Method for predicting low-temperature interval creep compliance based on asphalt complex modulus

Abstract

The invention is applicable to the technical field of road engineering, and particularly relates to a method for predicting creep compliance in a low-temperature interval based on complex modulus of asphalt. Meanwhile, unclear physical significance conditions such as empirical point selection, unsmooth curve and negative time spectrum in the discrete delay time spectrum are avoided, the continuous delay time spectrum in the method is an analytical formula obtained through theoretical derivation instead of a numerical solution, numerical calculation errors are obviously reduced compared with related research at home and abroad, and the method has the advantages of being high in practicability and easy to popularize. And the accuracy of the low-temperature creep compliance prediction value is greatly improved. Besides, according to the interface creep compliance prediction model and the coefficient calibration module provided by the invention, a user does not need to manually carry out step-by-step calculation in practical application, the creep compliance value can be directly obtained only by importing complex modulus test data, and the model can be calibrated based on an existing database, so that the prediction accuracy is improved; compared with the prior art, the engineering practicability can be remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of road engineering, and in particular relates to a method for predicting creep compliance in a low temperature range based on asphalt complex modulus. Background Art

[0002] At present, the application of environmentally friendly road materials and structures has received more and more attention. Among them, recycled pavement and recycled asphalt are currently widely used road engineering technologies. Although recycled asphalt has significant environmental and ecological value, it is still difficult to comprehensively evaluate its performance in engineering. Since recycled asphalt needs to be extracted from the road that has been paved and used, this work is time-consuming and labor-intensive, and the yield of extracted asphalt is extremely low. At the same time, asphalt is a typical viscoelastic material, its performance is significantly affected by temperature and frequency, so it is usually necessary to comprehensively characterize its properties through tests under a variety of temperature and frequency combinations, and the performance of asphalt needs to be evaluated in both the time domain and the frequency domain. At present, the current foreign specifications detail the test method for the low-temperature bending creep compliance of asphalt, and introduce the test method for the complex modulus of asphalt in different temperature ranges. These two properties are necessary detection indicators for asphalt, but in actual engineering, the efficiency of obtaining the viscoelastic properties of recycled asphalt by using two test methods separately is low, and additional asphalt samples are required, which increases the difficulty of evaluating the service performance of recycled asphalt. At the same time, since asphalt is a multiphase material, its performance can change according to different material types, sources and aging degrees, so it is inefficient to obtain its mechanical properties only by relying on test methods.

[0003] In essence, the complex modulus and creep compliance of asphalt can be converted to each other according to the linear viscoelastic theory. Therefore, it is feasible and economical to predict the creep compliance of asphalt materials in different temperature ranges (including temperatures outside the test temperature range) based on the time-temperature equivalence principle and the complex modulus master curve. At present, some related studies at home and abroad have explored the relationship between the complex modulus and creep compliance of asphalt through simple data regression equations, but have failed to fully consider the viscoelastic properties of asphalt materials, resulting in the obtained law being highly sensitive to data changes. Some studies have also adopted equations related to viscoelastic theory, but there are obvious deficiencies in test condition verification, data quality control, and fitting accuracy. At the same time, the accuracy and simplicity of complex domain calculations have been ignored, so the calculation accuracy and efficiency need to be improved. In addition, even if the accuracy of the model can be further improved, it is impractical to require users to gradually complete the performance conversion calculation in practical applications. Therefore, there is an urgent need for an interfacial creep compliance prediction model that can embed physical relationships and be calibrated based on a large amount of existing data. Summary of the invention

[0004] The purpose of the present invention is to provide a method for predicting creep compliance in the low-temperature range based on the complex modulus of asphalt, aiming to solve the obvious deficiencies in the prior art in test condition verification, data quality control and fitting accuracy, while ignoring the accuracy and simplicity of complex domain calculations. It also specifically explains how to establish and calibrate an interface model for predicting creep compliance based on the physical relationship and existing databases, so that users only need to import complex modulus data to predict low-temperature creep compliance.

[0005] The present invention is implemented as follows: a method for predicting creep compliance in a low temperature range based on asphalt complex modulus, characterized in that the method comprises: Collect complex modulus test data of asphalt of different types and sources and creep compliance test data of asphalt in low temperature range to build a material database; Check the quality of complex modulus test data, calculate storage modulus and loss modulus based on complex modulus, and construct the relationship diagram between storage modulus and loss modulus and the relationship diagram between complex modulus amplitude and phase angle; The lowest temperature in the complex modulus test is used as the reference temperature to establish the complex modulus master curve. A lower temperature outside the test temperature range is selected to predict the reduction time corresponding to the creep compliance at this temperature. The complex compliance is calculated by the complex modulus, and the continuous delay time spectrum is used to convert the complex compliance into the creep compliance corresponding to the low temperature in the time domain. The predicted creep compliance is the x-axis data, and the measured creep compliance is the y-axis data. If the determination coefficient R of the two about y=x is 2 If it reaches above 0.8, no other operation is required. If the determination coefficient is less than 0.8, the creep compliance prediction model is further calibrated based on linear regression; The interface module of the creep compliance prediction model is constructed based on MATLAB, which includes four modules: data import module, parameter calibration module, performance evaluation module and data storage module. The data import module is used to read the complex modulus data provided by the user, the parameter calibration module is used to input and modify the calibration parameters of linear regression, the performance evaluation module substitutes the complex modulus input by the user into the prediction relationship obtained by the aforementioned database, and predicts the low-temperature creep compliance based on the calibration parameters. The data storage module is used to store the creep compliance prediction data, and the interface model guided by mathematical and physical relationships and verified by the measured database is used to achieve fast and efficient creep compliance prediction and accuracy calibration.

[0006] Preferably, the step of calculating the storage modulus and loss modulus based on the complex modulus, constructing a relationship diagram between the storage modulus and the loss modulus and a relationship diagram between the complex modulus amplitude and the phase angle, also includes checking whether there is any data discontinuity or abnormality. If so, the corresponding part of the test data is discarded.

[0007] Preferably, for the complex modulus test data, the temperature is not lower than 0°C and not higher than 55°C, the test frequency at a single temperature does not exceed 30 Hz, when the temperature is higher than 35°C, the strain level is set to 1%, and when the temperature is lower than 35°C, the strain level is set to 0.1%.

[0008] Preferably, the continuous delay time spectrum is calculated as follows: in, is the delay time, is the continuous delay time spectrum, and To calculate the variable, , , , , and is the fitting parameter.

[0009] Preferably, the expression of creep compliance is: in: is the asphalt creep compliance, is the glassy compliance.

[0010] Preferably, the user input data should be complex modulus test values, and the prediction model should automatically calculate the parameters and simultaneously display a graphical representation of the complex modulus test values ​​and the fitted values, as well as the predicted value of the low temperature creep compliance.

[0011] The method for predicting creep compliance in low temperature range based on asphalt complex modulus provided by the present invention does not simply rely on the prediction equations obtained by two experimental mathematical statistical regressions, and avoids the systematic prediction errors caused by data differences. At the same time, the present invention uses continuous delay time spectrum to theoretically meet the strict viscoelastic conversion conditions, and avoids the unclear physical meanings of empirical point selection, non-smooth curves, and negative time spectrum in discrete delay time spectrum. The continuous delay time spectrum in the present invention is an analytical formula derived from theory rather than a numerical solution, which significantly reduces the numerical calculation error compared with related studies at home and abroad, and greatly improves the accuracy of low temperature creep compliance prediction values. In addition, the interface creep compliance prediction model and coefficient calibration module proposed by the present invention enable users to directly obtain creep compliance values ​​by importing complex modulus test data without manual step-by-step calculations in actual applications. The model can be calibrated based on the existing database, thereby improving the prediction accuracy, and can significantly improve engineering practicality compared with the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flow chart of a method for predicting creep compliance in a low temperature range based on asphalt complex modulus provided in an embodiment of the present invention; Figure 2 A schematic diagram of the relationship between the storage modulus and the loss modulus provided in an embodiment of the present invention; Figure 3 A schematic diagram of the relationship between the phase angle and the complex modulus amplitude provided by an embodiment of the present invention; Figure 4 A schematic diagram of the relationship between the predicted stiffness modulus value and the measured stiffness modulus value provided in an embodiment of the present invention; Figure 5 A schematic diagram of a creep compliance interface prediction model based on asphalt complex modulus provided in an embodiment of the present invention; Figure 6 A schematic diagram of an interface for model coefficient calibration provided by an embodiment of the present invention; DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0014] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0015] like Figure 1 FIG. 1 is a flow chart of a method for predicting creep compliance in a low temperature range based on asphalt complex modulus provided by an embodiment of the present invention, the method comprising: A material database is constructed based on the complex modulus test data of asphalt of different types and sources and the creep compliance test data of asphalt in the low temperature range. In this embodiment, a total of 45 asphalt samples were selected, which came from 8 different regions. The high temperature grading index ranged from 58 to 76, and the low temperature grading index ranged from -28 to -10. Among them, 5 asphalt cylinder specimens were modified asphalt, and 3 asphalt cylinder specimens contained polyphosphoric acid. In terms of aging degree, 39 asphalts have long-term aging samples, and 8 of them have short-term aging samples. In addition, there are 6 emulsified asphalt residues. Due to certain differences in the performance of different asphalts, the complex modulus temperature-frequency test range of each asphalt sample is not completely consistent, but the overall temperature range is within 0ºC-54 ºC, the overall frequency is within 0.1Hz-30Hz, and the strain level is set to 1% when the temperature is higher than 35°C, and the strain level is set to 0.1% when the temperature is lower than 35°C.

[0016] Check the quality of complex modulus test data, calculate storage modulus and loss modulus based on complex modulus, construct the relationship diagram between storage modulus and loss modulus and the relationship diagram between complex modulus amplitude and phase angle, and check whether there are discontinuities or abnormalities in the data. Figure 2 and Figure 3 If abnormal data is shown, this part of the test data should be eliminated.

[0017] For the data checked in the previous step, the lowest temperature in the complex modulus test is used as the reference temperature to establish the complex modulus master curve. A lower temperature outside the test temperature range is selected, and the complex compliance is converted into the creep compliance corresponding to the low temperature in the time domain using the continuous delay time spectrum. The complex modulus measured in the frequency domain is converted into the creep compliance in the time domain using the continuous delay time spectrum shown in the following formula.

[0018]

[0019] in: is the delay time, is the continuous delay time spectrum, and To calculate the variable, , , , , and is the fitting parameter.

[0020] The expression of creep compliance is: in: is the asphalt creep compliance, is the glassy compliance.

[0021] The asphalt model parameters and prediction results are shown in Table 1. The predicted creep compliance is the x-axis data and the measured creep compliance is the y-axis data. The predicted creep compliance and measured creep compliance results of all asphalts are shown in Table 1. Figure 6 As shown, the determination coefficient R 2 It reaches 0.91, indicating that the method of the present invention has high accuracy in predicting the creep compliance in the low temperature range based on the asphalt complex modulus and does not require additional calibration.

[0022] Based on the physical relationship obtained from the above data, the interface module of the creep compliance prediction model is designed through MATLAB, which should include four modules: data import, parameter calibration, performance evaluation and data storage, such as Figure 5 The data import module is used to read the new asphalt complex modulus data provided by the user, and the parameter calibration module is used to input and modify the calibration parameters of the linear regression, such as Figure 6The performance evaluation module substitutes the complex modulus input by the user into the prediction relationship obtained by the aforementioned database, and predicts the low-temperature creep compliance based on the calibration parameters. The data storage module is used to store the creep compliance prediction data. In summary, the interface model guided by mathematical and physical relationships and verified by the measured database can achieve fast and efficient creep compliance prediction and accuracy calibration.

[0023] Table 1 Asphalt model parameters and prediction results

[0024] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0025] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0026] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0027] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0028] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting creep compliance in low temperature range based on asphalt complex modulus, characterized in that: The method comprises: Collect complex modulus test data of asphalt of different types and sources and creep compliance test data of asphalt in low temperature range to build a material database; Check the quality of complex modulus test data, calculate storage modulus and loss modulus based on complex modulus, and construct the relationship diagram between storage modulus and loss modulus and the relationship diagram between complex modulus amplitude and phase angle; The lowest temperature in the complex modulus test is used as the reference temperature to establish the complex modulus master curve. A lower temperature outside the test temperature range is selected to predict the reduction time corresponding to the creep compliance at this temperature. The complex compliance is calculated by the complex modulus, and the continuous delay time spectrum is used to convert the complex compliance into the creep compliance corresponding to the low temperature in the time domain. The predicted creep compliance is the x-axis data, and the measured creep compliance is the y-axis data. If the determination coefficient R of the two about y=x is 2 If it reaches above 0.8, no other operation is required. If the determination coefficient is less than 0.8, the creep compliance prediction model is further calibrated based on linear regression; The interface module of the creep compliance prediction model is constructed based on MATLAB, which includes four modules: data import module, parameter calibration module, performance evaluation module and data storage module. The data import module is used to read the complex modulus data provided by the user, the parameter calibration module is used to input and modify the calibration parameters of linear regression, the performance evaluation module substitutes the complex modulus input by the user into the prediction relationship obtained by the aforementioned database, and predicts the low-temperature creep compliance based on the calibration parameters. The data storage module is used to store the creep compliance prediction data, and the interface model guided by mathematical and physical relationships and verified by the measured database is used to achieve fast and efficient creep compliance prediction and accuracy calibration.

2. The method for predicting creep compliance in low temperature range based on asphalt complex modulus according to claim 1 is characterized in that: The steps of calculating the storage modulus and loss modulus based on the complex modulus, constructing a relationship diagram between the storage modulus and the loss modulus and a relationship diagram between the complex modulus amplitude and the phase angle, also include checking whether there are data discontinuities or anomalies. If so, the corresponding part of the test data is eliminated.

3. The method for predicting creep compliance in low temperature range based on asphalt complex modulus according to claim 2 is characterized in that: For the complex modulus test data, the temperature is not lower than 0°C and not higher than 55°C, the test frequency at a single temperature does not exceed 30Hz, and the strain level is set to 1% when the temperature is higher than 35°C and to 0.1% when the temperature is lower than 35°C.

4. The method for predicting creep compliance in low temperature range based on asphalt complex modulus according to claim 1 is characterized in that: The continuous delay time spectrum is calculated as: in, is the delay time, is the continuous delay time spectrum, and To calculate the variable, , , , , and is the fitting parameter.

5. The method for predicting creep compliance in low temperature range based on asphalt complex modulus according to claim 1 is characterized in that: The expression of creep compliance is: in: is the asphalt creep compliance, is the glassy compliance.

6. The method for predicting creep compliance in low temperature range based on asphalt complex modulus according to claim 1, characterized in that: The user input data should be the complex modulus test value, and the prediction model should automatically calculate the parameters and display a graphical representation of the complex modulus test value and the fitted value, as well as the predicted value of the low-temperature creep compliance.

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