CN110186789B - Construction waste roadbed permanent deformation orthogonal estimation method based on grey system - Google Patents

Construction waste roadbed permanent deformation orthogonal estimation method based on grey system Download PDF

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CN110186789B
CN110186789B CN201910534325.0A CN201910534325A CN110186789B CN 110186789 B CN110186789 B CN 110186789B CN 201910534325 A CN201910534325 A CN 201910534325A CN 110186789 B CN110186789 B CN 110186789B
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张军辉
张安顺
彭俊辉
李崛
黄超
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Changsha University of Science and Technology
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Abstract

The invention discloses a construction waste roadbed permanent deformation orthogonal estimation method based on a grey system, which comprises the following specific steps: determining influence factors and research level of permanent deformation of the roadbed; designing a four-factor three-level orthogonal test; manufacturing a test piece by using the construction waste roadbed filling, and carrying out a dynamic triaxial test to obtain permanent deformation values of the test piece simulating different working conditions; calculating the association degree between the permanent deformation value of each test piece and each influence factor by using a grey theory, and sequencing the influence degree of each factor according to the association degree value; and constructing a roadbed permanent deformation estimation model according to the gray correlation calculation result of the influence factors, and fitting to obtain model parameters, thereby estimating the permanent deformation value of the same construction waste roadbed under other working conditions. The method has the advantages of effectively reducing the test amount, having high efficiency, reducing the possibility of test errors and accurately estimating the permanent deformation of the construction waste roadbed.

Description

Construction waste roadbed permanent deformation orthogonal estimation method based on grey system
Technical Field
The invention belongs to the technical field of road engineering, and relates to a construction waste roadbed permanent deformation orthogonal estimation method based on a gray system.
Background
With the increasing development speed of urbanization in China, the construction waste is generated rapidly due to the activities of infrastructure construction, transformation of new and old urban areas and the like. According to statistics, the total amount of construction waste generated in China every year is 2.4-3.6 million tons, which accounts for about 30-40% of the total amount of municipal waste, but the regeneration utilization rate is only 5%. A large amount of building wastes are piled or buried in the open air, which damages the ecological environment and harms the health of people. Highway subgrade average height of 3.4m in China and bidirectional four-lane high speedThe earth consumption of highway is about 45000m per kilometer3In national road network planning (2013 and 2030), the roadbed to be built needs a large amount of filler when being filled. The building waste is crushed, screened and impurity-removed to form the filler for roadbed filling, so that a large amount of building waste in urban construction can be effectively consumed, and the problems of environmental pollution, large amount of earthwork consumed by roadbed filling and the like caused by mining and quarrying in road construction can be solved, thereby saving construction cost, reducing energy consumption and realizing energy conservation and emission reduction.
The rapid increase of traffic volume puts higher requirements on the service performance of the roadbed, and the overlarge permanent deformation is likely to cause the diseases such as pavement cracking and the like. Obviously, the estimation of the permanent deformation of the roadbed filled with the novel filler, namely the construction waste, has important guiding significance on road design. At present, most scholars make relevant researches on natural granular fillers such as soil, gravel and the like, but the researches only stay in an indoor dynamic triaxial test to determine the permanent deformation of the fillers, so that the required test quantity is large, the time consumption is high, and a large prediction error is easy to generate. Furthermore, the construction waste fillers are greatly different from natural fillers: the natural filler is easy to obtain, and the building waste filler needs to be subjected to the working procedures of crushing, screening, impurity removal and the like, so that the time and the labor are consumed; the maximum grain size of the building waste filler exceeds 19mm, a test piece with the diameter of 150mm and the height of 300mm is required to be manufactured according to the requirements of a triaxial test, and the requirements on test equipment are high; the existing indoor dynamic triaxial test has the problems of long period, high difficulty, high cost and poor accuracy of a determination result due to the fact that a large amount of building waste filler is needed because of the large number of times of tests for determining the permanent deformation and large workload.
Most of the existing permanent deformation estimation models are complex in form, the difference between an estimation result and a measured value of an indoor dynamic triaxial test is large, the accuracy of the estimation result is poor, and the reliability is poor; the existing typical roadbed permanent deformation estimation model comprises formulas (1) to (6):
εp1＝ANb (1)
in the formula, epsilonp1For representing the permanent deformation value of the roadbed considering the loading times, A, b is a model parameter, and N is the loading timesAnd (4) counting.
Figure BDA0002100714120000021
In the formula, epsilonp2To represent the permanent deformation value of the subgrade taking into account the number of loads and the body stress, alpha1、α2、α3As a model parameter, σoctIn order to be the bulk stress,
Figure BDA0002100714120000022
σ1principal stress, σ3Is confining pressure, σatmFor reference, the stress is typically atmospheric pressure (100kPa), in other words the same.
Figure BDA0002100714120000023
In the formula, epsilonp3To represent the permanent deformation value of the subgrade taking into account the number of loads, the bulk stress and the shear stress of the octahedron, alpha1、α2、α3、α4As a model parameter, τoctThe shear stress of the octahedron is changed,
Figure BDA0002100714120000024
the other meanings are the same as above.
Figure BDA0002100714120000025
In the formula, epsilonp4To express the permanent deformation of the subgrade considering the number of times of loading, the volume stress, the shear stress of the octahedron and the water content, alpha1、α2、α3、α4、α5Is a model parameter, w is the actual water content, womcThe other meanings are the same as above for the optimal water content.
Figure BDA0002100714120000026
In the formula, epsilonp5To represent the permanent deformation of the subgrade taking into account the number of loads, the offset stress and the rebound deformation I, J is a model parameter, sigmadTo bias stress, MrThe elastic modulus is the same as above.
Figure BDA0002100714120000027
In the formula, epsilonp6In order to express the permanent deformation of the roadbed considering the loading times and the substrate suction force, a, b, c and d are model parameters, umAs a substrate suction, paThe atmospheric pressure (100Kpa), DDR (double data Rate) and PI (plasticity index) are respectively used as the dry density ratio, RCM (resin content) is the proportion of the crushed stone in the mixed filler, and the other meanings are as above.
Through analysis, the exponential type estimation models in the formulas (1) to (5) have simple structures, and all variables have definite meanings, but the influence of the compactness on the permanent deformation is not considered; the estimation model in the formula (6) is complex in structure, and although the influence of DDR (double data rate) as an index on compactness is considered, the substrate suction force needs to be measured, the test amount is large, errors easily occur, and the prediction precision is influenced.
Disclosure of Invention
In order to solve the problems, the invention provides a construction waste roadbed permanent deformation orthogonal estimation method based on a gray system, which comprehensively considers construction waste roadbed permanent deformation influence factors, effectively reduces the test amount, has high efficiency, small workload and easy operation, reduces the possibility of occurrence of test errors, can accurately estimate the construction waste roadbed permanent deformation, and solves the problems that the existing permanent deformation estimation model does not relate to construction waste, the required test amount is large, the estimation error is large, and the cost is high.
The invention adopts the technical scheme that a construction waste roadbed permanent deformation orthogonal estimation method based on a grey system specifically comprises the following steps:
s1, determining influence factors and research levels of permanent deformation of the roadbed, wherein the influence factors are water content, compactness, confining pressure and offset stress;
s2, designing a four-factor three-level orthogonal test to simulate different working conditions;
s3, manufacturing a test piece by using the construction waste roadbed filling, and carrying out a dynamic triaxial test to obtain the permanent deformation values of the test piece simulating different working conditions;
s4, calculating the association degree between the permanent deformation value of each test piece and each influence factor by using a grey theory, and sorting the influence degree of each factor according to the association degree value;
s5, constructing a permanent deformation estimation model of the roadbed according to the grey correlation calculation result of the influencing factors, which is shown in the following formula,
Figure BDA0002100714120000031
wherein epsilonpIndicating permanent deformation of the road bed of construction waste, alpha1、α2、α3、α4、α5、α6Are all model parameters, N is the number of times of loading, K is the degree of compaction, σoctIn order to realize the body stress, the stress,
Figure BDA0002100714120000032
σ1principal stress, σ3Is confining pressure, σatmFor reference to stress, atmospheric pressure, τoctIs the shear stress of an octahedron,
Figure BDA0002100714120000033
w is the actual water content, womcThe optimal water content is obtained; and (4) fitting the permanent deformation prediction model of the roadbed by using EXCEL software and a nonlinear regression method in a planning and solving function and using the permanent deformation orthogonal test result in the step 3 to obtain model parameters, so that the permanent deformation value of the construction waste roadbed in the step S3 under other working conditions is predicted.
Further, in the step S2, in the four-factor three-level orthogonal test, the water content is 0.9OMC, 1.0OMC, and 1.1OMC, where the OMC is the optimal water content of the filler; the compaction degree is 90%, 93% and 96%, the confining pressure is 12kPa, 28kPa and 42kPa, and the bias stress is 28kPa, 48kPa and 69 kPa.
Further, in step S3, obtaining the permanent deformation values of the test pieces under different working conditions specifically includes: determining the size of a test piece according to the grain size range of the selected building waste filler, obtaining the maximum dry density and the optimal water content of the material through a compaction test, and manufacturing the test piece according to the target water content and the compaction degree in the designed orthogonal test scheme; and applying different confining pressure and bias stress to the test piece by adopting a dynamic triaxial apparatus, and obtaining the permanent deformation value of the test piece after intermittent loading for multiple times.
Further, in step S4, the association between the permanent deformation value of each test piece and each influence factor is calculated by using a gray theory, which specifically includes the following steps:
s41, taking the permanent deformation value of each test piece as a reference sequence and recording as X0＝{x0(k) K is 1,2,. n }, and the influence factors are used as comparison sequences and are marked as Xi＝{xi(k) 1,2,. n }, first normalized dimensionless processing is performed according to the following formula:
Figure BDA0002100714120000041
Figure BDA0002100714120000042
representing the value of the comparison sequence after standardized dimensionless treatment,
Figure BDA0002100714120000043
expressing the numerical value of the reference sequence after standardized dimensionless treatment, wherein k is the corresponding working condition number, and i is the number of the influence factor;
s42, calculating the proximity:
Figure BDA0002100714120000044
wherein, Δi(k) The closeness of the influence factor i to the permanent deformation after standardized dimensionless treatment under the kth working condition;
s43, calculating the correlation coefficient:
Figure BDA0002100714120000045
wherein xi isi(k) The coefficient of correlation between the influencing factor i and the permanent deformation under the kth working condition, DeltaminFor the minimum of all the calculated proximity values, ΔmaxTaking rho as a sensitivity coefficient to be the maximum value of all the calculated proximity values, and taking the value of 0.5-0.6;
s44, calculating the association degree between the test result and each influence factor:
Figure BDA0002100714120000046
wherein, γiAnd n is the total number of working conditions for the relevance degree of the permanent deformation and the influencing factor i.
Further, the intermittent loading frequency is 1Hz, wherein the loading is carried out for 0.2s, the intermittent loading is carried out for 0.8s, and the result after the 10000 times of loading is taken as the permanent deformation value of the test piece.
Further, in the step S5, when the roadbed permanent deformation prediction model parameters are fitted according to the permanent deformation orthogonal test result, the test results after being loaded for 100, 300, 500, 700, 900, 1000, 2000, 3000, 4000, 5000, 6000, 8000 and 10000 times are taken for fitting.
The method has the advantages that the stress state and the physical state of the building waste filler are comprehensively analyzed, a reasonable orthogonal test is designed, a new roadbed permanent deformation estimation model is constructed according to the gray correlation degree calculation result of the influence factors, the roadbed permanent deformation estimation model is fitted by using the permanent deformation orthogonal test result to obtain model parameters, and therefore the permanent deformation value of the same building waste roadbed under other working conditions is estimated. The number of test pieces required for carrying out four-factor three-level influence factor full-test research is reduced from 81 to 9, and test data verifies that the estimated permanent deformation value is very close to the actual measurement value of the indoor dynamic triaxial test, so that the model obtained by the method is high in precision and strong in prediction reliability; the method has the advantages that the accuracy of the pre-estimated result is guaranteed, the test quantity is greatly reduced, the test efficiency is improved, the problem that the test times are too many when a multi-factor multi-level full test is carried out is effectively solved, the test quantity of the dynamic triaxial test is effectively reduced, the possibility of large errors in the test is reduced, and the prediction precision is improved.
The new variables introduced by the roadbed permanent deformation estimation model have good compatibility with the selected existing model, and the model parameters can be obtained only by means of conventional fitting software, so that a basis is provided for accurately calculating the roadbed permanent deformation of the construction waste; the test cost is reduced, better guidance and suggestion are provided for the application of the recycled material in road engineering in the environment of 'green traffic', and obvious economic and social benefits are achieved.
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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 a method in an embodiment of the invention.
FIG. 2 is a graph comparing the predicted permanent set values of the present invention with the permanent set values measured in an indoor dynamic triaxial test.
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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
A construction waste roadbed permanent deformation orthogonal estimation method based on a grey system specifically comprises the following steps:
s1, determining influence factors and research levels of permanent deformation of the roadbed, wherein the influence factors are water content, compactness, confining pressure and offset stress; the research level of each factor is respectively selected as follows: the water content is 0.9OMC (OMC is the optimum water content of the filler), 1.0OMC, 1.1OMC, the compactness is 90%, 93%, 96%, the confining pressure is 12kPa, 28kPa, 42kPa, and the bias stress is 28kPa, 48kPa, 69 kPa;
s2, designing a test scheme by adopting a reasonable orthogonal table according to the influence factors and the number of research levels and based on the fractional principle of factor design; the orthogonal test scheme is designed according to the principle of 'uniform dispersion and orderliness comparability', the times of different numbers appearing in each column are required to be equal, the arrangement modes of the numbers in any two columns are complete and balanced, different research levels of all influencing factors are arranged from small to large and are marked as level 1, level 2 and level 3 in sequence, a test piece is manufactured by using construction waste roadbed filling, a four-factor three-water orthogonal test is designed, and the specific orthogonal test scheme is designed as follows: 1111. 1222, 1333, 2123, 2231, 2312, 3132, 3213, 3321, simulating different working conditions;
s3, determining the size of the test piece according to the particle size range of the selected building waste filler, obtaining the maximum dry density and the optimal water content of the material through a compaction test, and manufacturing the test piece according to the target water content and the compaction degree in the designed orthogonal test scheme; applying different confining pressure and bias stress to the test piece by adopting a dynamic triaxial apparatus, wherein the loading frequency is 1Hz during the test, the loading is carried out for 0.2s, the interval is 0.8s, and the result after the 10000 th loading is taken as the permanent deformation value of the test piece;
s4, calculating the association degree between the permanent deformation value of each test group and each influence factor by using a grey theory, and sorting the influence degree of each factor according to the association degree value;
s41, taking the permanent deformation value of each test set as a reference sequence and marking as X0＝{x0(k) Where k is 1, 2.. n }, and the influencing factors (moisture content, degree of compaction, confining pressure, bias stress) are used as a comparison sequence and are denoted as Xi＝{xi(k) Where k is 1,2,. n }, first normalized according to equation (7) without normalizationDimension treatment:
Figure BDA0002100714120000061
Figure BDA0002100714120000062
representing the value of the comparison sequence after standardized dimensionless treatment,
Figure BDA0002100714120000063
expressing the numerical value of the reference sequence after standardized dimensionless treatment, wherein k is the corresponding working condition number, and i is the number of the influence factor;
s42, calculating the proximity according to equation (8):
Figure BDA0002100714120000064
wherein, Δi(k) The closeness of the influence factor i to the permanent deformation after standardized dimensionless treatment under the kth working condition;
s43, calculating the correlation coefficient according to equation (9):
Figure BDA0002100714120000071
wherein xi isi(k) The coefficient of correlation between the influencing factor i and the permanent deformation under the kth working condition, DeltaminIs the minimum value, Δ, of all the proximity values calculated according to equation (8)maxThe rho is the sensitivity coefficient (usually 0.5-0.6) which is the maximum value of all the proximity values obtained by calculation according to the formula (8);
s44, calculating the association degree between the test result and each influence factor according to the formula (10):
Figure BDA0002100714120000072
wherein, gamma isiIs a permanent magnetAnd (4) the association degree of the long-time deformation and the influence factor i, wherein n is the total number of the working conditions.
S5, according to the grey correlation degree calculation result of the influence factors, constructing a permanent deformation estimation model of the roadbed, see formula (11),
Figure BDA0002100714120000073
wherein epsilonpIndicating permanent deformation of the road bed of construction waste, alpha1、α2、α3、α4、α5、α6Are all model parameters, N is the number of times of loading, K is the degree of compaction, σoctIs body stress, σatmFor reference to stress, atmospheric pressure, τoctShear stress of octahedron, w actual water content, womcThe optimal water content is obtained; adopting EXCEL software, fitting the roadbed permanent deformation estimation model by a nonlinear regression method in a planning and solving function by using a permanent deformation orthogonal test result to obtain model parameters, and estimating the permanent deformation value of the construction waste roadbed according to the roadbed permanent deformation estimation model; and when model parameters are fitted according to the test results, fitting is carried out on the test results after the loads of 100 th, 300 th, 500 th, 700 th, 900 th, 1000 th, 2000 th, 3000 th, 4000 th, 5000 th, 6000 th, 8000 th and 10000 th times.
Examples
The concrete implementation process of the invention is demonstrated by using the construction waste filler which is taken from the test section of filling the roadbed of the highway by the construction waste of the Beijing capital loop highway (the Daxing-Tongzhou section).
Step 1, determining influence factors of permanent deformation of a roadbed and setting different research levels of all the factors; the water content is 0.9OMC, 1.0OMC, 1.1OMC, the compaction degree is 90%, 93%, 96%, the confining pressure is 12kPa, 28kPa, 42kPa, and the bias stress is 28kPa, 48kPa, 69 kPa;
step 2, according to the principle of 'uniform dispersion, uniformity and comparability', different research levels of all influencing factors are arranged from small to large and are marked as level 1, level 2 and level 3 in sequence, and a four-factor three-level orthogonal test scheme is designed to be under 9 working conditions (respectively level 1111, 1222, 1333, 2123, 2231, 2312, 3132, 3213 and 3321) as shown in table 1;
TABLE 1 orthogonal test Schedule
Figure BDA0002100714120000081
Step 3, the particle screening result shows that the maximum particle size of the selected building waste filler exceeds 19mm, and a test piece with the diameter of 150mm and the height of 300mm is required to be manufactured according to the requirement of a triaxial test; the maximum dry density and the optimum water content of the material obtained by the compaction test are respectively 1.843g/cm314.8%, manufacturing test pieces according to the water content and the compactness in the orthogonal test scheme in the table 1, and performing a permanent deformation test on the test pieces simulating different working conditions; the dynamic triaxial apparatus applies corresponding confining pressure and bias stress shown in table 1 to the test piece, the loading frequency is 1Hz during the test, wherein the loading is carried out for 0.2s and the interval is 0.8s, the result after the 10000 th loading is taken as the permanent deformation value of the test piece, and the test result is shown in table 2 below;
TABLE 2 statistical table of permanent deformation orthogonal test results
Figure BDA0002100714120000091
And 4, calculating the association degree between the test result and each influence factor by using a grey theory, and sequencing the influence degrees of each factor according to the value of the association degree. The permanent set value is taken as a reference sequence and is marked as X0＝{x0(k) N, and the influencing factors (water content, degree of compaction, confining pressure, bias stress) are used as comparison sequences and are marked as Xi＝{xi(k) Where k is 1,2,. n }, n denotes the number of operating conditions, i denotes an influence factor (i denotes a water content when i is 1, a degree of compaction when i is 2, a confining pressure when i is 3, and an offset stress when i is 4), and k denotes an operating condition, first, a standardized dimensionless process is performed according to equation (7):
Figure BDA0002100714120000092
Figure BDA0002100714120000093
representing the value of the comparison sequence after standardized dimensionless treatment,
Figure BDA0002100714120000094
the normalized dimensionless values of the reference sequence are shown in Table 3 below:
TABLE 3 statistical table of normalized dimensionless processing results
Figure BDA0002100714120000101
The proximity is then calculated as equation (8):
Figure BDA0002100714120000102
wherein, Δi(k) The closeness of the influence factor i to the permanent deformation after standardized dimensionless treatment under the kth working condition; the proximity calculation results are shown in table 4:
table 4 statistical table of proximity calculation results
△1(k) △2(k) △3(k) △4(k)
0.0000 0.0000 0.0000 0.0000
0.3782 0.3448 0.9552 0.3361
0.7143 0.6476 1.7857 0.7500
2.3987 2.5098 1.1765 1.0455
0.1895 0.1118 2.5784 0.0784
1.4491 1.4936 1.5602 0.8459
0.4388 0.6611 1.8389 0.0532
4.1335 4.3224 4.3557 2.8915
1.1363 1.2919 0.0252 1.3585
As can be seen from Table 4,. DELTA.min＝0，ΔmaxWhen 4.3557, the correlation coefficient is calculated according to equation (9):
Figure BDA0002100714120000111
wherein ξi(k) Taking a sensitivity coefficient rho of 0.5 as a correlation coefficient of an influence factor i and permanent deformation under the kth working condition; the correlation coefficient calculation results are shown in table 5:
TABLE 5 statistical table of correlation coefficient calculation results
ξ1(k) ξ2(k) ξ3(k) ξ4(k)
1 1 1 1
0.8710 0.8811 0.7278 0.8837
0.7815 0.7977 0.5886 0.7730
0.5157 0.5044 0.6847 0.7096
0.9309 0.9581 0.4977 0.9702
0.6380 0.6310 0.6208 0.7512
0.8534 0.7944 0.5814 0.9796
0.3819 0.3714 0.3697 0.4690
0.6921 0.6641 0.9902 0.6528
And finally, calculating the association degree between the test result and each influence factor according to the formula (10) and sequencing the influence degrees of the factors according to the numerical value of the association degree:
Figure BDA0002100714120000112
γ1＝0.7151,γ2＝0.7081,γ3＝0.6443,γ4＝0.7770，γ1showing the degree of association of permanent deformation with water content, gamma2Representing the degree of association of permanent set with degree of compaction, gamma3Representing the degree of association of permanent deformation with confining pressure, gamma4Representing the degree of association of permanent deformation and bias stress; the result shows that the influence degrees of all factors on the permanent deformation of the construction waste roadbed are ranked as follows: the deflection stress is larger than the water content is larger than the compactness is larger than the confining pressure.
And 5, comprehensively analyzing the existing roadbed permanent deformation estimation model, wherein the calculation result of the grey correlation degree of each influence factor in the step 4 shows that the compaction degree has obvious influence on the roadbed permanent deformation of the construction waste, so that the compaction degree must be reflected in an estimation equation. And (3) selecting an exponential model with more comprehensive consideration factors shown in the formula (4) for perfecting, and introducing the compactness into the formula (4) in an exponential form to obtain an estimated model suitable for permanent deformation of the roadbed filled with the construction waste, wherein the estimated model is shown in the formula (11):
Figure BDA0002100714120000113
in the formula, epsilonpIndicating permanent deformation of the road bed of construction waste, alpha1、α2、α3、α4、α5、α6As model parameters, K is the compaction, N is the number of loads, σoctIn order to be the bulk stress,
Figure BDA0002100714120000121
σ1principal stress, σ3Is confining pressure, σatmFor reference stress, typically atmospheric pressure (100kPa), τoctIs the shear stress of an octahedron,
Figure BDA0002100714120000122
w is the actual water content, womcThe optimal water content is obtained; adopting EXCEL software, and fitting the improved pre-estimated model according to the test results of permanent deformation orthogonal tests after loading for 100, 300, 500, 700, 900, 1000, 2000, 3000, 4000, 5000, 6000, 8000 and 10000 times by a nonlinear regression method in a planning and solving function to obtain a model parameter alpha1、α2、α3、α4、α5、α6And estimating the permanent deformation value of the construction waste roadbed according to the roadbed permanent deformation estimation model.
The fitting results are shown in table 6:
TABLE 6 statistical table of fitting results
α1 α2 α3 α4 α5 α6 R2
0.412 0.212 2.208 -0.523 1.097 3.296 0.909
As can be seen from Table 6, the correlation coefficient of the prediction model of permanent deformation of the roadbed of the construction waste corresponding to the test results under 9 working conditions is greater than 0.9, which shows that the model obtained by the method has better precision and reliability.
In order to further verify the accuracy of the model shown in the formula (11), the test scheme shown in table 7 is adopted to simulate another 9 working conditions for a permanent deformation test.
TABLE 7 model verification test arrangement table
Figure BDA0002100714120000123
The actual permanent deformation values of the test pieces under 9 working conditions shown in Table 7 are measured by adopting an indoor dynamic triaxial test, and meanwhile, the permanent deformation values of the test pieces under 9 working conditions are estimated by adopting the estimation model of permanent deformation of the roadbed filled with the construction waste, namely the compaction degree K, the loading times N and the body stress sigma of the test pieces under 9 working conditionsoctReference stress σatmGenerally atmospheric pressure (100kPa), octahedral shear stress tauoctActual water content w, optimum water content womcRespectively substituting the formula (11) to obtain the estimated permanent deformation value, drawing a graph 2 according to the real permanent deformation value and the estimated permanent deformation value, wherein R is shown in the graph 22The proximity of the permanent deformation value obtained by the estimation method of the invention and the indoor dynamic triaxial test measurement value is very high, which fully shows that the model obtained by the estimation method of the invention has high precision and strong prediction reliability, and greatly shrinks while ensuring the accuracy of the estimation resultThe test quantity is reduced, the test efficiency is improved, the test cost is reduced, and a basis is provided for accurately calculating the permanent deformation of the construction waste roadbed.
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 (3)

1. A construction waste roadbed permanent deformation orthogonal estimation method based on a gray system is characterized by comprising the following steps:
s1, determining influence factors and research levels of permanent deformation of the roadbed, wherein the influence factors are water content, compactness, confining pressure and offset stress;
s2, designing a four-factor three-level orthogonal test to simulate different working conditions;
s3, manufacturing a test piece by using the construction waste roadbed filling, and carrying out a dynamic triaxial test to obtain the permanent deformation values of the test piece simulating different working conditions;
s4, calculating the association degree between the permanent deformation value of each test piece and each influence factor by using a grey theory, and sequencing the influence degrees of each factor according to the association degree value, wherein the influence degrees of each factor on the permanent deformation value of the roadbed of the construction waste are sequenced as follows: the deflection stress is larger than the water content is larger than the compactness is larger than the confining pressure;
s5, according to the grey correlation degree calculation result of the influence factors, constructing a permanent deformation estimation model of the roadbed, see the formula (1-1),
Figure FDF0000017628100000011
wherein epsilonpIndicating permanent deformation of the road bed of construction waste, alpha1、α2、α3、α4、α5、α6Are all model parameters, N is the number of times of loading, K is the degree of compaction, σoctIn order to realize the body stress, the stress,
Figure FDF0000017628100000012
σ1principal stress, σ3Is confining pressure, σatmFor reference to stress, atmospheric pressure, τoctThe shear stress of the octahedron is changed,
Figure FDF0000017628100000013
w is the actual water content, womcThe optimal water content is obtained; adopting EXCEL software, fitting the road base permanent deformation estimation model by using the permanent deformation orthogonal test result in the step 3 through a nonlinear regression method in a planning and solving function to obtain model parameters, and estimating the permanent deformation value of the construction waste road base in the step S3 under other working conditions by using a formula (1-1);
in the step S2, in the four-factor three-level orthogonal test, the water content is 0.9OMC, 1.0OMC, 1.1OMC, where OMC is the optimal water content of the filler; the compactness is 90%, 93% and 96%, the confining pressure is 12kPa, 28kPa and 42kPa, and the bias stress is 28kPa, 48kPa and 69 kPa;
in step S4, the association between the permanent deformation value of each test piece and each influence factor is calculated by using a gray theory, and the method specifically includes the following steps:
s31, taking the permanent deformation value of each test piece as a reference sequence and recording as X0＝{x0(k) K is 1,2,. n }, and the influence factors are used as comparison sequences and are marked as Xi＝{xi(k) First, a normalized dimensionless process is performed according to equation (2-1):
Figure FDF0000017628100000014
Figure FDF0000017628100000021
representing the value of the comparison sequence after standardized dimensionless treatment,
Figure FDF0000017628100000022
expressing the numerical value of the reference sequence after standardized dimensionless treatment, wherein k is the corresponding working condition number, and i is the number of the influence factor;
s32, calculating the proximity according to the formula (2-2):
Figure FDF0000017628100000023
wherein, Deltai(k) The closeness of the influence factor i to the permanent deformation after the standardized dimensionless treatment under the kth working condition;
s33, calculating the correlation coefficient according to the formula (2-3):
Figure FDF0000017628100000024
wherein xi isi(k) Is the coefficient of correlation between the influencing factor i and the permanent deformation, deltaminIs the minimum value, Delta, of all the proximity values calculated according to equation (2-2)maxTaking the value of rho as a sensitivity coefficient as the maximum value in all the proximity values calculated according to the formula (2-2), and taking the value of 0.5-0.6;
s34, calculating the association degree between the test result and each influence factor according to the formula (2-4):
Figure FDF0000017628100000025
wherein, γiThe correlation degree of the permanent deformation and the influence factor i is shown, and n is the total number of working conditions;
in step S3, the permanent deformation values of the test pieces under different working conditions are obtained, specifically: determining the size of a test piece according to the grain size range of the selected building waste filler, obtaining the maximum dry density and the optimal water content of the material through a compaction test, and manufacturing the test piece according to the target water content and the compaction degree in the designed orthogonal test scheme; and applying different confining pressure and bias stress to the test piece by adopting a dynamic triaxial apparatus, and obtaining the permanent deformation value of the test piece after intermittent loading for multiple times.
2. The method for estimating the orthogonality of the permanent deformation of the roadbed of the construction wastes based on the gray system as claimed in claim 1, wherein the intermittent loading frequency is 1Hz, the loading is carried out for 0.2s, the intermittent loading is carried out for 0.8s, and the result after the 10000 th loading is taken as the permanent deformation value of the test piece.
3. The method for estimating orthogonality of the permanent deformation of the roadbed of the construction wastes based on the gray system as claimed in claim 1, wherein in the step S5, when the model parameters of the roadbed permanent deformation estimation are fitted according to the permanent deformation orthogonal test results, the test results after the 100 th, 300 th, 500 th, 700 th, 900 th, 1000 th, 2000 th, 3000 th, 4000 th, 5000 th, 6000 th, 8000 th and 10000 th times of loading are taken for fitting.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106480868A (en) * 2016-09-22 2017-03-08 长沙理工大学 A kind of earth roadbed New Method for Predicting Deformation of particulate
CN109690284A (en) * 2016-06-30 2019-04-26 坎特伯雷大学 For testing the device and method of road surface sample

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109690284A (en) * 2016-06-30 2019-04-26 坎特伯雷大学 For testing the device and method of road surface sample
CN106480868A (en) * 2016-09-22 2017-03-08 长沙理工大学 A kind of earth roadbed New Method for Predicting Deformation of particulate

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Experimental and Modeling Studies of Permanent;Puppala A J et.al;《Journal of Geotechnical&Geoenvironmental Engineering》;20091231;第135卷(第10期);第1379-1389页 *
南方湿热地区高液限黏土永久变形预估研究;张磊;《中国优秀硕士学位论文全文数据库工程科技II辑》;20190315(第3期);摘要、第二章、第四章，表4.2 *
基于灰色关联度法的SBS改性沥青车辙因子影响因素分析;朱平等;《中国建材科技》;20141231;第23卷(第3期);第73页第1节至第75页第5节，表3 *

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