Method, device and equipment for predicting complex shear modulus of mucilage and medium

Abstract

The invention relates to a rubber cement complex shear modulus prediction method, device, equipment and medium, and belongs to the technical field of road engineering.The method comprises the steps that a rubber cement complex shear modulus determinacy prediction model is obtained; performing error propagation analysis on the complex shear modulus certainty prediction model to obtain a complex shear modulus error model of the rubber cement; and predicting the complex shear modulus distribution condition of the rubber cement based on the complex shear modulus determinacy prediction model and the complex shear modulus error model. According to the method, the uncertainty of the rubber cement is fully considered, and the obtained complex shear modulus distribution condition of the rubber cement better conforms to the actual condition, so that a more effective prediction tool is provided for actual road engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering, and in particular to a method, device, equipment and medium for predicting the complex shear modulus of mortar. Background Art

[0002] With the rapid development of highway construction in my country, by the end of 2023, the total mileage of highways in China has reached 183,600 kilometers, of which asphalt pavement accounts for more than 95%. Asphalt mixture is the main material of asphalt pavement, and its performance directly affects the service life, durability and driving comfort of the pavement. As an important component of asphalt mixture, mortar plays a key role in filling the gaps between aggregates and bonding aggregates and fillers in the mixture. It can effectively control the void ratio of the mixture, and tightly bond aggregates and fillers together to form a whole, thereby improving the structural stability of the mixture.

[0003] The complex shear modulus is one of the key indicators for evaluating the viscoelastic properties of mortar, which reflects the viscoelastic behavior of mortar under different temperature and frequency conditions. However, due to the heterogeneity of mortar materials and the inevitable errors in laboratory tests, there is a certain uncertainty in the rheological properties of mortar, and the complex shear modulus of mortar fluctuates within a certain range. This uncertainty not only increases the difficulty of predicting the performance of asphalt mixtures, but may also affect the design and construction quality of pavement engineering.

[0004] In existing research, most scholars tend to make deterministic predictions on the complex shear modulus of the mortar, treating the complex shear modulus of the mortar as a constant and ignoring the uncertainty of the mortar, resulting in the prediction results being inconsistent with the actual situation and unable to truly reflect the rheological properties of the mortar. Summary of the invention

[0005] In view of this, it is necessary to provide a method, device, equipment and medium for predicting the complex shear modulus of mortar to solve the problem that the complex shear modulus of mortar predicted by the prior art cannot effectively reflect the actual situation.

[0006] In order to solve the above problems, in a first aspect, the present invention provides a method for predicting the complex shear modulus of mortar, comprising: Obtaining a deterministic prediction model for the complex shear modulus of the mortar; Performing error propagation analysis on the complex shear modulus deterministic prediction model to obtain a complex shear modulus error model of the mortar; Based on the complex shear modulus deterministic prediction model and the complex shear modulus error model, the complex shear modulus distribution of the mortar is predicted.

[0007] Optionally, the mortar includes asphalt and mineral powder; the complex shear modulus error model is a complex shear modulus error model when the volume fraction of the mineral powder in the mortar is the target volume fraction of the mineral powder, and the complex shear modulus deterministic prediction model is expressed by the following formula:

[0008] in, represents the complex shear modulus of the glue, represents the complex shear modulus of asphalt, represents the Poisson's ratio of asphalt, Indicates the volume fraction of mineral powder; , represents the shear modulus of mineral powder, , , , , represents the bulk modulus of asphalt, represents the bulk modulus of mineral powder; The complex shear modulus error model is expressed by the following formula:

[0009] in, represents the standard deviation of the complex shear modulus of the glue, express For variables The partial derivative of Represents the standard deviation of the complex shear modulus of asphalt.

[0010] Optionally, the predicting the complex shear modulus distribution of the mortar based on the complex shear modulus deterministic prediction model and the complex shear modulus error model includes: Obtaining a complex shear modulus distribution model of the asphalt at different reduction frequencies; Predicting, according to the complex shear modulus distribution model, a first complex shear modulus mean and a first complex shear modulus standard deviation of the asphalt at different reduction frequencies; Determining a second complex shear modulus mean value of the mortar at different reduction frequencies according to the first complex shear modulus mean value and a complex shear modulus deterministic prediction model of the mortar; Determining a second complex shear modulus standard deviation of the mortar at different reduction frequencies according to the first complex shear modulus standard deviation and a complex shear modulus error model of the mortar; According to the second complex shear modulus mean and the second complex shear modulus standard deviation, the complex shear modulus distribution range of the mortar at different reduction frequencies is determined.

[0011] Optionally, the obtaining of the complex shear modulus distribution model of the asphalt at different reduction frequencies includes: Obtaining complex shear modulus test data and an initial complex shear modulus distribution model of the asphalt at different reduction frequencies; Determining model parameters of the initial complex shear modulus distribution model based on the test data and the maximum likelihood estimation method; Based on the model parameters and the initial complex shear modulus distribution model, the complex shear modulus distribution model of the asphalt is determined.

[0012] Optionally, the initial complex shear modulus distribution model is a normal distribution model.

[0013] Optionally, determining the complex shear modulus distribution interval of the mortar at different reduction frequencies according to the second complex shear modulus mean and the second complex shear modulus standard deviation includes: According to the second complex shear modulus mean and the second complex shear modulus standard deviation, a distribution interval of the complex shear modulus of the mortar when the complex shear modulus of the mortar meets a preset confidence requirement at different reduction frequencies is determined.

[0014] Optionally, the method further includes: Obtaining the upper limit and lower limit of the complex shear modulus distribution interval of the mortar corresponding to different target mineral powder volume fractions at the target reduction frequency; Fitting the upper limit value and the lower limit value to obtain a first mapping relationship between the mineral powder volume fraction and the upper limit value and a second mapping relationship between the mineral powder volume fraction and the lower limit value at a target reduction frequency; Based on the first mapping relationship and the second mapping relationship, the complex shear modulus distribution of the mortar is predicted.

[0015] In a second aspect, the present invention further provides a device for predicting complex shear modulus of mortar, comprising: A first model acquisition module is used to obtain a complex shear modulus deterministic prediction model of the mortar; A second model acquisition module is used to perform error propagation analysis on the complex shear modulus deterministic prediction model to obtain a complex shear modulus error model of the mortar; A prediction module is used to predict the complex shear modulus distribution of the mortar based on the complex shear modulus deterministic prediction model and the complex shear modulus error model.

[0016] In a third aspect, the present invention also provides an electronic device comprising a memory and a processor, wherein the memory is used to store programs; and the processor is coupled to the memory and is used to execute the programs stored in the memory to implement the steps in any one of the above-mentioned methods for predicting the complex shear modulus of mortar.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium for storing a computer-readable program, wherein the program or instruction, when executed by a processor, can implement the steps in any one of the above-mentioned methods for predicting the complex shear modulus of the mortar.

[0018] The beneficial effects of the present invention are: After obtaining the complex shear modulus deterministic prediction model of the mortar, the present invention performs error propagation analysis on the complex shear modulus deterministic prediction model of the mortar based on the error propagation theory to obtain the complex shear modulus error model of the mortar; then based on the complex shear modulus deterministic prediction model of the mortar and the complex shear modulus error model, the complex shear modulus distribution of the mortar can be predicted. The present invention fully considers the uncertainty of the mortar, and the obtained complex shear modulus distribution of the mortar is more in line with the actual situation, thereby providing a more effective prediction tool for actual road engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of an embodiment of a method for predicting complex shear modulus of mortar provided by the present invention; Figure 2 A diagram of the coefficient of variation of the complex shear modulus of asphalt at different reduction frequencies provided by the present invention; Figure 3 A coefficient of variation diagram of the complex shear modulus of the mortar at different mineral powder volume fractions provided by the present invention; Figure 4 A confidence distribution interval diagram of complex shear modulus of mortar at different mineral powder volume fractions provided by the present invention; Figure 5 A diagram of upper and lower limits of the complex shear modulus distribution interval of the mortar at different reduction frequencies provided by the present invention; Figure 6 A complex shear modulus prediction diagram of mortar at a volume fraction of 31.5% of mineral powder provided by the present invention; Figure 7 A complex shear modulus distribution diagram of asphalt provided by the present invention; Figure 8 A reliability curve comparison diagram provided by the present invention; Fig. 9 A diagram of estimated values ​​of complex shear modulus parameters of asphalt at different reduction frequencies provided by the present invention; Fig.10 A schematic structural diagram of an embodiment of a device for predicting complex shear modulus of mortar provided by the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0021] In the description of the embodiments of the present invention, unless otherwise specified, the meaning of "plurality" is two or more. The "first", "second", etc. involved in the embodiments of the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence, nor are they used to indicate or imply their relative importance or implicitly indicate the number of technical features indicated. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] Reference Figure 1 , showing a schematic flow chart of an embodiment of a method for predicting the complex shear modulus of mortar provided by the present invention, the method comprising: S101, obtaining a deterministic prediction model for the complex shear modulus of the mortar.

[0024] S102, performing error propagation analysis on the complex shear modulus deterministic prediction model to obtain a complex shear modulus error model of the mortar.

[0025] S103, predicting the complex shear modulus distribution of the mortar based on the complex shear modulus deterministic prediction model and the complex shear modulus error model.

[0026] A deterministic prediction model is a model based on mathematical and physical principles that is used to predict the deterministic output of a system under given input conditions. Unlike probabilistic models, deterministic models do not consider randomness or uncertainty, but directly calculate the output results through mathematical equations or algorithms based on known inputs and system characteristics.

[0027] The complex shear modulus deterministic prediction model can be the JC model (Johnson-Cook model), which is a deterministic mechanical prediction model. Compared with other models, it takes into account the interaction between particles. Existing relevant literature shows that this model has a good effect in the deterministic prediction of the viscoelastic properties of the mortar. The mortar includes asphalt and mineral powder. The complex shear modulus deterministic prediction model of the mortar can be expressed by the following formula: (1) In the formula, represents the complex shear modulus of the glue (kPa), represents the complex shear modulus of asphalt (kPa), The Poisson's ratio of asphalt is 0.49. Indicates the volume fraction of mineral powder; , represents the shear modulus of mineral powder, , , , , represents the bulk modulus of asphalt (kPa), represents the bulk modulus of mineral powder (kPa), , , Represents the Poisson's ratio of the mineral powder, which is taken as 0.35.

[0028] Error propagation theory provides a method to analyze how the uncertainty of input variables affects the output variables. Specifically, in the function In the error propagation of is a set of independent variables, the error of the function It can be obtained by the following formula: (2) Where: express For variables The partial derivative of Representation variables of error.

[0029] Based on the error propagation theory, the complex shear modulus error model of the glue can be obtained: (3) Where: represents the error of the complex shear modulus of the glue, that is, the standard deviation, express For variables The partial derivative of represents the error of the complex shear modulus of asphalt, that is, the standard deviation, It represents the Poisson's ratio of mineral powder, which is taken as 0.35. The other symbols can be deduced by referring to the meaning of the symbols in the above formula.

[0030] (4) Where: It represents the mean value of the complex shear modulus of the glue. The meanings of other symbols in this formula can be referred to The meaning of the symbols can be inferred by analogy.

[0031] Based on the deterministic prediction model of the complex shear modulus of the mortar, the mean of the complex shear modulus of the mortar can be calculated; based on the complex shear modulus error model of the mortar, the standard deviation of the complex shear modulus of the mortar can be calculated; based on the mean and standard deviation, the distribution of the complex shear modulus of the mortar can be obtained.

[0032] After obtaining the complex shear modulus deterministic prediction model of the mortar, this embodiment performs error propagation analysis on the complex shear modulus deterministic prediction model of the mortar based on the error propagation theory to obtain the complex shear modulus error model of the mortar; then based on the complex shear modulus deterministic prediction model of the mortar and the complex shear modulus error model, the complex shear modulus distribution of the mortar can be predicted. The present invention fully considers the uncertainty of the mortar, and the complex shear modulus distribution of the mortar obtained is more in line with the actual situation, thereby providing a more effective prediction tool for actual road engineering, which is helpful to optimize the design and performance improvement of mortar materials.

[0033] In one embodiment, the present invention selects one of the 70# base asphalt (GS) for research, performs a temperature frequency scanning experiment on the 70# base asphalt to obtain the complex shear modulus master curve of the 70# base asphalt, and calculates the coefficient of variation at different reduction frequencies based on the master curve. Figure 2, shows a graph of the coefficient of variation of the complex shear modulus of asphalt at different reduction frequencies provided by the present invention. It can be observed from the figure that the coefficient of variation of the complex shear modulus of asphalt shows a trend of first decreasing and then increasing with the increase of the reduction frequency. In the lower reduction frequency range, the coefficient of variation gradually decreases, indicating that the stability of the complex shear modulus of asphalt is improved at this time. When the reduction frequency is further increased to 1Hz, the coefficient of variation reaches the minimum value, indicating that the variability of the asphalt shear modulus is most stable at this reduction frequency. Subsequently, as the reduction frequency increases, the coefficient of variation begins to increase again, indicating that under higher reduction frequency conditions, the variability of the complex shear modulus of asphalt rises again.

[0034] In order to study the source of mortar variability, the present invention prepared 4 mortars with different mineral powder volume fractions, namely 15%, 25%, 35% and 40% mineral powder volume fractions, and carried out 8 groups of parallel tests on each mortar to obtain the master curve of complex shear modulus of mortars with different mineral powder volume fractions. At the same time, the coefficient of variation at different reduction frequencies was calculated according to the master curve. The results are shown in Table 1 below, and a schematic diagram of the coefficient of variation is drawn, as shown in Table 1 below. Figure 3 shown.

[0035] Table 1 Variation coefficient of complex shear modulus of mortar at different reduction frequencies and mineral powder volume fractions

[0036] From the horizontal comparison in Table 1, it can be clearly observed that the volume fraction of mineral powder has little effect on the variability of the mortar. Figure 3 This was further verified in Figure 3 It is shown that the coefficient of variation of the mortar decreases first and then increases with the increase of the reduction frequency. Specifically, when the reduction frequency reaches 1 Hz, the coefficient of variation of the mortar is the smallest. This phenomenon is similar to the characteristics of asphalt, indicating that the variability of the mortar is mainly influenced by the dominant influence of asphalt properties. Based on the above results, this study believes that asphalt is the main factor causing the variability of the mortar, that is, the error of the mortar mainly comes from asphalt, and has little to do with the volume fraction of the mineral powder.

[0037] Therefore, this embodiment further studies the complex shear modulus error model of the mortar under a specific mineral powder volume fraction. When the mineral powder volume fraction in the mortar is a specific mineral powder volume fraction (target mineral powder volume fraction), the complex shear modulus error model of the mortar is expressed by the following formula: (5) (6) In one embodiment, when the volume fraction of mineral powder in the mortar is a specific volume fraction of mineral powder, S103 may specifically include: obtaining a complex shear modulus distribution model of asphalt at different reduction frequencies; predicting the first complex shear modulus mean and the first complex shear modulus standard deviation of the asphalt at different reduction frequencies according to the complex shear modulus distribution model; determining the second complex shear modulus mean of the mortar at different reduction frequencies according to the first complex shear modulus mean and the complex shear modulus deterministic prediction model of the mortar; determining the second complex shear modulus standard deviation of the mortar at different reduction frequencies according to the first complex shear modulus standard deviation and the complex shear modulus error model of the mortar; determining the complex shear modulus distribution range of the mortar at different reduction frequencies according to the second complex shear modulus mean and the second complex shear modulus standard deviation.

[0038] Get the mean of the first complex shear modulus at different reduction frequencies Then, the first complex shear modulus average Substituting into formula (4), we can calculate the mean value of the second complex shear modulus of the mortar at different reduction frequencies: . The standard deviation of the first complex shear modulus at different reduction frequencies is obtained Then, the first complex shear modulus standard deviation Substituting into the above formula (5), the standard deviation of the second complex shear modulus of the mortar at different reduction frequencies can be calculated: .based on and , the distribution range of the complex shear modulus of the mortar at different reduction frequencies can be determined.

[0039] In one embodiment, the step of determining the complex shear modulus distribution interval of the mortar at different reduction frequencies may include: determining the complex shear modulus distribution interval of the mortar when the complex shear modulus of the mortar at different reduction frequencies meets the preset confidence requirement. The preset confidence requirement may be a confidence level greater than or equal to 95%. At this time, the complex shear modulus distribution interval of the mortar may also be a complex shear modulus confidence distribution interval of the mortar.

[0040] Reference Figure 4 , showing a confidence distribution interval diagram of the complex shear modulus of the mortar at different mineral powder volume fractions provided by the present invention. Figure 4 (a): Mineral powder volume fraction 15%, PICP = 89.19%; Figure 4 (b): Mineral powder volume fraction 25%, PICP = 92.44%; Figure 4 (c): Mineral powder volume fraction 35%, PICP = 93.82%; Figure 4 (d): Mineral powder volume fraction 40%, PICP = 88.26%. Among them, PICP (prediction interval coverage probability) indicates the prediction interval coverage rate. It represents the overlapping area of ​​the predicted confidence distribution interval of the complex shear modulus of the mortar and the main curve of the complex shear modulus of the mortar obtained by actual test. Represents the predicted confidence distribution interval of the complex shear modulus of the glue.

[0041] contrast Compared with the main curve, it can be found that there is a good consistency between the two, and the PICP of the predicted results is high. Among them, the PICP of the mortar with a mineral powder volume fraction of 35% is 93.82%, which is the highest PICP. PICP shows a trend of increasing first and then decreasing. The reason for this phenomenon is related to the JC model itself. The model has a good prediction effect on the complex shear modulus of the mortar with medium and low mineral powder volume fractions. The model takes into account the interaction between particles. At lower mineral powder volume fractions, the interaction between particles is not significant, that is, when the mineral powder volume fraction is 15%, due to the small amount of mineral powder, the interaction between particles is not obvious. When the mineral powder volume fraction is higher, the mineral powder is in closer contact with each other in the entire dispersion system of the mortar, and the interaction between particles is more obvious. Therefore, the prediction effect is better in the area with relatively high volume fractions.

[0042] This result shows that the method based on the JC model combined with the error propagation analysis can effectively predict the random characteristics of the asphalt-mortar system, and its prediction effect is quite significant. Therefore, we conclude that the error propagation analysis method has shown its effectiveness in the error analysis of the JC model and provides a reliable means for predicting the performance of asphalt-mortar.

[0043] In one embodiment, the method for predicting the complex shear modulus of mortar also includes: obtaining the upper limit and lower limit of the complex shear modulus distribution range of the mortar corresponding to different target mineral powder volume fractions at the target reduction frequency; fitting the upper limit and lower limit to obtain a first mapping relationship between the mineral powder volume fraction and the upper limit and a second mapping relationship between the mineral powder volume fraction and the lower limit at the target reduction frequency; based on the first mapping relationship and the second mapping relationship, predicting the complex shear modulus distribution of the mortar.

[0044] For example, you can Figure 4The prediction results in are used to determine the confidence distribution interval of the complex shear modulus of the mortar when the reduction frequency is 10000 Hz and the mineral powder volume fraction is 15%. Then, the upper and lower limits of the interval are determined from the distribution interval. The upper and lower limits of the complex shear modulus distribution interval of the mortar corresponding to other mineral powder volume fractions are determined in a similar manner. After obtaining the upper and lower limits corresponding to multiple groups of mineral powder volume fractions, the upper and lower limits can be fitted respectively to obtain the first mapping relationship between the mineral powder volume fraction and the upper limit, and the second mapping relationship between the mineral powder volume fraction and the lower limit. Based on the first mapping relationship and the second mapping relationship, the upper and lower limits corresponding to any mineral powder volume fraction when the reduction frequency is 10000 Hz can be predicted.

[0045] Based on the previous embodiment, the confidence distribution interval of the complex shear modulus of the mortar corresponding to any reduction frequency under a specific mineral powder volume fraction can be predicted. Based on this embodiment, the confidence distribution interval of the complex shear modulus of the mortar corresponding to any mineral powder volume fraction under a specific reduction frequency can be predicted. Furthermore, a first mapping relationship between the mineral powder volume fraction and the upper limit value and a second mapping relationship between the mineral powder volume fraction and the lower limit value under multiple different reduction frequencies can also be obtained. Therefore, based on the first mapping relationship and the second mapping relationship under the multiple different reduction frequencies, the confidence distribution interval of the complex shear modulus of the mortar corresponding to any mineral powder volume fraction under any reduction frequency is predicted.

[0046] Reference Figure 5 , showing the upper and lower limits of the complex shear modulus distribution range of the mortar at different reduction frequencies provided by the present invention. Figure 5 (a): Reduced frequency 10000 Hz; Figure 5 (b): Reduced frequency 5000 Hz; Figure 5 (c): Reduced frequency 1000 Hz; Figure 5 (d): Reduce the frequency to 1 Hz. Figure 5 The upper and lower limit formulas of the complex shear modulus of the mortar fitted at different reduction frequencies in can be further used to calculate the distribution interval of the complex shear modulus of the mortar at a 95% confidence level at any volume fraction. Specifically, by applying these fitting formulas to the corresponding volume fractions, we can determine the confidence interval of the complex shear modulus of the mortar within the range of the random variable under consideration. This calculation process provides a quantitative basis for predicting the randomness of the complex shear modulus of the mortar, thereby making the predicted value of the complex shear modulus of the mortar more consistent with its actual situation.

[0047] Reference Figure 6, shows a complex shear modulus prediction diagram of a mortar at a 31.5% mineral powder volume fraction provided by the present invention. The 31.5% mineral powder volume fraction is calculated based on the optimal mix ratio of 70# matrix asphalt (GS) and limestone mineral powder. To ensure the reliability of the prediction results, eight groups of parallel tests were conducted on the 31.5% volume fraction mortar.

[0048] It can be clearly observed from the graphical results that there is a good consistency between the predicted interval values ​​calculated based on the above-mentioned interval upper and lower limit fitting formulas and the measured values. PICP=94.87%, indicating that most of the predicted value interval falls within the measured value, and the predicted interval has high reliability and good prediction effect. This shows that the fitting formula used can effectively realize the randomness prediction of asphalt mortar at different volume fractions. Further analysis shows that the prediction method performs better in practical applications and can accurately predict the interval range of the mortar shear modulus. This result verifies the effectiveness of the fitting formula and provides a reliable tool for the performance prediction of asphalt mortar, demonstrating its wide applicability and high accuracy at various volume fractions.

[0049] In one embodiment, the step of obtaining the complex shear modulus distribution model of asphalt at different reduction frequencies may include: obtaining the complex shear modulus test data and the initial complex shear modulus distribution model of asphalt at different reduction frequencies; wherein the initial complex shear modulus distribution model is a normal distribution model; determining the model parameters of the initial complex shear modulus distribution model based on the test data and the maximum likelihood estimation method; determining the complex shear modulus distribution model of asphalt based on the model parameters and the initial complex shear modulus distribution model.

[0050] The present invention conducts temperature frequency sweep tests on four types of 70# matrix asphalts, and conducts 10 sets of parallel tests on each type of asphalt, obtaining a total of 40 complex shear modulus master curves of asphalt. Taking the complex shear modulus of asphalt at 30°C and 10 Hz as an example, the distribution characteristics of the complex shear modulus of asphalt are verified. Figure 7 , shows a complex shear modulus distribution diagram of an asphalt provided by the present invention. Figure 7 It can be seen that the complex shear modulus of asphalt obeys the normal distribution.

[0051] SW is used to test whether the distribution characteristics of the complex shear modulus of asphalt satisfy the normal distribution. The test results are shown in Table 2. It can be concluded from Table 2 that the P value is greater than 0.05, so it can be considered that the hypothesis is established and the distribution of the complex shear modulus of asphalt satisfies the characteristics of the normal distribution.

[0052] Table 2 Normal distribution parameters of complex shear modulus of asphalt

[0053] Since the sample size involved in this study is small, it is necessary to select an appropriate parameter estimation method to make reasonable parameter estimation with a small sample size. There are many methods for normal distribution parameter estimation. This study selects three commonly used normal distribution parameter estimation methods, namely maximum likelihood estimation, Bayesian estimation, and logarithmic family distribution fitting. In order to verify the effectiveness and accuracy of the above three parameter estimation methods, the reliability difference function and log-likelihood function are used as evaluation indicators for selection. The smaller the reliability difference value and the larger the log-likelihood function value, the more accurate and effective the method is.

[0054] The reliability difference function is as follows: (7) Where: represents the reliability estimate, represents the true value of reliability, n Indicates the sample size.

[0055] The log-likelihood function is shown below: (8) Where: represents the sample value, represents the sample mean, Represents the sample standard deviation.

[0056] Generate normal distribution location parameter using python , scale parameter , The three methods mentioned above are used to estimate the parameters of the samples, and the results are shown in Table 3 below.

[0057] Table 3 Normal distribution parameter estimates

[0058] It can be seen from Table 3 that the reliability difference value obtained by the maximum likelihood estimation method is the smallest, the log-likelihood function value is the largest, and the parameter estimate is closest to the given value. Therefore, it can be considered that the maximum likelihood estimation method has good accuracy and reliability in parameter estimation of the normal distribution.

[0059] Reference Figure 8 , showing a reliability curve comparison diagram provided by the present invention. Figure 8 The reliability curves of parameter estimates obtained by different parameter estimation methods are compared with the standard reliability curves. Figure 2 From the local enlarged figure in , we can see that the reliability curve obtained by the maximum likelihood estimation method is closer to the standard reliability curve and has a smaller error, which further illustrates that the maximum likelihood estimation method has higher accuracy in the normal distribution.

[0060] The shear modulus data of 70# matrix asphalt (GS) in this study was used to verify the reliability of the estimated actual test data. The accuracy of the parameter estimation method was judged by the log-likelihood function value. The data obtained by the three parameter estimation methods are shown in Table 4 below.

[0061] Table 4 Estimation of normal distribution parameters of complex shear modulus of asphalt

[0062] It can be concluded from Table 4 that the maximum likelihood estimation method has the largest log-likelihood function value compared with the other two methods, which shows that the maximum likelihood estimation method has good accuracy in parameter estimation of asphalt shear modulus.

[0063] Reference Fig. 9 , showing a diagram of estimated values ​​of complex shear modulus parameters of asphalt at different reduction frequencies provided by the present invention. After obtaining the complex shear modulus test data of asphalt at different reduction frequencies, the maximum likelihood estimation method is used to estimate the distribution of the complex shear modulus of asphalt at each reduction frequency, and the mean and standard deviation of the complex shear modulus of asphalt at each reduction frequency can be obtained. Afterwards, based on the mean and standard deviation of the complex shear modulus of asphalt at each reduction frequency, the mean and standard deviation of the complex shear modulus of the mortar at each reduction frequency can be calculated. Furthermore, the confidence distribution interval of the complex shear modulus of the mortar is calculated.

[0064] Reference Fig.10 , showing a schematic structural diagram of an embodiment of a device for predicting complex shear modulus of glue provided by the present invention, the device 10 comprises: The first model acquisition module 101 is used to obtain a complex shear modulus deterministic prediction model of the mortar; The second model acquisition module 102 is used to perform error propagation analysis on the complex shear modulus deterministic prediction model to obtain a complex shear modulus error model of the mortar; The prediction module 103 is used to predict the complex shear modulus distribution of the mortar based on the complex shear modulus deterministic prediction model and the complex shear modulus error model.

[0065] It should be noted that the implementation principle or implementation process of the above modules can refer to the above-mentioned embodiment of the method for predicting the complex shear modulus of the mortar, and will not be described one by one here.

[0066] In one embodiment, the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of any one of the above-mentioned methods for predicting the complex shear modulus of mortar are implemented.

[0067] In one embodiment, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by the processor, the steps of any one of the above-mentioned methods for predicting the complex shear modulus of mortar are implemented.

[0068] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0069] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting complex shear modulus of mortar, characterized in that: include: Obtaining a deterministic prediction model for the complex shear modulus of the glue; Performing error propagation analysis on the complex shear modulus deterministic prediction model to obtain a complex shear modulus error model of the mortar; Based on the complex shear modulus deterministic prediction model and the complex shear modulus error model, the complex shear modulus distribution of the mortar is predicted.

2. The method for predicting the complex shear modulus of mortar according to claim 1, characterized in that: The mortar includes asphalt and mineral powder; the complex shear modulus error model is a complex shear modulus error model when the volume fraction of the mineral powder in the mortar is the target volume fraction of the mineral powder, and the complex shear modulus deterministic prediction model is expressed by the following formula: in, represents the complex shear modulus of the glue, represents the complex shear modulus of asphalt, represents the Poisson's ratio of asphalt, Indicates the volume fraction of mineral powder; , represents the shear modulus of mineral powder, , , , , represents the bulk modulus of asphalt, represents the bulk modulus of mineral powder; The complex shear modulus error model is expressed by the following formula: in, represents the standard deviation of the complex shear modulus of the glue, express For variables The partial derivative of Represents the standard deviation of the complex shear modulus of asphalt.

3. The method for predicting the complex shear modulus of mortar according to claim 2, characterized in that: The method of predicting the complex shear modulus distribution of the mortar based on the complex shear modulus deterministic prediction model and the complex shear modulus error model includes: Obtaining a complex shear modulus distribution model of the asphalt at different reduction frequencies; According to the complex shear modulus distribution model, predicting the first complex shear modulus mean and the first complex shear modulus standard deviation of the asphalt at different reduction frequencies; Determining a second complex shear modulus mean value of the mortar at different reduction frequencies according to the first complex shear modulus mean value and a complex shear modulus deterministic prediction model of the mortar; Determining a second complex shear modulus standard deviation of the mortar at different reduction frequencies according to the first complex shear modulus standard deviation and a complex shear modulus error model of the mortar; According to the second complex shear modulus mean and the second complex shear modulus standard deviation, the complex shear modulus distribution range of the mortar at different reduction frequencies is determined.

4. The method for predicting the complex shear modulus of mortar according to claim 3, characterized in that: The obtaining of the complex shear modulus distribution model of the asphalt at different reduction frequencies includes: Obtaining complex shear modulus test data and an initial complex shear modulus distribution model of the asphalt at different reduction frequencies; Determining model parameters of the initial complex shear modulus distribution model based on the test data and the maximum likelihood estimation method; Based on the model parameters and the initial complex shear modulus distribution model, the complex shear modulus distribution model of the asphalt is determined.

5. The method for predicting the complex shear modulus of mortar according to claim 4, characterized in that: The initial complex shear modulus distribution model is a normal distribution model.

6. The method for predicting the complex shear modulus of mortar according to claim 3, characterized in that: Determining the complex shear modulus distribution interval of the mortar at different reduction frequencies according to the second complex shear modulus mean and the second complex shear modulus standard deviation includes: According to the second complex shear modulus mean and the second complex shear modulus standard deviation, a distribution interval of the complex shear modulus of the mortar when the complex shear modulus of the mortar meets a preset confidence requirement at different reduction frequencies is determined.

7. The method for predicting the complex shear modulus of mortar according to claim 3, characterized in that: The method further comprises: Obtaining the upper limit and lower limit of the complex shear modulus distribution interval of the mortar corresponding to different target mineral powder volume fractions at the target reduction frequency; Fitting the upper limit value and the lower limit value to obtain a first mapping relationship between the mineral powder volume fraction and the upper limit value and a second mapping relationship between the mineral powder volume fraction and the lower limit value at a target reduction frequency; Based on the first mapping relationship and the second mapping relationship, the complex shear modulus distribution of the mortar is predicted.

8. A device for predicting complex shear modulus of mortar, characterized in that: include: A first model acquisition module is used to obtain a complex shear modulus deterministic prediction model of the mortar; A second model acquisition module is used to perform error propagation analysis on the complex shear modulus deterministic prediction model to obtain a complex shear modulus error model of the mortar; A prediction module is used to predict the complex shear modulus distribution of the mortar based on the complex shear modulus deterministic prediction model and the complex shear modulus error model.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the method for predicting the complex shear modulus of the mortar as described in any one of claims 1 to 7 above.

10. A computer-readable storage medium, characterized in that: Used to store computer-readable programs, which, when executed by a processor, can implement the steps of the method for predicting the complex shear modulus of the mortar as described in any one of claims 1 to 7.

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