 to obtain a pre-estimated model parameter k1、k2、k3、k4、k5、k6Three basic physical parameters of the roadbed filling material are as follows: liquid limit wLPlastic limit wPAnd plasticity index PI, establishing a derived variable H, i.e.
Figure BDA0002265474830000073
And establishing a derivative variable H and a pre-estimated model parameter k1、k2、k3、k4、k5、k6The relation equation between the two is shown in formula (3):
Figure BDA0002265474830000074
wherein R is2Is the correlation coefficient.
S4: obtaining the estimated model parameter k of the given roadbed filling by the relational equation established in the step S31、k2、k3、k4、k5、k6Therefore, the dynamic rebound modulus values of different roadbed fillers subjected to different times of freeze-thaw cycles under various working conditions are predicted through the prediction model established in the step S2.
In the embodiment of the method, the first step,
the invention discloses a method for quickly predicting dynamic resilience modulus of a roadbed filler in a freezing region, which is specifically carried out according to the following steps as shown in figure 1:
the roadbed filler test piece is manufactured by filling the construction waste filler of the highway roadbed test section with the construction waste from the Beijing capital loop highway (Daxing-Tongzhou section), and the basic physical parameters are shown in the table 1;
TABLE 1 basic physical parameters of roadbed filler made of construction wastes
S1: by investigating climate data, the extreme value of the negative temperature in the Beijing area in nearly ten years is-18.7 ℃, the freezing negative temperature in the high-low temperature alternating test machine is set to be-20 ℃ according to the extreme value, the possible negative temperature change range is included, the positive temperature during melting is set to be 20 ℃ according to the principle of equal absolute values, so as to ensure that the test piece is completely melted, the freezing time and the melting time are both set to be 12 hours, the times of freezing and melting cycle are set to be 0 time, 1 time, 3 times, 6 times and 10 times according to the rule of increasing the backward difference value, and the test rule is convenient to be clear;
preparing test pieces by using the selected roadbed filling materials according to the target compaction degree of 93 percent and 96 percent and the target moisture content of OMC-2 percent and OMC +2 percent respectively according to the maximum dry density and the optimal moisture content value determined by the compaction test result, wherein the OMC is the optimal moisture content obtained by the compaction test, the size of each test piece is 15cm multiplied by 30cm, a self-made split mold is adopted for carrying out static pressure forming under a universal hydraulic tester, the load form of a repeated loading dynamic triaxial test after freeze-thaw circulation is a half sine wave, the frequency is 1Hz, the loading time is 0.2s, the pause time is 0.8s, after the stress level of each loading sequence is finished, the test result of the last 5 loading cycles is selected to calculate the dynamic rebound modulus value, and the loading sequence of the dynamic triaxial test is shown in table 2; the test results show that the dynamic rebound modulus of the construction waste subjected to different times of freeze-thaw cycles has approximately the same variation trend with bias stress, confining pressure, compaction degree and actual water content, is limited to space, and only the dynamic rebound modulus test results of 0 times of freeze-thaw cycles are given, as shown in fig. 3a-3f, and the influence of the freeze-thaw cycles on the dynamic rebound modulus will be separately described in detail in fig. 4a-4 f.
TABLE 2 dynamic resilience modulus dynamic triaxial test loading sequence
Figure BDA0002265474830000091
S2: in order to visually reflect the influence of repeated freeze-thaw cycling on the dynamic resilience modulus, from the viewpoint of macroscopic damage, the self-defined dynamic resilience modulus 'freeze-thaw damage factor' subjected to freeze-thaw cycling for different times under various working conditions is determined according to the formula (1):
Figure BDA0002265474830000092
wherein: d is a freeze-thaw damage factor; mR(0)Is a dynamic rebound modulus value not subjected to freeze-thaw cycling; mR(N)Is the dynamic rebound modulus value after N times of freeze-thaw cycles, and N is 0, 1, 3, 6 and 10 respectively.
The calculation results are shown in fig. 4a to 4f, and as can be seen from fig. 4a to 4f, the development trend of the "freeze-thaw damage factor" along with the number of freeze-thaw cycles is close to the logarithmic function, and based on this, the expression form of the number of freeze-thaw cycles in the dynamic elastic modulus estimation model is considered from the following aspects: if the basic logarithmic form lnN is used directly, the dynamic resilience modulus value will approach infinity to negative infinity when N is 0, i.e. no freeze-thaw cycles are experienced, which is clearly not in accordance with the practical situation. To solve this ambiguity, the basic logarithmic form lnN is modified to obtain ln (e + N). The reason why the common logarithm e is selectively added is that if the added constant is less than 1, positive and negative opposite signs of ln (e + N) corresponding to different freezing-thawing cycle times may appear for the freezing- thawing cycle times 0, 1, 3, 6 and 10 selected in the research, and at the moment, the model coefficient k can only be passed through1The sign is adjusted to influence the fitting precision, the common logarithm e is added, the ln (e + N) corresponding to each freezing-thawing cycle number is constant, and the phenomenon of different signs is avoided. Further, the use of ln (e + N) alone is not consistent with the actual situation, since ln (e + N) increases monotonically with N and the number of freeze-thaw cycles N has a damping effect on the dynamic modulus of resilience, therefore, the addition of "times shell k" is used2"the effect of ln (e + N) on the dynamic rebound modulus is adjusted in a manner that the embodiment of the number of freeze-thaw cycles is finally determined as
Figure BDA0002265474830000101
Number of times k2Meanwhile, the dynamic modulus after freeze-thaw cycles of any number of times can be predicted as an adjustment coefficient, the dynamic modulus has no correlation with other variables such as compactness and the like, is not limited by the compactness, can be used under any compactness, and reasonably reflects the effect of the repeated freeze-thaw cycles on the dynamic rebound modulus and simultaneously improves the prediction precision of the model.
Selecting the degree of compaction according to the development trend of freeze-thaw damage factors along with the number of freeze-thaw cycles and the change rule of the dynamic resilience modulus along with various influence factorsThe actual water content is used as the state variable of the pre-estimated model, the minimum body stress and the octahedral shear stress are selected as the stress variable of the pre-estimated model, and
Figure BDA0002265474830000102
the influence of the freeze-thaw cycle effect on the dynamic resilience modulus is reflected, a roadbed filler dynamic resilience modulus estimation model comprehensively considering the repeated freeze-thaw cycle effect, the state variable and the stress variable in the frozen region is established, and the formula (2) is as follows:
Figure BDA0002265474830000103
wherein: mRThe dynamic resilience modulus of the roadbed filling material is set; n is the number of freeze-thaw cycles; c is the degree of compaction; w is the actual water content of the filler, womcThe optimum water content of the filler is obtained; thetamTo minimize body stress, θm＝θ-σdTheta is the bulk stress, sigmadIs the bias stress; theta ═ sigma1+σ2+σ3，σ1Is the principal vertical stress, σ2Is the median principal stress, σ3For confining pressure, sigma in dynamic triaxial test2＝σ3；τoctIs the shear stress of an octahedron,
Figure BDA0002265474830000104
Pa101.3kPa, atmospheric pressure; k is a radical of1To correct the coefficient, k2Reflecting the influence of the number of freeze-thaw cycles, k3Reflecting the influence of compactness, k4Reflecting the influence of the actual water cut, k5Reflecting the influence of the minimum body stress, k6Reflecting the effect of octahedral shear stress.
S3: fitting the test data in the step S1 to obtain a pre-estimated model parameter k1、k2、k3、k4、k5、k6As shown in Table 3, the prediction accuracy of the prediction model is high as the correlation coefficient of the model is as high as 0.92.
TABLE 3 statistics of pre-estimated model parameters
k1 k2 k3 k4 k5 k6 R2
0.794 -0.334 3.857 -0.673 0.439 1.021 0.92
The pre-estimation model established by the invention is verified by adopting test data of Wu Xiao Wen, Zaman and the like, Ren and the like, the verification result is shown in Table 4, the pre-estimation model is well coupled with the test data, the correlation coefficient is higher, and the pre-estimation model established by the invention is proved to be reasonable. The verified roadbed fillers comprise low liquid limit clay which is taken from Ottawa in Heilongjiang and Canada and improved limestone of Ojolama in America, the coverage of the filler range is wide, and the estimation model established by the invention can be popularized and applied to estimation of dynamic resilience modulus of other roadbed fillers and the same roadbed filler under other working conditions and has higher estimation precision.
TABLE 4 verification results of the prediction model of the present invention
Subsequently, three basic physical parameters of the four roadbed fillers in the embodiment are selected: liquid limit wLPlastic limit wPAnd plasticity index PI, respectively establishing derivative variables
Figure BDA0002265474830000112
Wherein: h is a derivative variable; w is aLIs the liquid limit of the filler; w is apIs the plastic limit of the filler; and PI is a plastic limit index.
Consider that if three basic physical property parameters: liquid limit wLPlastic limit wPAnd the plasticity index PI and the pre-estimated model parameter are regressed simultaneously, the regression process is complex, so that three basic physical property parameters are unified into one variable through a derivative variable H, and the three basic physical property parameters and the pre-estimated model parameter k are unified into one variable1、k2、k3、k4、k5、k6And establishing contact. Furthermore, the model parameter k1、k2、k3、k4、k5、k6Only numerical values, no units, from the viewpoint of dimensional uniformity to improve the correlation, are adopted
Figure BDA0002265474830000113
The form of the method is beneficial to improving the estimation precision and is more reasonable.
Establishing a derivative variable H and a pre-estimated model parameter k by adopting EXCEL software through a polynomial successive regression method1、k2、k3、k4、k5、k6The relation equation between the two is shown as a formula (3);
Figure BDA0002265474830000114
s4: tong (Chinese character of 'tong')Determining estimated model parameter k of given roadbed filling material by equation (3)1、k2、k3、k4、k5、k6Therefore, the dynamic resilience modulus value of different roadbed fillers subjected to different times of freeze-thaw cycles under various working conditions is predicted through the roadbed filler dynamic resilience modulus estimation model established in the step S2; according to the specification of the highway subgrade design (JTG D30-2015) at page 144 in China: the equivalent stress level in the roadbed with different traffic load grades changes slightly, and the equivalent stress can be used according to 70kPa of body stress and 13kPa of octahedral shear stress when the rebound modulus is predicted, at the moment, the minimum body stress value can be determined to be 42.5kPa by the definition of the minimum body stress, and the dynamic rebound modulus can be rapidly predicted only through basic physical parameters of the filler after model parameters are obtained.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (6)

1. A method for rapidly predicting dynamic resilience modulus of a roadbed filler in a frozen season area is characterized by comprising the following steps:
s1: performing a freeze-thaw cycle test on the roadbed filler test piece, and then obtaining dynamic resilience modulus values of the roadbed filler test piece subjected to freeze-thaw cycles of different times under various working conditions through a dynamic triaxial test;
s2: establishing a roadbed filling dynamic resilience modulus estimation model comprehensively considering repeated freeze-thaw cycle action, state variables and stress variables:
Figure FDA0002265474820000011
wherein: mRThe dynamic resilience modulus of the roadbed filling material is set; n is the number of freeze-thaw cycles; c is the degree of compaction; w is the actual water content of the filler, womcThe optimum water content of the filler is obtained; thetamTo minimize body stress, θm＝θ-σdTheta is the bulk stress, sigmadIs the bias stress; theta ═ sigma1+σ2+σ3，σ1Is the principal vertical stress, σ2Is the median principal stress, σ3For confining pressure, sigma in dynamic triaxial test2＝σ3；τoctIs the shear stress of an octahedron,
Figure FDA0002265474820000012
Pa101.3kPa, atmospheric pressure; k is a radical of1To correct the coefficient, k2Reflecting the influence of the number of freeze-thaw cycles, k3Reflecting the influence of compactness, k4Reflecting the influence of the actual water cut, k5Reflecting the influence of the minimum body stress, k6Reflecting the influence of the octahedral shear stress;
s3: fitting according to the test data of the step S1 to obtain a pre-estimated model parameter k1、k2、k3、k4、k5、k6And establishing a pre-estimated model parameter k1、k2、k3、k4、k5、k6A relation equation between the basic physical property parameters of the roadbed filling;
s4: obtaining the estimated model parameter k of the given roadbed filling by the relational equation established in the step S31、k2、k3、k4、k5、k6Therefore, the dynamic rebound modulus values of different roadbed fillers subjected to different times of freeze-thaw cycles under various working conditions are predicted through the prediction model established in the step S2.
2. The method for rapidly predicting the dynamic resilience modulus of the roadbed filler in the frozen region as claimed in claim 1, wherein the step S1 is specifically as follows: according to the maximum dry density and the optimal water content value determined by the compaction test result, roadbed filler test pieces are prepared by the selected roadbed fillers according to the target compaction degrees of 93% and 96% and the target water contents of OMC-2% and OMC + 2%, the OMC is the optimal water content obtained by the compaction test, a split film is adopted for static pressure forming under a universal hydraulic testing machine, the load form of repeated loading dynamic triaxial test after the freeze-thaw cycle test is half sine wave, the frequency is 1Hz, the loading time is 0.2s, the pause time is 0.8s, and after the stress level of each loading sequence is finished, the test result of the last 5 times of loading cycle is selected to calculate the dynamic rebound modulus value.
3. The method for rapidly predicting the dynamic resilience modulus of the roadbed filler in the frozen region as claimed in claim 2, wherein in the step S1, the freeze-thaw cycle test: and investigating the negative temperature extreme value of the area of the actual engineering of the roadbed filler to be estimated in nearly ten years, wherein the freezing negative temperature of the test is smaller than the negative temperature extreme value so as to include a possible negative temperature change range, the absolute values of the melting positive temperature and the freezing negative temperature of the test are equal, the freezing time and the melting time are both set to be 12h, and the times of the freezing-thawing cycle are set to be 0 time, 1 time, 3 times, 6 times and 10 times according to the rule that the backward difference increases progressively.
4. The method for rapidly predicting the dynamic rebound modulus of the roadbed filler in the frozen region is according to the claim 1, the step S3 is characterized in that the dynamic rebound modulus of the roadbed filler is predicted according to three basic physical parameters of the roadbed filler: liquid limit wLPlastic limit wPAnd plasticity index PI, establishing a derived variable H, i.e.
Figure FDA0002265474820000021
Fitting according to the test data of the step S1 to obtain a pre-estimated model parameter k1、k2、k3、k4、k5、k6And establishing a derivative variable H and a pre-estimated model parameter k1、k2、k3、k4、k5、k6Is close toIs a system equation.
5. The method for rapidly predicting the dynamic rebound modulus of the road-based filler in the frozen region in the season as claimed in claim 4, wherein in the step S3, the EXCEL software is adopted to establish the derived variable H and the estimated model parameter k through a polynomial successive regression method according to the test data of the step S11、k2、k3、k4、k5、k6The relation equation between the following formulas:
Figure FDA0002265474820000022
wherein R is2Is the correlation coefficient.
6. The method for rapidly predicting the dynamic resilience modulus of the roadbed filler in the frozen region as claimed in claim 1, wherein in the step S2, the repeated freezing and thawing cycle is used for determining the appearance form in the prediction model:
providing a freeze-thaw damage factor D of the dynamic resilience modulus of the roadbed filling, which is shown as the following formula:
Figure FDA0002265474820000023
wherein D is a freeze-thaw damage factor; mR(0)Is a dynamic rebound modulus value not subjected to freeze-thaw cycling; mR(N)Is the dynamic resilience modulus value after N times of freeze-thaw cycles, and N is respectively 0, 1, 3, 6 and 10;
according to the development trend of the freeze-thaw damage factor D along with the number of freeze-thaw cycles, determining the embodiment form of the freeze-thaw cycles in the estimation model as
Figure FDA0002265474820000024
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