CN110455740B - Asphalt aging time course prediction method - Google Patents
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Abstract
The invention discloses a method for predicting asphalt aging time course, which comprises the steps of dividing a plurality of asphalt samples into a plurality of parts, heating and aging the parts for different time lengths, rapidly melting each small sample after aging treatment, and flatly coating the small sample on dry and clean SiO2On the surface of the glass sheet, and SiO2The surface of the glass sheet is completely covered, then a Fourier transform infrared spectrometer is adopted, and diamond ATR is selected for each SiO2Collecting spectrograms of the asphalt on the glass sheet, analyzing the spectrogram obtained after each small sample is processed in the step S3 by using a total reflection infrared spectroscopy analysis method and an infrared spectrogram quantitative analysis method, and constructing an asphalt aging time course prediction model based on main components according to the result obtained after analysis; and collecting the spectrogram of the sample to be detected, analyzing the obtained spectrogram by using a total reflection infrared spectroscopy analysis method and an infrared spectrogram quantitative analysis method, and introducing the analysis result into the prediction model obtained in the step S4 to obtain the aging time course of the asphalt to be detected. The method is simple.
Description
Technical Field
The invention belongs to the field of road engineering, and particularly relates to an asphalt aging time course prediction method based on infrared spectroscopy.
Background
Asphalt is a blended high-molecular organic compound, slow aging of a pavement in service can cause changes of chemical components and rheological properties, and the aging of the asphalt plays a key role in the quality of pavement performance, which is always a hot problem in the research of road building materials. Because the asphalt structure and the property change are complex, the asphalt aging stage is difficult to distinguish, and the road maintenance node can not be accurately grasped, the road maintenance investment scale in China is increased year by year. Therefore, the method has considerable research value for judging the aging degree of the road asphalt and can provide theoretical basis for road maintenance departments. The infrared spectrum quantitative analysis is widely applied in the field of asphalt aging, can establish a relation with macroscopic performance, and starts to directly combine all data of infrared spectrum reflection spectrum with a chemometrics method to establish a model along with the rapid development of chemometrics so as to realize the prediction of macroscopic indexes. However, part of regions in the spectrogram do not change with the aging degree of the asphalt, and the problem becomes more complicated by establishing a model through the full spectrum of the infrared spectrum, so that the application of the full spectrum in the judgment of the aging degree of the road asphalt is limited.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a pitch aging time course prediction method which is simple in steps, high in accuracy and based on infrared spectrum.
In order to achieve the purpose, the technical scheme of the invention is as follows: a pitch aging time course prediction method comprises the following steps:
step S1: taking m asphalt samples, equally dividing each asphalt sample into n small samples, respectively baking the n small samples corresponding to each asphalt sample at constant temperature for different durations to carry out aging treatment of different degrees, wherein the aging treatment flows of the m asphalt samples are consistent, and m and n are positive integers more than 2;
step S2: quickly melting each small sample aged in the step S1, sampling x parts of the sample, and flatly coating the sample on x dry clean SiO2On the surface of the glass sheet, and SiO2The surface of the glass sheet is completely covered, then a Fourier transform infrared spectrometer is adopted, and diamond ATR is selected for each SiO2On the glass sheetCollecting spectrogram of asphalt, wherein each SiO is2Respectively collecting y times of asphalt on the glass sheet to obtain m x n x y parts of maps, wherein x and y are positive integers;
step S3: performing SNV smoothing and baseline correction processing on the x y maps corresponding to each small sample in the step S2, and calculating the average value of the absorption intensity of the maps to serve as the final map data of the asphalt sample;
step S4: analyzing the obtained map of each small sample after being processed in the step S3 by using a total reflection infrared spectrum analysis method and an infrared spectrogram quantitative analysis method, and inputting the obtained main characteristic absorption peak functional group index value of each asphalt sample into SPSS23 software for PCA analysis to construct an asphalt aging time course prediction model;
step S5: melting an asphalt sample to be measured, and then flatly coating the melted asphalt sample on dry and clean SiO2On the surface of the glass sheet, and SiO2The surface of the glass sheet is completely covered, then a Fourier transform infrared spectrometer is adopted, and diamond ATR is selected for each SiO2And (4) collecting the spectrogram of the asphalt on the glass sheet, analyzing the obtained spectrogram by using a total reflection infrared spectroscopy analysis method and an infrared spectrogram quantitative analysis method, and introducing the analysis result into the prediction model obtained in the step S4 to obtain the aging time course of the asphalt to be detected.
In the above technical scheme, m is 4, n is 5, x is 3, and y is 3.
In the technical scheme, the aging treatment time of four small samples in the five small samples corresponding to each asphalt sample is 85min, 120min, 240min and 360min respectively, and the rest of the five small samples are not subjected to aging treatment;
in the above technical solution, the baking device used in the baking in step S1 is a rotary thin film oven.
The baking temperature of the rotary film oven in the technical scheme is 163 ℃.
In the technical scheme, the Fourier transform infrared spectrometer needs to be preheated for at least 30 minutes in advance, background scanning is carried out before each measurement, and acquisition parameters are set to have the resolution of 4cm-1The number of scanning times is 32The test range is 500-4000 cm-1。
In the above technical scheme, the SiO2The gauge of the glass sheet was 20mm by 1 mm.
Compared with the prior art, the invention has the beneficial effects that: repeated spectrum analysis is carried out on each small sample for multiple times, so that the repeatability is guaranteed, and errors caused by unstable measuring environment and non-uniform asphalt aging are overcome; and with SiO2The glass sheet replaces ZnSe crystals, so that the sample preparation is simple and quick, the process of cleaning the crystals is avoided, and the attenuated total reflection infrared spectrum can be obtained more quickly; the prediction model constructed by the method is high in prediction precision and good in applicability.
Drawings
FIG. 1 illustrates the operating principle of Fourier infrared spectroscopy;
FIG. 2 is a principal component analysis dimension reduction model;
FIG. 3 shows the infrared characterization results of Jinling No. 70 asphalt;
FIG. 4 shows the infrared characterization of Tapuk No. 70 asphalt;
FIG. 5 is the infrared characterization of SBS-1 asphalt;
FIG. 6 is a representation of the infrared characterization of SBS-2 asphalt;
FIG. 7 is a schematic diagram of peak area calculation;
FIG. 8 shows the index variation trend of carbonyl sulfoxide functional group of Nanjing 70# asphalt;
FIG. 9 shows the index change trend of the carbonyl sulfoxide functional group of Tapek 70# asphalt;
FIG. 10 shows the index change trend of the SBS-1 asphalt carbonyl sulfoxide functional group;
FIG. 11 shows the index change trend of the SBS-2 asphalt carbonyl sulfoxide functional group;
FIG. 12 is a graph showing the relationship between the aging time and the comprehensive index F of the base asphalt and the modified asphalt among the four kinds of asphalt.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
The embodiment provides a method for predicting an asphalt aging time course, which comprises the following steps:
step S1: taking 4 asphalt samples (four asphalt samples in the embodiment are Jinling 70#, Tapock 70#, SBS-1 and SBS-2 asphalt as an example, wherein the first two asphalt samples are base asphalt, and the second two asphalt samples are modified asphalt), equally dividing each asphalt sample into 5 small samples, respectively baking the 5 small samples corresponding to each asphalt sample at constant temperature in a rotary film oven at 163 ℃ for different aging treatments in different degrees, wherein the aging treatment time periods of the four small samples in the five small samples corresponding to each asphalt sample are 85min, 120min, 240min and 360min, respectively, and the rest of the five small samples are not subjected to aging treatment (namely, the baking time is 0 min);
step S2: quickly melting each small sample aged in the step S1, sampling 3 parts of the sample, and flatly coating the sample on 3 dry clean SiO films2On the surface of the glass sheet, and SiO2Complete coverage of the surface of the glass sheet, said SiO2The specification of the glass sheet was 20mm 1mm, then a fourier transform infrared spectrometer was used and diamond ATR was selected for each of the SiO2Collecting spectrogram of the asphalt on the glass sheet, wherein each SiO is2Collecting the asphalt on the glass sheet for 3 times respectively to obtain 180 parts of maps in total;
step S3: in order to avoid influences of instrument noise, uneven samples, base line drift, light scattering and the like, preprocessing original spectrum data, carrying out SNV smoothing (the number of smoothing points is 5) and base line correction processing (the used spectrum analysis software is Thermo Scientific OMNIC) on 9 infrared spectrograms corresponding to each small sample, obtaining the average value of the absorption intensity as the final spectrogram data of the asphalt sample, then introducing a CSV file of the absorption intensity of the spectrogram after pretreatment of 20 small samples into Origin software, and drawing the spectrogram (the infrared spectrums of the 20 samples of the four types of asphalt are similar in shape, the positions of characteristic absorption peaks, namely functional groups, are approximately the same, but the peak heights, namely the absorbance, of partial characteristic absorption peaks are different due to different properties of the samples);
step S4: analyzing the obtained map of each small sample after being processed in the step S3 by using a total reflection infrared spectrum analysis method and an infrared spectrogram quantitative analysis method, and inputting the obtained main characteristic absorption peak functional group index value of each asphalt sample into SPSS23 software for PCA analysis to construct an asphalt aging time course prediction model;
step S5: melting an asphalt sample to be measured, and then flatly coating the melted asphalt sample on dry and clean SiO2On the surface of the glass sheet, and SiO2The surface of the glass sheet is completely covered, then a Fourier transform infrared spectrometer is adopted, and diamond ATR is selected for each SiO2And (4) collecting the spectrogram of the asphalt on the glass sheet, analyzing the obtained spectrogram by using a total reflection infrared spectroscopy analysis method and an infrared spectrogram quantitative analysis method, and introducing the analysis result into the prediction model obtained in the step S4 to obtain the aging time course of the asphalt to be detected.
In the above technical solution, the fourier transform infrared spectrometer in step S2 needs to be preheated for at least 30 minutes in advance, background scanning is performed before each measurement, and the acquisition parameters are set to have a resolution of 4cm-1The scanning times are 32 times, and the test range is 500-4000 cm-1。
Wherein, the working principle of Fourier infrared spectrum is shown in figure 1, figure 2 is a principal component analysis dimensionality reduction model, in order to analyze the change of different asphalts along with aging time, infrared spectrum comparison graphs of the same asphalts under different aging time are respectively drawn, as shown in figures 3-6, the positions and changes of the spectral peaks of Jinling 70# and Taipock 70# are the same, 13 obvious characteristic absorption peaks are provided, and the positions and changes of the spectral peaks of SBS-1 asphalt and SBS-2 asphalt are the same, compared with the base asphalt, except the 13 common absorption peaks, the position and changes of the spectral peaks are 699cm at 699cm-1And 966cm-1Two obvious characteristic absorption peaks are added, so that the matrix asphalt and the SBS modified asphalt can be distinguished by the existence of the two absorption peaks; four kinds of original asphalt are 1700cm-1There was no absorption peak, and after a short aging period of 85min, a carbonyl (C = O) absorption peak appeared there due to oxidation of carbon and oxygen, with increasing aging timeAbsorption peaks become more and more obvious, but the carbonyl absorption peaks of different asphalts have different change speeds; four types of original asphalt at 1030cm-1The sulfoxide group (S = O) has a weaker absorption peak, the longer the aging time is, the stronger the absorption peak intensity of the sulfoxide group is, and the change rate of the sulfoxide group of different asphalts is different. The sulfoxide group and the carbonyl group both change obviously with the increase of the aging time, so that the sulfoxide group and the carbonyl group can be used for reflecting the aging degree of the asphalt, and researches show that the content change of four components in the aging process of the asphalt can reflect the aging degree of the asphalt. 2920cm reflecting the variation of the saturation fraction of the asphalt was selected in this example-1And 2820cm-1Fatty functional group (CH) of (C)2,CH3) And 1376cm-1And 1456cm-1Asymmetric aliphatic functionality (C-CH)3) 1600cm reflecting the change of aromatic ring components (aromatic components, colloid and asphaltene) of the asphalt-1At 1700cm of aromatic functional group-1Carbonyl (C = O) functional group, 1030cm-1Sulfoxide (S = O) functional groups were the main targets for quantitative analysis.
According to Lambert-beer's law, the infrared spectrograms of 20 asphalt samples are quantitatively analyzed, firstly, a correction base line, namely tangent lines of the lowest points on two sides of a characteristic absorption peak, is determined, then, the area defined by the base line and a spectral curve is calculated and is the peak area of the absorption peak, a schematic diagram of correction base line selection and peak area calculation is shown in figure 7, finally, a CSV file of the asphalt sample is led into OMNIC software, and a peak area calculation tool is used for obtaining the peak areas of 15 obvious characteristic absorption peaks of each asphalt. In order to find the best quantitative analysis method for the four types of asphalt samples selected and best suitable for the research, the standards selected for the research include the following four: a. the1、650～1400 cm-1Sum of peak areas of range fingerprint regions; a. the2、1400～4000 cm-1The peak area of the range group stretching vibration area is sum; a. the3、650～4000cm-1A full spectrum of ranges; a. the42920 and 2850cm-1 The sum of the peak areas; comparing different analysis modes, the area ratio of each characteristic absorption peak, namely the functional group index is defined as follows:
in the formula: i isC=OAnd IS=ORespectively are indexes of carbonyl and sulfoxide functional groups; i isBAnd IB,aIs an aliphatic functional group, an asymmetric aliphatic functional group index; i isArIs an aromatic functional group index; a. the2920、A2850、A1700、A1600、A1456、A1376、A1030Wave numbers of 2920, 2850, 1700, 1600, 1456, 1376 and 1030cm-1The corresponding peak area; sigma AiThe peak area under the ith standard is the sum (i =1,2,3, 4), and specific values are shown in table 1.
TABLE 1 Table for taking values of spectral peak area of each reference
According to the analysis, the peak area of the functional group is irrelevant to the selection of the reference standard, and the selection of different reference standards can cause the change rule of the functional group index along with the increase of the aging time to have difference and even opposite change trends. The asphalt is subjected to oxidation reaction with oxygen to generate carbonyl and sulfoxide groups, so that the functional group indexes of the carbonyl and sulfoxide groups show a growing trend along with the prolonging of the aging time. The research will respectively research the variation trend of the carbonyl and sulfoxide functional group indexes of four kinds of asphalt under different standards along with the aging time, and find the reference standard which is most suitable for the asphalt selected in the research based on the variation trend. And (3) calculating the functional group indexes of the asphalt samples under different benchmarks according to the formula (1), wherein the index change trend of the carbonyl and sulfoxide functional groups is shown in figures 8-11. The carbonyl indexes of the four types of asphalt show a growth trend under four selected reference benchmarks, and the sulfoxide group of the Tappack 70# asphalt has negative growth within the aging time of 120-240 min under the A2, A3 and A4 benchmarks, and does not accord with the asphalt aging mechanism, so that the A1 (650-1400 cm) is adopted-1The range fingerprint area is used as a reference standard, and the change of each functional group in the aging process can be accurately and reasonably represented.
And finally, establishing an asphalt aging time course prediction model based on principal component analysis.
The 5 main characteristic absorption peak functional index values for the 20 asphalt samples were input into the SPSS23 software for PCA analysis and the results are shown in tables 2-4. From table 2, it can be seen that the correlation between the original variables is high, wherein the correlation between the index of the aliphatic functional group and the index of the asymmetric aliphatic functional group is up to 0.946, and the correlation between the index of the carbonyl group and the index of the sulfoxide functional group is also up to 0.842, so that it is necessary to establish a principal component analysis model.
TABLE 2 correlation matrix of index of five characteristic functional groups
| IC=O | IS=O | IB,a | IB | IAr | |
| IC=O | 1 | 0.842 | 0.18 | 0.135 | 0.16 |
| IS=O | 0.842 | 1 | 0.36 | 0.395 | 0.125 |
| IB,a | 0.18 | 0.36 | 1 | 0.946 | 0.819 |
| IB | 0.135 | 0.395 | 0.946 | 1 | 0.479 |
| IAr | 0.16 | 0.125 | 0.819 | 0.479 | 1 |
TABLE 3 Total variance interpretation
TABLE 4 rotational component matrix
| Index of functional group | PCA1 | PCA2 |
| IC=O | 0.36 | 0.852 |
| IS=O | 0.525 | 0.779 |
| IB,a | 0.937 | -0.295 |
| IB | 0835 | -0.216 |
| IAr | 0.699 | -0.37 |
In the total variance interpretation table (table 3), the eigenvalues of two components are greater than 1, and the cumulative contribution of the two components is as high as 81.425%, so that most of the information of the original five data indexes can be represented by selecting the first two components as principal components. Then, the rotational component matrix in Table 4 is analyzed to find IB,a、IB、IArThese three functional group indices are more heavily loaded on PCA1, indicating that PCA1 represents the change in aliphatic functionality and aromatic functionality, i.e., the change in component content, and PCA1 can be defined as "component Change factor", whereas I isC=OAnd IS=OThe loading on PAC2 was greater, then PCA2 reflected a change in the index of the carbonyl and sulfoxide functional groups due to oxidation with oxygen, and PCA2 was defined as the "oxidation factor". In addition, the principal component score of each asphalt sample is calculated according to the component score coefficient matrix, and the expressions of PCA1, PCA2 and the comprehensive index F are as follows:
in the formula: ZX 1-ZX 5 are respectively I of each asphalt sampleC=O、IS=O、IB,a、IB、IArData after functional group indices were normalized.
And calculating the comprehensive index F value of each asphalt sample according to the formula, and finding that the comprehensive index F values of the four types of asphalt are increasingly larger along with the deepening of the aging degree, so that the aging of the asphalt can be represented according to the comprehensive index F. Fig. 12 shows the relationship between the F value and the aging time of four kinds of asphalt with different aging times, and it can be seen that the aging times of two kinds of base asphalt and two kinds of modified asphalt are respectively linear with the aging time within the range of 0-360 min, and the correlation degree is higher, wherein the linear fitting correlation degree of the two kinds of base asphalt is 0.9509, while the modified asphalt is slightly lower, mainly because the aging process of the modified asphalt is complex and the influence factors are more. The asphalt aging time course can be predicted by the following mathematical model:
in the formula: f is the comprehensive index of the asphalt, and t is the aging time (min) of the asphalt.
SK90# base asphalt with the aging time of 360min and modified asphalt with the aging time of 240minAnd blue, collecting the attenuated total reflection infrared spectra of the two, carrying out quantitative analysis to obtain a functional group index, substituting the standardized data into the formula (3), and respectively calculating the principal component score and the comprehensive index F of the two. Substituting F into the prediction models respectively to obtain predicted aging time tsk90#=358min、tModified asphalt=234min, the coefficient of variation was 0.39% and 1.79%, respectively, and it was found that the prediction model was reliable. Therefore, the aging time of the asphalt can be quickly obtained through the prediction model only by acquiring the attenuation total reflection infrared spectrogram of unknown asphalt, and meanwhile, the experimental result accords with the attenuation total reflection infrared spectrum characteristics, so that the prediction model is reasonable and applicable.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (5)
1. The asphalt aging time course prediction method is characterized by comprising the following steps:
step S1: taking 4 asphalt samples, wherein the four asphalt samples respectively comprise two types of base asphalt and modified asphalt, equally dividing each asphalt sample into 5 small samples, respectively baking the 5 small samples corresponding to each asphalt sample at constant temperature for different durations to perform aging treatment of different degrees, and the aging treatment flows of the 4 asphalt samples are consistent, wherein the aging treatment durations of the four small samples in the five small samples corresponding to each asphalt sample are respectively 85min, 120min, 240min and 360min, and the rest one small sample is not subjected to aging treatment;
step S2: quickly melting each small sample aged in the step S1, sampling 3 parts of the sample, and flatly coating the sample on 3 dry clean SiO films2On the surface of the glass sheet, and SiO2The surface of the glass sheet is completely covered, then a Fourier transform infrared spectrometer is adopted, and diamond ATR is selected for each SiO2Collecting spectrogram of the asphalt on the glass sheet, andeach of the SiO23 times of collecting the asphalt on the glass sheet respectively to obtain 4 × 5 × 3 parts of maps;
step S3: performing SNV smoothing and baseline correction treatment on 3 x 3 maps corresponding to each small sample in the step S2, and calculating the average value of the absorption intensity of the maps to serve as the final map data of the asphalt sample;
step S4: analyzing the obtained map of each small sample after being processed in the step S3 by using a total reflection infrared spectrum analysis method and an infrared spectrogram quantitative analysis method, inputting the obtained main characteristic absorption peak functional group index values of each asphalt sample into SPSS23 software for PCA analysis to construct an asphalt aging time course prediction model, wherein:
in formula (1): i isC=OAnd IS=ORespectively are the indexes of carbonyl and sulfoxide functional groups, IBAnd IB,aIs an aliphatic functional group, an asymmetric aliphatic functional group index, IArIs an index of aromatic functional groups, A2920、A2850、A1700、A1600、A1456、A1376、A1030Wave numbers of 2920, 2850, 1700, 1600, 1456, 1376 and 1030cm-1Is measured at the corresponding peak area, ∑ AiIs the sum of peak areas at the ith basis, i =1,2,3, 4;
in formula (2): ZX 1-ZX 5 are respectively I of each asphalt sampleC=O、IS=O、IB,a、IB、IArThe data of the functional group index after standardization, PCA1 is a component variation factor, and PCA2 is an oxidation factor;
the prediction models of the matrix asphalt and the modified asphalt are as follows:
in formula (3): f is the comprehensive index of the asphalt, and t is the aging time of the asphalt, and the unit is min;
step S5: melting an asphalt sample to be measured, and then flatly coating the melted asphalt sample on dry and clean SiO2On the surface of the glass sheet, and SiO2The surface of the glass sheet is completely covered, then a Fourier transform infrared spectrometer is adopted, and diamond ATR is selected for each SiO2And (4) collecting the spectrogram of the asphalt on the glass sheet, analyzing the obtained spectrogram by using a total reflection infrared spectroscopy analysis method and an infrared spectrogram quantitative analysis method, and introducing the analysis result into the prediction model obtained in the step S4 to obtain the aging time course of the asphalt to be detected.
2. The method for predicting the aging time course of asphalt according to claim 1, wherein the baking device in the baking in step S1 is a rotary thin film oven.
3. The method for predicting the aging time course of asphalt according to claim 2, wherein the baking temperature of the rotary thin film oven is 163 ℃.
4. The asphalt aging time course prediction method according to any one of claims 1 to 3, wherein the Fourier transform infrared spectrometer is preheated for at least 30 minutes in advance, background scanning is performed before each measurement, and the acquisition parameters are set as follows: resolution was 4cm-1The scanning times are 32 times, and the test range is 500-4000 cm-1。
5. The method for predicting the aging time course of asphalt according to claim 4, wherein the SiO is2The gauge of the glass sheet was 20mm by 1 mm.
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| CN111595727B (en) * | 2020-06-30 | 2021-09-14 | 武汉理工大学 | Method for establishing evaluation for rapidly predicting asphalt-aggregate adhesiveness and asphalt toughness |
| CN113008738B (en) * | 2021-03-09 | 2024-04-19 | 山西省交通规划勘察设计院有限公司 | Method for determining short-term aging test parameters of SBS modified asphalt |
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