Method for determining geopolymer improved soil roadbed maintenance time based on damage self-repairing rate

Abstract

The invention discloses a geopolymer improved soil roadbed maintenance time determination method based on a damage self-repairing rate. The method comprises the following steps: establishing an improved soil unconfined compressive strength and elastic modulus prediction model under different maintenance time; establishing an improved soil roadbed finite element mechanical simulation model considering the overlying base layer construction, and determining the improved soil roadbed micro-damage stress level caused by the construction mechanical action at different maintenance time; designing a pre-micro-damage dynamic triaxial test, and establishing an unconfined compressive strength and elastic modulus estimation model of the improved soil after pre-micro-damage under different maintenance time; and a damage self-repairing rate is introduced, the maintenance time of the improved soil before micro-damage is pre-added and meeting the unconfined compressive strength and elastic modulus self-repairing requirements is determined, and the larger value of the two maintenance time is taken as the final maintenance time of the improved soil. According to the method, the maintenance time before micro-damage pre-adding meeting the requirement for the micro-damage self-repairing rate is obtained, the construction efficiency is improved while the construction bearing requirement is met, and operation is easy, scientific and reasonable.

Description

Technical Field

[0001] The invention belongs to the technical field of roadbed construction and relates to a method for determining the curing time of a geopolymer-improved soil roadbed based on a damage self-repair rate. Background Art

[0002] Existing roadbed improved soil construction mostly uses traditional materials such as lime and cement. These materials usually need to be cured for 7 days after construction is completed to ensure that the improved soil roadbed reaches a certain bearing capacity, thereby meeting the requirements of subsequent semi-rigid base construction. However, if the curing time is too short, the bearing capacity of the improved soil roadbed is insufficient, and micro-damage will be caused to the improved soil roadbed during the construction of the semi-rigid base on the upper layer, which will easily affect the development of the unconfined compressive strength and elastic modulus of the roadbed and thus damage the roadbed; on the contrary, if the curing time is too long, it will affect the progress of the project and delay the construction period. Geopolymer materials have a faster unconfined compressive strength development rate. Compared with lime and cement materials, they can make the improved soil roadbed reach the bearing capacity required for semi-rigid base construction in a shorter time. The present invention uses 7d (168h) as the standard curing time, corresponding to the final unconfined compressive strength and elastic modulus of the improved soil.

[0003] When using geopolymer materials to improve roadbed soil, the current practice is to refer to lime and cement improved soil and use a 7-day curing time. However, geopolymer materials have an early strength effect, and the 7-day curing time is no longer applicable. Therefore, it is necessary to find a curing time to ensure that the project progress is not delayed while ensuring that the unconfined compressive strength of the roadbed is not damaged during base construction. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a method for determining the curing time of geopolymer-improved soil roadbed based on the damage self-repair rate, thereby obtaining the curing time before pre-addition of micro-damage that meets the requirements of the micro-damage self-repair rate, thereby meeting the construction bearing requirements while improving the construction efficiency, and the operation is simple, scientific and reasonable.

[0005] The technical solution adopted by the present invention is a method for determining the curing time of a geopolymer-improved soil roadbed based on the damage self-repair rate, comprising the following steps:

[0006] Step 1: Determine the geopolymer content α and the volume porosity η of the improved soil according to the engineering improvement plan. Substitute the geopolymer content α, the volume porosity η of the improved soil, and the standard curing time into the improved soil unconfined compressive strength prediction model and the improved soil elastic modulus prediction model with curing time, volume porosity, and geopolymer content as variables, as shown in Equations (1) and (2), respectively, to obtain the final unconfined compressive strength and elastic modulus of the improved soil without pre-damaged micro-damage.

[0007]

[0008] Where: a0, b0, c1, d1, e1, f1, c2, d2, e2 and f2 are constant coefficients; E is the elastic modulus of the improved soil; UCS is the unconfined compressive strength of the improved soil; t is the curing time; α is the amount of geopolymer; η is the volume porosity of the improved soil;

[0009] Step 2: The final unconfined compressive strength and elastic modulus of the modified soil without pre-addition of micro-damage are multiplied by the micro-damage self-repair rate to obtain the unconfined compressive strength and elastic modulus of the modified soil after pre-addition of micro-damage that meet the self-repair rate requirements;

[0010] Step 3: Substitute the unconfined compressive strength and elastic modulus of the improved soil obtained in step 2 that meet the self-repair rate requirements after pre-micro-damage into the prediction model of the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage for the curing time before pre-micro-damage, as shown in equations (3) and (4):

[0011] UCS′=a′ln(t′)+b′ (3)

[0012] E′=c′ln(t′)+d′ (4)

[0013] Where: a′, b′, c′ and d′ are constant coefficients; UCS′ is the final unconfined compressive strength of the improved soil after pre-micro-damage; E′ is the final elastic modulus of the improved soil after pre-micro-damage; t′ is the curing time of the improved soil before pre-micro-damage;

[0014] Thus, the curing time before micro-damage is added is obtained, and the larger of the two curing times is taken as the final curing time of the improved soil.

[0015] Furthermore, in step 1, the method for determining the unconfined compressive strength prediction model and the elastic modulus prediction model of the improved soil with curing time, volume porosity, and geopolymer content as variables includes the following steps:

[0016] Step 11: Prepare geopolymer, obtain the unconfined compressive strength of geopolymer at different curing times through experiments, and based on the corresponding relationship between the unconfined compressive strength of geopolymer and curing time, select the form with the highest fit through data planning and solving method to establish a prediction model of the unconfined compressive strength of geopolymer with respect to curing time;

[0017] Step 12: preparing modified soil specimens with different geopolymer content, and obtaining the volume porosity of the modified soil specimens and the unconfined compressive strength and elastic modulus of the modified soil specimens at different curing times through experiments;

[0018] Step 13: Based on the influence of the curing time, volume porosity, and geopolymer content of the improved soil specimens on the unconfined compressive strength of the improved soil specimens, a prediction model that conforms to the influence rules is obtained by combining multiple linear regression with an artificial neural network algorithm. The prediction model of the unconfined compressive strength of the geopolymer obtained in step 11 is then coupled to establish a prediction model of the unconfined compressive strength of the improved soil with curing time, volume porosity, and geopolymer content as variables.

[0019] Based on the influence of the curing time, volume porosity and geopolymer content of the improved soil specimens on the elastic modulus of the improved soil specimens, a prediction model that conforms to the said influence law is obtained by combining multiple linear regression with an artificial neural network algorithm. Then, the geopolymer unconfined compressive strength prediction model obtained in step 11 is coupled to establish a prediction model for the elastic modulus of improved soil with curing time, volume porosity and geopolymer content as variables.

[0020] Furthermore, in step 11, the prediction model of the unconfined compressive strength of geopolymer with respect to curing time is:

[0021] s=a0 ln(t)+b0

[0022] Where: a0 and b0 are constant coefficients, s is the unconfined compressive strength of geopolymer, and t is the curing time.

[0023] Furthermore, in step 11, the constant coefficient is determined by:

[0024] Based on the measured unconfined compressive strength of geopolymer, the coefficient with the minimum fitting error is obtained according to the least square method in the form of the prediction model, thus obtaining:

[0025] s=13.942ln(t)-37.630

[0026] Where t is the curing time.

[0027] Furthermore, in step 1, the method for determining the constant coefficient is as follows: based on the measured unconfined compressive strength of the improved soil, the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model formula (1), thereby obtaining:

[0028]

[0029] Based on the measured elastic modulus of improved soil, the coefficient with the minimum fitting error is obtained according to the least square method in the form of prediction model (2), thus obtaining:

[0030]

[0031] Where t is the curing time.

[0032] Furthermore, in step 2, the micro-damage self-repair rate is determined according to the highway grade. The micro-damage self-repair rate of an expressway or a first-class highway is 80%; the micro-damage self-repair rate of a second-class highway is 70%; and the micro-damage self-repair rate of a third-class or fourth-class highway is 60%.

[0033] Furthermore, in step 3, the method for determining the prediction model of the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage with respect to the curing time before pre-micro-damage includes the following steps:

[0034] Step 31: Establish a finite element mechanical simulation model of the improved soil roadbed taking into account the construction of the overlying base in ABAQUS finite element calculation software. Input the predicted elastic modulus of the improved soil under different curing times. Based on the semi-rigid base construction method, input the base deadweight load and the force acting on the improved soil roadbed due to mechanical compaction during base construction. Use ABAQUS finite element calculation software to calculate the maximum vertical stress response σ1 and transverse confining compressive stress σ3 generated in the improved soil roadbed during the construction of the semi-rigid base.

[0035] Step 32: Using the maximum vertical stress response σ1 and transverse confining pressure stress σ3 calculated in step 31 as the pre-micro-damage stress level, conduct a dynamic triaxial test of the improved soil with pre-micro-damage, and convert the pre-micro-damage duration and number according to the semi-rigid base construction method; conduct dynamic triaxial tests of the improved soil with pre-micro-damage after different curing times, and then continue curing to the standard curing time; obtain the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage under different curing times before pre-micro-damage through experiments; based on the corresponding relationship between the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage and the curing time before pre-micro-damage, select the form with the highest fitting degree through the data planning solution method, and establish prediction models of the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage with respect to the curing time before pre-micro-damage.

[0036] Furthermore, in step 3, the method for determining the constant coefficient is as follows: based on the measured unconfined compressive strength of the improved soil after micro-damage, the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model formula (3), thereby obtaining:

[0037] UCS′=0.941ln(t′)-2.766

[0038] Based on the measured elastic modulus of the improved soil after micro-damage, the coefficient with the minimum fitting error is obtained according to the least squares method in the form of the prediction model (4), thus obtaining:

[0039] E′=236.770ln(t ′ )-410.810

[0040] Where: t′ is the curing time before micro-damage of improved soil.

[0041] The beneficial effects of the present invention are:

[0042] The present invention systematically analyzes the variation patterns of the unconfined compressive strength and stiffness of geopolymer-improved soil roadbed with and without pre-damage at different curing times. In combination with actual engineering needs, the present invention determines the shortest curing time that can meet the bearing capacity requirements of semi-rigid base construction and ensure the long-term performance stability of the roadbed.

[0043] The present invention combines factors such as geopolymer content, curing time, improved soil porosity, and geopolymer unconfined compressive strength to form a prediction model for the unconfined compressive strength and elastic modulus of improved soil through fitting a large number of test results. The unconfined compressive strength prediction model for geopolymer is coupled to take into account the impact of the unconfined compressive strength development trend of the cementitious material in the improved soil. Furthermore, the porosity and geopolymer content are equivalent to considering the impact of the soil sample itself and the influence of the composition ratio of the soil sample and geopolymer in the improved soil. The present invention can scientifically and rationally determine the curing time of geopolymer-improved soil roadbeds, fill in technical gaps, and improve construction efficiency. By adopting dual control indicators of unconfined compressive strength and stiffness (elastic modulus) damage self-repair rate, the quality and long-term stability of the improved soil roadbed can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a flow chart of a method for determining maintenance time according to an embodiment of the present invention.

[0046] Figure 2 It is a model for estimating the unconfined compressive strength of geopolymer in an embodiment of the present invention.

[0047] Figure 3 It is the fitting result of the unconfined compressive strength estimation model of improved soil in the embodiment of the present invention.

[0048] Figure 4 It is the fitting result of the improved soil elastic modulus estimation model in the embodiment of the present invention.

[0049] Figure 5 It is the base construction load action model in the embodiment of the present invention.

[0050] Figure 6 It is the stress output result of the finite element calculation in the embodiment of the present invention.

[0051] Figure 7 2 is a schematic diagram of a dynamic triaxial test with pre-added micro-damage in an embodiment of the present invention.

[0052] Figure 8 It is a model for estimating the final unconfined compressive strength of pre-damaged improved soil in an embodiment of the present invention.

[0053] Figure 9 It is the final elastic modulus estimation model of the pre-damaged improved soil in the embodiment of the present invention.

[0054] Figure 10 The unconfined compressive strength micro-damage self-repair rate index in the embodiment of the present invention is used to determine the curing time of the geopolymer improved soil.

[0055] Figure 11 The elastic modulus micro-damage self-repair rate index in the embodiment of the present invention is used to determine the curing time of the geopolymer improved soil.

[0056] In the figure, 1. Base, 2. Improved soil specimen, 3. Permeable stone, 4. Confining pressure chamber, 5. Loading device. DETAILED DESCRIPTION

[0057] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] A method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate, such as Figure 1 As shown, the following steps are included:

[0059] S1, prepare geopolymer, test the unconfined compressive strength of geopolymer material at curing times of 24h, 48h, 72h, 96h, 120h, 144h, and 168h. According to the data obtained from a large number of tests (a total of 7 different curing times, 3 parallel samples for each curing time, and the average of the 3 test results, a total of 21 groups of experiments), the data are shown in Table 1. The measured data of geopolymer are used. The data planning solution method is used to select the form with the highest fit to establish a prediction model for the unconfined compressive strength s of geopolymer with respect to curing time t:

[0060] s=a0 ln(t)+b0 (1)

[0061] Where: a0, b0 are constant coefficients, s is the unconfined compressive strength of geopolymer (MPa); t is the curing time (h);

[0062] At the same time, the logarithmic model conforms to the objective law of the change of unconfined compressive strength with time. The unconfined compressive strength increases rapidly at early strength, and the growth of the unconfined compressive strength tends to slow down with the passage of time.

[0063] Table 1 Measured and predicted unconfined compressive strength of geopolymers

[0064] Curing time t(h) Measured s(MPa) Prediction s (MPa) 24 8.2 6.7 48 15.2 16.3 72 20.9 22.0 96 25.1 26.0 120 28.3 29.1 144 32.8 31.7 168 35.1 33.8

[0065] Based on the experimental data in Table 1 (measured geopolymer s), the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model formula (1), thus obtaining formula (2):

[0066] s=13.942 ln(t)-37.630 (2)

[0067] The geopolymer prediction data in Table 1 are obtained according to formula (2).

[0068] Figure 2 The solid line in Table 1 is the measured data, and the dashed line is the geopolymer prediction data calculated by formula (2). The fitting accuracy R 2 It is 0.983.

[0069] S2, prepared improved soil specimens with different geopolymer content, tested and calculated the unconfined compressive strength (UCS) and elastic modulus (E) of the improved soil (geopolymer improved soil) at curing times of 24h, 48h, 72h, 96h, 120h, 144h, and 168h. Specific data are shown in Table 2 for the measured UCS of the improved soil and Table 3 for the measured E of the improved soil.

[0070] S3, saturate the improved soil sample with water and test its wet weight G w , and then dry it and test the dry weight G d Based on the difference between dry weight and wet weight, the volume porosity η (%) of the improved soil was calculated by formula (3):

[0071]

[0072] Where, γ w Indicates the density of water (g / cm 3 ), V s Indicates the total volume of the sample (cm 3 ), G w is the wet weight of the improved soil specimen after saturation (g), G d is the dry weight of the improved soil specimen after drying (g).

[0073] In S4, based on a large amount of test data (a total of 35 different working conditions, 3 parallel samples were made for each working condition, and the average of the 3 test results was taken, for a total of 105 groups of experiments), a neural network algorithm and a multivariate linear regression analysis method were used to directly output a prediction model that conforms to the rules of a large amount of test data. Finally, the prediction model for the unconfined compressive strength of geopolymer obtained in S1 was coupled (that is, the prediction model for the unconfined compressive strength of geopolymer is a part of the prediction model for the unconfined compressive strength of improved soil) to establish a prediction model for the unconfined compressive strength of improved soil with the curing time t, the volume porosity η of the improved soil, and the geopolymer content α as variables, thus obtaining formula (4):

[0074]

[0075] Where a0, b0, c1, d1, e1, and f1 are constant coefficients; UCS is the unconfined compressive strength of the improved soil (MPa); t is the curing time (h) (the improved soil is prepared by mixing the geopolymer with the soil sample immediately after preparation, so the curing time of the improved soil is the same as that of the geopolymer); α is the geopolymer dosage (%); η is the volume porosity of the improved soil (%).

[0076] Table 2 Measured and predicted unconfined compressive strength of geopolymer-improved soil

[0077]

[0078] Based on the test data in Table 2 (measured UCS of improved soil), the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model (4), thus obtaining formula (5):

[0079]

[0080] The predicted unconfined compressive strength of geopolymer-improved soil in Table 2 is obtained by formula (5).

[0081] Figure 3 The vertical coordinate of the improved soil prediction UCS is calculated by formula (5), and the fitting accuracy R 2 It is 0.953.

[0082] S5, based on a large amount of test data (a total of 35 different working conditions, 3 parallel specimens for each working condition, and the average of the 3 test results, a total of 105 groups of experiments), uses the neural network algorithm and the multivariate linear regression analysis method to output a prediction model that conforms to the rules of a large amount of test data. Then, coupled with the geopolymer unconfined compressive strength prediction model obtained in S1 (that is, the geopolymer unconfined compressive strength prediction model is a part of the improved soil elastic modulus prediction model), a modified soil elastic modulus prediction model is established with the curing time t, improved soil porosity η, and geopolymer content α as variables, that is, formula (6) is obtained:

[0083]

[0084] Where: a0, b0, c2, d2, e2, f2 are constant coefficients; E is the elastic modulus of the improved soil (MPa); t is the curing time (h); α is the geopolymer content (%); η is the volume porosity of the improved soil (%).

[0085] Since the unconfined compressive strength of geopolymer increases, the unconfined compressive strength and elastic modulus of geopolymer-improved soil also increase. The embodiment of the present invention couples the geopolymer unconfined compressive strength formula and uses coefficients c1 and c2 to respectively reflect the relationship between the unconfined compressive strength and elastic modulus of the improved soil and the geopolymer unconfined compressive strength prediction model.

[0086] Table 3 Elastic modulus test and prediction data of geopolymer-improved soil

[0087]

[0088]

[0089] Based on the test data in Table 3 (measured E of improved soil), the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model formula (6), thus obtaining formula (7):

[0090]

[0091] Figure 4 The vertical coordinate of the improved soil prediction E is calculated by formula (7), and the fitting accuracy R 2 It is 0.951.

[0092] In the embodiment, the soil sample is high liquid limit silt, and the improved soil is composed of high liquid limit silt and different amounts of geopolymer.

[0093] S6. A finite element mechanical simulation model of the improved soil roadbed considering the overlying base construction was established in ABAQUS finite element calculation software. The predicted elastic modulus (E) of the improved soil under different curing times was input. Based on the semi-rigid base construction method, the base deadweight load and the force acting on the improved soil roadbed due to mechanical compaction during base construction were input. The maximum vertical stress response σ1 and transverse confining compressive stress σ3 generated in the improved soil roadbed during the semi-rigid base construction were calculated using ABAQUS finite element calculation software.

[0094] S7, using the maximum vertical stress response σ1 and the transverse confining compressive stress σ3 as the pre-damage stress level, conducts a dynamic triaxial test of the improved soil with pre-damage. The duration and number of pre-damage tests are converted based on the semi-rigid base construction method. Specifically, the weight of the roller during base construction is used as the load size, the contact area between the roller drum and the construction surface is used as the load application area, the action duration is calculated based on the roller's travel speed and the roller drum's contact width, and the number of compactions is used as the load application number. This is equivalent to replacing the impact of on-site construction on the roadbed with an indoor pre-damage dynamic triaxial test. Then, the development of the unconfined compressive strength and elastic modulus of the improved soil after the pre-damage dynamic triaxial test is tested, which can reflect the development of the unconfined compressive strength and elastic modulus of the improved soil on site. This allows accurate and efficient guidance of on-site construction from indoor tests.

[0095] The semi-rigid base layer is made of cement-stabilized crushed stone with a 20 cm thick layer and a cement content of 6% (mass fraction) and a density of 2.4 g / cm 3 The base layer weight is converted into a uniformly distributed load and applied to the entire ABAQUS finite element calculation roadbed model. The base layer compaction process uses a 20-ton single steel wheel roller with a rolling speed of 2 km / h and 6 rolling passes. The contact area between the steel wheel and the roadbed is about 0.6m 2 (The range of steel wheel load). The compaction process parameters are converted into loads and applied to the ABAQUS finite element calculation roadbed model. The results are as follows: Figure 5-6 .

[0096] The finite element calculation results show that the maximum vertical stress σ1 generated by the roadbed is 4.307×10 4 Pa; the confining pressure σ3 is 2.635×10 4 Pa, which is used as the pre-added micro-damage stress level; according to the compaction process, the pre-added micro-damage load action time is 6 seconds, the loading interval is 60 seconds, and the action is 6 times, see Figure 7 ; The pre-loaded micro-damage dynamic triaxial test device includes a base 1, an improved soil specimen 2 is placed on the base 1, permeable stones 3 are provided at the upper and lower ends of the improved soil specimen 2, and a confining pressure chamber 4 is provided outside the improved soil specimen 2 for applying a lateral confining pressure stress σ3; a loading device 5 applies a maximum vertical stress response σ1 to the upper end of the improved soil specimen 2.

[0097] S8, after curing for 24h, 48h, 72h, 96h, 120h, 144h, and 168h, dynamic triaxial tests were carried out on the improved soil with pre-micro-damage, and then the soil was cured to the standard curing time (7d, i.e., 168h). The final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage were tested, as shown in Tables 4 and 5. Based on a large number of test results (three parallel samples were made for each curing time, and the average value of the three groups of test results was taken. There were 21 groups of experiments in Tables 4 and 5 respectively), the data planning solution method was used to select the form with the highest fitting degree, and a prediction model for the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage was constructed with the curing time before pre-micro-damage of the improved soil as the independent variable.

[0098] UCS′=a′ln(t′)+b′ (8)

[0099] Where a′ and b′ are constant coefficients; UCS′ is the final unconfined compressive strength of the improved soil after pre-microdamage (MPa); t′ is the curing time of the improved soil before pre-microdamage (h).

[0100] E′=c′ln(t′)+d′ (9)

[0101] Where c′ and d′ are constant coefficients; E′ is the final elastic modulus of the improved soil after pre-microdamage (MPa); t′ is the curing time of the improved soil before pre-microdamage (h).

[0102] Equation (8) is a prediction model for the final unconfined compressive strength of improved soil after microdamage at different curing times before microdamage. Equation (1) is a prediction model for the unconfined compressive strength of geopolymer. The only influencing factor in Equation (8) is the curing time before microdamage. Equation (1) is the curing time, which is essentially the curing time. Therefore, the form is consistent with Equation (1). The unconfined compressive strength and elastic modulus of improved soil both follow a logarithmic trend over time. Therefore, Equation (9) has the same form as Equation (8), both being logarithmic models.

[0103] Table 4 Unconfined compressive strength test and prediction data of pre-damaged improved soil

[0104]

[0105] Based on the experimental data (measured UCS′) in Table 4, the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model formula (8), thereby obtaining formula (10):

[0106] UCS′=0.941 ln(t′)-2.766 (10)

[0107] The predicted UCS′ data in Table 4 are obtained according to formula (10).

[0108] Figure 7 The solid line in Table 4 is the measured UCS′, and the dashed line is the predicted UCS′ calculated by formula (10). The fitting accuracy R 2 is 0.991.

[0109] Table 5 Elastic modulus test and prediction data of pre-damaged improved soil

[0110]

[0111] Based on the experimental data (measured E′) in Table 5, the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model formula (9), thereby obtaining formula (11):

[0112] E′=236.770 ln(t ′ )-410.810 (11)

[0113] The predicted E′ data in Table 5 are obtained according to formula (11).

[0114] Figure 8 The solid line in Table 5 is the measured E′, the dashed line is the predicted E′ calculated by formula (11), and the fitting accuracy R 2 It is 0.989.

[0115] S9, the index of micro-damage self-repair rate is determined according to the highway engineering grade (refer to the classification of highway grades in the Highway Roadbed Design Specification JTG30-2015)

[0116] Table 6 Requirements for micro-damage self-repair rate

[0117] Highway grade Micro-damage self-repair rate requirements Expressways and first-class highways 80％ Secondary highway 70％ Class III and IV highways 60％

[0118] In practical applications, when the improvement plan and base construction plan of the actual project are consistent, the self-repair rate remains unchanged. Based on the requirement that the micro-damage self-repair rate of unconfined compressive strength and elastic modulus simultaneously meet 80%, the final curing time of geopolymer-improved soil is determined to be 103 hours.

[0119] Specific calculation method: According to the requirement that the micro-damage self-repair rate meets 80%, such as Figure 9-10As shown in Figure 1, according to the parameters determined by the engineering improvement plan (geopolymer content α, volume porosity η of the improved soil), the standard curing time (t = 168h) is substituted into equations (5) and (7), respectively, to obtain the final unconfined compressive strength and elastic modulus of the improved soil without pre-addition of micro-damage. Multiplying by the self-repair rate of 80%, the unconfined compressive strength and elastic modulus of the improved soil after pre-addition of micro-damage that meet the self-repair rate requirements are obtained. Substituting them into equations (10) and (11), the curing time before pre-addition of micro-damage that meets the micro-damage self-repair rate requirements is obtained. The larger of the two curing times is taken as the final curing time of the improved soil.

[0120] The micro-damage self-repair rate introduced in the embodiment of the present invention can reflect the recovery effect of the geopolymer-improved soil on the impact of the base construction, and to a certain extent can highlight the characteristics of the performance development of the geopolymer-improved soil material over time.

[0121] The embodiment of the present invention determines the influence of base construction on the unconfined compressive strength and elastic modulus of the improved soil roadbed based on the pre-addition of micro-damage, establishes a prediction model, and ultimately determines the maintenance time based on the micro-damage self-repair rate. By replacing the impact of on-site construction on the roadbed with an indoor pre-addition of micro-damage test, and then testing the development of the unconfined compressive strength and elastic modulus of the improved soil after the pre-addition of micro-damage test, the development of the unconfined compressive strength and elastic modulus of the improved soil on-site can be reflected. While ensuring that the on-site construction is not affected, the indoor test conditions are closer to the actual construction conditions of the project, thereby improving the accuracy of the indoor test guidance for on-site construction.

[0122] The correlation coefficients R of all the models fitted in the method of the embodiment of the present invention are 2 Both values are above 0.95, indicating high model fitting accuracy and reflecting the accuracy of the method. Furthermore, the method of the present invention uses numerical simulation to calculate an equivalent response to the impact of on-site construction. Combined with indoor testing, this approach makes the indoor test conditions more closely aligned with actual construction conditions, significantly improving the accuracy of the method. The micro-damage self-repair rate is directly linked to engineering design requirements, making this indicator more scientific and reasonable.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate, characterized in that: The following steps are involved: Step 1: Determine the geopolymer content α and the volume porosity η of the improved soil according to the engineering improvement plan. Substitute the geopolymer content α, the volume porosity η of the improved soil, and the standard curing time into the improved soil unconfined compressive strength prediction model and the improved soil elastic modulus prediction model with curing time, volume porosity, and geopolymer content as variables, as shown in Equations (1) and (2), respectively, to obtain the final unconfined compressive strength and elastic modulus of the improved soil without pre-damaged micro-damage. Where: a0, b0, c1, d1, e1, f1, c2, d2, e2 and f2 are constant coefficients; E is the elastic modulus of the improved soil; UCS is the unconfined compressive strength of the improved soil; t is the curing time; α is the amount of geopolymer; η is the volume porosity of the improved soil; Step 2: The final unconfined compressive strength and elastic modulus of the modified soil without pre-addition of micro-damage are multiplied by the micro-damage self-repair rate to obtain the unconfined compressive strength and elastic modulus of the modified soil after pre-addition of micro-damage that meet the self-repair rate requirements; Step 3: Substitute the unconfined compressive strength and elastic modulus of the improved soil obtained in step 2 that meet the self-repair rate requirements after pre-micro-damage into the prediction model of the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage for the curing time before pre-micro-damage, as shown in equations (3) and (4): UCS′=a′ln(t′)+b′ (3) E′=c′ln(t′)+d′ (4) Where: a′, b′, c′ and d′ are constant coefficients; UCS′ is the final unconfined compressive strength of the improved soil after pre-addition of micro-damage; E′ is the final elastic modulus of the improved soil after pre-addition of micro-damage; t′ is the curing time before micro-damage of the improved soil; Thus, the curing time before micro-damage is added is obtained, and the larger of the two curing times is taken as the final curing time of the improved soil.

2. The method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate according to claim 1, characterized in that: In step 1, the method for determining the unconfined compressive strength prediction model and the elastic modulus prediction model of improved soil with curing time, volume porosity, and geopolymer content as variables includes the following steps: Step 11: Prepare geopolymer, obtain the unconfined compressive strength of geopolymer at different curing times through experiments, and based on the corresponding relationship between the unconfined compressive strength of geopolymer and curing time, select the form with the highest fit through data planning and solving method to establish a prediction model of the unconfined compressive strength of geopolymer with respect to curing time; Step 12: preparing modified soil specimens with different geopolymer content, and obtaining the volume porosity of the modified soil specimens and the unconfined compressive strength and elastic modulus of the modified soil specimens at different curing times through experiments; Step 13: Based on the influence of the curing time, volume porosity, and geopolymer content of the improved soil specimens on the unconfined compressive strength of the improved soil specimens, a prediction model that conforms to the influence rules is obtained by combining multiple linear regression with an artificial neural network algorithm. The prediction model of the unconfined compressive strength of the geopolymer obtained in step 11 is then coupled to establish a prediction model of the unconfined compressive strength of the improved soil with curing time, volume porosity, and geopolymer content as variables. Based on the influence of the curing time, volume porosity and geopolymer content of the improved soil specimens on the elastic modulus of the improved soil specimens, a prediction model that conforms to the said influence law is obtained by combining multiple linear regression with an artificial neural network algorithm. Then, the geopolymer unconfined compressive strength prediction model obtained in step 11 is coupled to establish a prediction model for the elastic modulus of improved soil with curing time, volume porosity and geopolymer content as variables.

3. The method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate according to claim 2, characterized in that: In step 11, the prediction model of the unconfined compressive strength of geopolymer with respect to curing time is: s=a0 ln(t)+b0 Where: a0 and b0 are constant coefficients, s is the unconfined compressive strength of geopolymer, and t is the curing time.

4. The method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate according to claim 3, characterized in that: In step 11, the method for determining the constant coefficient is: Based on the measured unconfined compressive strength of geopolymer, the coefficient with the minimum fitting error is obtained according to the least square method in the form of the prediction model, thus obtaining: s=13.942ln(t)-37.630 Where t is the curing time.

5. The method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate according to claim 1, characterized in that: In step 1, the method for determining the constant coefficient is as follows: based on the measured unconfined compressive strength of the improved soil, the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model formula (1), thereby obtaining: Based on the measured elastic modulus of improved soil, the coefficient with the minimum fitting error is obtained according to the least square method in the form of prediction model (2), thus obtaining: Where t is the curing time.

6. The method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate according to claim 1, characterized in that: In step 2, the micro-damage self-repair rate is determined according to the highway grade. The micro-damage self-repair rate of an expressway or a first-class highway is 80%; the micro-damage self-repair rate of a second-class highway is 70%; and the micro-damage self-repair rate of a third-class or fourth-class highway is 60%.

7. The method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate according to claim 1, characterized in that: In step 3, the method for determining the prediction model of the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage with respect to the curing time before pre-micro-damage includes the following steps: Step 31: Establish a finite element mechanical simulation model of the improved soil roadbed taking into account the construction of the overlying base in ABAQUS finite element calculation software. Input the predicted elastic modulus of the improved soil under different curing times. Based on the semi-rigid base construction method, input the base deadweight load and the force acting on the improved soil roadbed due to mechanical compaction during base construction. Use ABAQUS finite element calculation software to calculate the maximum vertical stress response σ1 and transverse confining compressive stress σ3 generated in the improved soil roadbed during the construction of the semi-rigid base. Step 32: Using the maximum vertical stress response σ1 and transverse confining pressure stress σ3 calculated in step 31 as the pre-micro-damage stress level, conduct a dynamic triaxial test of the improved soil with pre-micro-damage, and convert the pre-micro-damage duration and number according to the semi-rigid base construction method; conduct dynamic triaxial tests of the improved soil with pre-micro-damage after different curing times, and then continue curing to the standard curing time; obtain the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage under different curing times before pre-micro-damage through experiments; based on the corresponding relationship between the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage and the curing time before pre-micro-damage, select the form with the highest fitting degree through the data planning solution method, and establish prediction models of the final unconfined compressive strength and elastic modulus of the improved soil after pre-micro-damage with respect to the curing time before pre-micro-damage.

8. The method for determining the curing time of geopolymer-improved soil roadbed based on damage self-repair rate according to claim 1, characterized in that: In step 3, the constant coefficient is determined by: based on the measured unconfined compressive strength of the improved soil after micro-damage, the coefficient with the smallest fitting error is obtained according to the least squares method in the form of the prediction model formula (3), thereby obtaining: UCS′=0.941ln(t′)-2.766 Based on the measured elastic modulus of the improved soil after micro-damage, the coefficient with the minimum fitting error is obtained according to the least square method in the form of the prediction model (4), thus obtaining: E′=236.770ln(t ′ )-410.810 Where: t′ is the curing time before micro-damage of improved soil.

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