A Fast and High-Precision Identification Method for Asphalt Feature Functional Groups

Abstract

This invention discloses a rapid and high-precision identification method for asphalt characteristic functional groups, comprising the following steps: Step S1, conducting infrared spectroscopy experiments on the target asphalt binder; Step S2, constructing the target covariance matrix; Step S3, decomposing and sorting the eigenvalues ​​of the target covariance matrix; Step S4, constructing the orthogonal basis vector A of the target asphalt binder; Step S5, conducting infrared spectroscopy experiments on the reference asphalt binder; Step S6, constructing the reference covariance matrix; Step S7, constructing the reference orthogonal basis vector B; Step S8, calculating the absorbance ratio of the target asphalt binder and the reference asphalt binder; Step S9, calculating and determining the characteristic coefficients. This invention employs the above-mentioned rapid and high-precision identification method for asphalt characteristic functional groups to overcome the shortcomings of existing technologies and improve the identification efficiency and accuracy of asphalt binder characteristic functional groups.

Description

Technical Field

[0001] This invention relates to the field of environmental testing technology for road engineering, and in particular to a rapid and high-precision method for identifying characteristic functional groups in asphalt. Background Technology

[0002] In existing technologies, asphalt binders are important road engineering materials, and their performance is crucial for ensuring the quality of asphalt pavements. Currently, Fourier transform infrared spectroscopy (FTIR) is widely used in the quality inspection of asphalt binders. The traditional method involves manually comparing the infrared spectral data of a reference asphalt binder with those of a reference asphalt binder to determine the characteristic functional group spectrum of the target asphalt binder. This method can meet the quality inspection needs of traditional asphalt binders (such as base asphalt and SBS modified asphalt).

[0003] However, to meet the performance requirements of asphalt pavements, a wide variety of new asphalt binders are being used in current pavement engineering. Traditional methods rely on the professional experience of operators, and their low efficiency and low accuracy are becoming increasingly apparent. Therefore, there is an urgent need to propose a rapid and high-precision method for extracting characteristic functional groups of asphalt. Summary of the Invention

[0004] The purpose of this invention is to provide a rapid and high-precision method for identifying characteristic functional groups in asphalt, so as to overcome the shortcomings of the prior art and improve the identification efficiency and accuracy of characteristic functional groups in asphalt binders.

[0005] To achieve the above objectives, the present invention provides a rapid and high-precision method for identifying characteristic functional groups in asphalt, comprising the following steps:

[0006] Step S1: Conduct an infrared spectroscopy experiment on the target asphalt binder to obtain its infrared spectrum.

[0007] Step S2: Based on the infrared spectral experimental results of the target asphalt binder, construct the target covariance matrix;

[0008] Step S3: Eigenvalue decomposition and sorting of the target covariance matrix. The eigenvalues ​​of the constructed target asphalt covariance matrix are decomposed to extract the eigenvalues ​​and their corresponding eigenvectors, and then sorted in descending order of eigenvalues.

[0009] Step S4: Select the eigenvectors corresponding to the first i largest eigenvalues ​​to construct the orthogonal basis vectors A of the target asphalt binder;

[0010] Step S5: Conduct infrared spectroscopy experiments on the reference asphalt binder to obtain its infrared spectrum.

[0011] Step S6: Based on the infrared spectral experimental results of the benchmark asphalt binder, construct the benchmark covariance matrix;

[0012] Step S7: Construct the benchmark orthogonal basis vectors B;

[0013] Step S8: Calculate the absorbance ratio of the target asphalt binder and the reference asphalt binder;

[0014] Step S9: Calculation and determination of characteristic coefficients.

[0015] Preferably, in step S2, the column vectors of the target covariance matrix are the infrared spectral wavelengths, and the row vectors are the absorbances corresponding to different wavelengths.

[0016] Preferably, in step S4, i ≥ 6, which is the number of characteristic functional groups of the asphalt binder.

[0017] Preferably, in step S5, the reference asphalt binder is base asphalt or SBS modified asphalt.

[0018] Preferably, in step S6, the column vectors of the reference covariance matrix are the infrared spectral wavelengths, and the row vectors are the absorbances corresponding to different wavelengths.

[0019] Preferably, step S7 specifically includes:

[0020] The eigenvalue decomposition of the constructed benchmark asphalt covariance matrix is ​​performed to extract the eigenvalues ​​and their corresponding eigenvectors. Based on the i eigenvalues ​​selected for the target asphalt binder, the corresponding benchmark asphalt binder eigenvalues ​​are found, and the orthogonal basis vector B of the benchmark asphalt binder is constructed using its corresponding eigenvectors.

[0021] Preferably, step S8 specifically includes:

[0022] The ratio of infrared absorbance b corresponding to the kth characteristic value of the target asphalt binder and the reference asphalt binder is calculated according to the following formula. k :

[0023]

[0024] Among them, z k The infrared absorbance corresponding to the kth characteristic value of the target asphalt binder; l k The infrared absorbance corresponds to the kth characteristic value of the reference asphalt binder.

[0025] Preferably, step S9 specifically includes:

[0026] The characteristic coefficient d is calculated according to the following formula. k If d k If the value is greater than 1, then the functional group represented by the infrared spectral wavelength corresponding to k is the characteristic functional group.

[0027]

[0028] Among them, A T B is the transpose of the orthogonal basis vectors A of the target asphalt binder; T It is the transpose of the orthogonal basis vector B of the reference asphalt binder.

[0029] Therefore, the present invention employs a rapid and high-precision identification method for asphalt characteristic functional groups, which can effectively overcome the shortcomings of existing technologies, improve the identification efficiency and accuracy of asphalt binder characteristic functional groups, and provide a brand-new technical means for the field of asphalt testing, with broad application prospects and important practical significance.

[0030] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0031] Figure 1 This is a flowchart of an embodiment of a method for rapid and high-precision identification of asphalt feature functional groups according to the present invention. Detailed Implementation

[0032] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0034] Example 1

[0035] This invention provides a rapid and high-precision method for identifying characteristic functional groups of asphalt, which can be applied to the testing of road engineering materials. It helps engineers quickly and accurately identify the characteristic functional groups of asphalt binders, thereby assessing their performance and quality differences and providing a scientific basis for road construction.

[0036] Experimental materials include:

[0037] Target asphalt binder: A certain type of road base asphalt;

[0038] Reference asphalt binder: standard base asphalt.

[0039] like Figure 1 As shown, the specific steps include:

[0040] Step S1: Infrared spectroscopy experiment.

[0041] Infrared spectroscopy experiments were conducted on the target asphalt binder to obtain its infrared spectrum. It is assumed that the experimental data includes wavelengths in the range of 4000–650 cm⁻¹. -1 The absorbance value.

[0042] Step S2: Construct the target covariance matrix.

[0043] Based on the infrared spectral experimental results of the target asphalt binder, the target covariance matrix is ​​constructed.

[0044] in:

[0045] The column vectors are infrared spectral wavelengths (e.g., 4000, 3999, ..., 650 cm⁻¹). -1 );

[0046] The row vectors represent the absorbance corresponding to different wavelengths.

[0047] For example, assuming the experimental data contains 100 wavelength points, the target covariance matrix is ​​a (100×100) matrix.

[0048] Step S3: Decomposition and sorting of eigenvalues ​​of the target covariance matrix.

[0049] Eigenvalue decomposition is performed on the target covariance matrix to extract eigenvalues ​​and their corresponding eigenvectors, which are then sorted in descending order of eigenvalue. Assume the decomposition yields the top 6 largest eigenvalues ​​and their corresponding eigenvectors.

[0050] Step S4: Construct orthogonal basis vectors A.

[0051] Select the eigenvectors corresponding to the first (i=6) largest eigenvalues ​​to form the orthogonal basis vectors A of the target asphalt binder.

[0052] Step S5: Infrared spectroscopy experiment of reference asphalt binder.

[0053] Infrared spectroscopy experiments were conducted on a reference asphalt binder (standard base asphalt) to obtain its infrared spectrum. It is assumed that the experimental data also include wavelengths in the range of 4000–650 cm⁻¹. -1 The absorbance value.

[0054] Step S6: Construct the benchmark covariance matrix.

[0055] Based on the infrared spectral experimental results of the benchmark asphalt binder, a benchmark covariance matrix is ​​constructed. Its structure is similar to that of the target covariance matrix.

[0056] Step S7: Construct the baseline orthogonal basis vectors B.

[0057] Eigenvalue decomposition is performed on the benchmark covariance matrix to extract eigenvalues ​​and their corresponding eigenvectors. Based on the six eigenvalues ​​selected for the target asphalt binder, the corresponding eigenvalues ​​of the benchmark asphalt binder are found, and the orthogonal basis vectors B of the benchmark asphalt binder are constructed using their corresponding eigenvectors.

[0058] Step S8: Calculate the absorbance ratio.

[0059] The ratio of infrared absorbance b corresponding to the kth characteristic value of the target asphalt binder and the reference asphalt binder is calculated according to the formula. k :

[0060]

[0061] Among them, z k The infrared absorbance corresponding to the kth characteristic value of the target asphalt binder; l k The infrared absorbance corresponds to the kth characteristic value of the reference asphalt binder.

[0062] Step S9: Calculation and determination of characteristic coefficients.

[0063] Calculate the characteristic coefficient d according to the formula k :

[0064]

[0065] Among them, A T B is the transpose of the orthogonal basis vectors A of the target asphalt binder; T It is the transpose of the orthogonal basis vector B of the reference asphalt binder.

[0066] If (d) k If k > 1), then the functional group represented by the infrared spectral wavelength corresponding to k is the characteristic functional group.

[0067] Results analysis.

[0068] Based on the above steps, let's assume the following result is obtained:

[0069] For k=1, d1=1.2, it indicates that the functional group corresponding to the first wavelength is a characteristic functional group;

[0070] For k=2, d2=0.8, it indicates that the functional group corresponding to the second wavelength is not a characteristic functional group.

[0071] Finally, based on the above results, the characteristic functional groups of the target asphalt binder can be determined.

[0072] Therefore, the present invention employs a rapid and high-precision identification method for asphalt characteristic functional groups, which can effectively overcome the shortcomings of existing technologies, improve the identification efficiency and accuracy of asphalt binder characteristic functional groups, and provide a brand-new technical means for the field of asphalt testing, with broad application prospects and important practical significance.

[0073] It is worth noting that all the contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for rapid and high-precision identification of functional groups of bitumen, characterized in that This includes the following steps: Step S1: Conduct an infrared spectroscopy experiment on the target asphalt binder to obtain its infrared spectrum. Step S2: Based on the infrared spectral experimental results of the target asphalt binder, construct the target covariance matrix; the column vectors of the target covariance matrix are the infrared spectral wavelengths, and the row vectors are the absorbances corresponding to different wavelengths; Step S3: Eigenvalue decomposition and sorting of the target covariance matrix. The eigenvalues ​​of the constructed target asphalt covariance matrix are decomposed to extract the eigenvalues ​​and their corresponding eigenvectors, and then sorted in descending order of eigenvalues. Step S4, Select Before i The orthogonal basis vector A of the target asphalt binder is constructed by using the eigenvectors corresponding to the largest eigenvalues. Step S5: Conduct infrared spectroscopy experiments on the reference asphalt binder to obtain its infrared spectrum. Step S6: Based on the infrared spectral experimental results of the benchmark asphalt binder, construct the benchmark covariance matrix; the column vectors of the benchmark covariance matrix are the infrared spectral wavelengths, and the row vectors are the absorbances corresponding to different wavelengths; Step S7: Construct the benchmark orthogonal basis vectors B; Step S8: Calculate the absorbance ratio of the target asphalt binder and the reference asphalt binder; Step S8 specifically involves: The infrared absorbance ratio corresponding to the characteristic values of the target asphalt binder and the reference asphalt binder is calculated according to the following formula k b k :​ ； in, z k For the target asphalt binder k The infrared absorbance corresponding to each characteristic value; l k For the benchmark asphalt binder k The infrared absorbance corresponding to each characteristic value; Step S9, Calculation and determination of characteristic coefficients, specifically step S9 is as follows: The characteristic coefficient is calculated according to the following formula d k If d k is greater than 1, then k the functional group represented by the infrared spectrum wavelength corresponding to the characteristic functional group; ； wherein, is the transpose of the orthogonal basis vector A of the target asphalt binder; is the transpose of the orthogonal basis vector B of the reference asphalt binder.

2. The method for rapid and high-precision identification of asphalt characteristic functional groups according to claim 1, characterized in that, In step S4 i≥ 6, which is the number of characteristic functions of the bituminous binder.

3. The rapid and high-precision identification method for asphalt characteristic functional groups according to claim 1, characterized in that, In step S5, the reference asphalt binder is either base asphalt or SBS modified asphalt.

4. The method for rapid and high-precision identification of asphalt characteristic functional groups according to claim 1, characterized in that, Step S7 is as follows: The eigenvalue decomposition of the constructed benchmark asphalt covariance matrix is ​​performed to extract eigenvalues ​​and their corresponding eigenvectors. This is then applied based on the selected target asphalt binder. i Find the corresponding eigenvalues ​​of the reference asphalt binder, and construct the orthogonal basis vector B of the reference asphalt binder using its corresponding eigenvectors.

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