Low Temperature Performance Prediction Model of Cold-Filled SMA-13 Asphalt Mixture

Affiliation(s)

^{1}
The Transportation Engineering School, Shenyang Jianzhu University, Shenyang, China.

^{2}
Yueyang Maritime Bureau of the People’s Republic of China, Yueyang, China.

^{3}
Liaoning Provincial Transportation Development Center, Shenyang, China.

ABSTRACT

Sets of cold-filled SMA-13 asphalt mixture were designed by means of orthogonal design method. The bending and low temperature creep tests of the cold-filled SMA-13 asphalt mixture were carried out. The related models of the fractal dimension and the road performance evaluation index including low temperature bending failure strain ε_{B} and bending strength RB are established by using fractal theory. The model can be used to predict the low temperature performance of cold-filled SMA-13 asphalt mixture according to the design gradation, which can reduce the test workload and improve the working efficiency, so as to provide the reference for engineering design.

Sets of cold-filled SMA-13 asphalt mixture were designed by means of orthogonal design method. The bending and low temperature creep tests of the cold-filled SMA-13 asphalt mixture were carried out. The related models of the fractal dimension and the road performance evaluation index including low temperature bending failure strain ε

KEYWORDS

Low Temperature Performance, Prediction Model, Cold-Filled SMA-13 Asphalt Mixture, Fractal Dimension, Evaluation Index

Low Temperature Performance, Prediction Model, Cold-Filled SMA-13 Asphalt Mixture, Fractal Dimension, Evaluation Index

1. Introduction

The heat compensation method is not suitable for construction in a humid, low temperature environment and pollutes the environment. On the basis of the previous research results at home and abroad, the research group, combined with the characteristics of climate, transportation and materials in Northeast China, independently research and develop cold-filled asphalt mixture suitable for the freezing period and with good road performance. Cold-filled asphalt mixture low-temperature performance is an important component of road performance, especially for the northeastern region. If the correlation model between cold-filled asphalt mixture fractal dimension and low temperature performance evaluation index can be established, the low temperature performance of cold-filled asphalt mixture can be predicted through the gradation fractal dimension to reduce the amount of test work. Based on the correlation analysis between the fractal dimension and the evaluation index of low temperature performance, the low temperature performance prediction model is established and the low temperature performance prediction model of cold-filled SMA-13 asphalt mixture is recommended through the comparison of multiple models [1] .^{ }

2. The Raw Material Performance Test

Liaohe petroleum asphalt grade A No. 90, which is widely used in the northeast of China and the basic performance test results are shown in Table 1 [2] .^{ }

The coarse aggregate of cold-filled SMA-13 asphalt mixture use basalt gravel produced by Jilin Dawan Quarry. The basic performance test results are shown in Table 2 [3] .

The fine aggregate should be clean, dry, no weathering, no impurities, and appropriate particle size distribution. Fine aggregate use mechanism sand from limestone produced by Liaoyang Xiaotun Yongli quarries. The basic performance test results are shown in Table 3.

Lignin fiber was used, the basic performance test results are shown in Table 4.

The variation coefficient of test data in Tables 1-4 is less than 15%.

24 sets of cold-filled asphalt additive preparation schemes were designed, which were combined with matrix asphalt, thinner and mineral materials to form cold-filled asphalt mixture. The compaction, looseness and low-temperature workability test were tested to select the optimal one. No. 16 cold-filled asphalt liquid was selected [4] . The raw materials used in the test are all in line with the requirements of the road.

3. Cold-Filled Asphalt Mixture Design

The aggregate gradation design scheme and the optimum oil-stone ratio of cold-filled SMA-13 asphalt mixture are shown in Table 5 [5] .

Table 1. No. 90 Class A asphalt test results.

Table 2. Basalt coarse aggregate technical index.

Table 3. The technical index of limestone fine aggregate.

Table 4. The technical index of lignin fiber.

Test results

Trabecular bending test at a temperature of −10˚C were done according to the Standard Test Method of Bitumen and Bituminous Mixtures for Highway Engineering (JTG E20-2011). The experimental results and the corresponding fractal dimensions of the low temperature stability for cold-filled SMA-13 asphalt mixture are summarized in Table 6 [6] .

Model building

It can be seen from Table 6 that the range of fractal dimension satisfying the low-temperature bending strain is D = 2.5484 - 2.6122, D_{c} = 2.0172 - 2.1676, D_{f} = 2.6695 - 2.8772.

The ternary linear regression model is established through taking ε_{B} as the dependent variable, taking D, D_{c}, D_{f} as the independent variables.

Table 5. The scheme of gradation design of cold-filled SMA-13 asphalt mixture.

Table 6. The fractal dimension of cold-filled SMA-13 asphalt mixture and the low temperature test data.

Note: The second group of bending strain ε_{B} data in the table is subject to further determination. The variation coefficient of test data in the table is less than 15%.

The correlation model of the bending strain and the fractal dimension is established by the regression analysis, as is shown in Formula (1).

ε_{B} = −432953 − 20571.98D + 99964.86 D_{nc} + 104839.34 D_{f} (1)

Regression coefficient R^{2} = 0.956.

The ternary linear correlation models of bending strain and three fractal dimensions are established, the correlations of data in Table 6 are analyzed by using SPSS software to obtain the correlation between the bending strain and fractal dimension, as is shown in Table 7.

It can be seen from Table 7, the correlation sequence of low temperature bending strain ε_{B} and the fractal dimension D, D_{C}, D_{f} from large to small is D_{f} > D > D_{C}, indicating that the relation between the aggregate fractal dimension and bending strain is relatively large, the correlation between ε_{B} and D_{C} is relatively small.

The correlation model of ε_{B} and D_{f} is established, as is shown in the formula (2).

ε_{B} = −115184 + 46204D_{f} (2)

Regression coefficient R^{2} = 0.790.

The correlation model of ε_{B} and D, D_{f} is established, as is shown in the Formula (3).

ε_{B} = −333868 + 104099D + 28508D_{f} (3)

Regression coefficient R^{2} = 0.998.

Similarly, the ternary linear regression models of bending strength is established, as is shown in the Formula (4).

RB = 59 + 22.55D − 26.96D_{c} − 21.19D_{f} (4)

Regression coefficient R^{2} = 0.940.

For the correlation between the bending failure strength and the fractal dimension, the data in Table 6 are analyzed by SPSS software. The relationship between the bending failure strength R_{B} and the fractal dimension is shown in Table 8.

Table 7. Correlation between low temperature bending failure strain and fractal dimension of cold-filled SMA-13 asphalt mixture.

Table 8. Correlation between low temperature bending strength and fractal dimension of cold-filled SMA-13 asphalt mixture.

Table 9. The prediction model comparison of bending strain and bending strength for cold-filled SMA-13 asphalt mixture.

It can be seen from Table 8 that the correlation between the bending strength R_{B} and the fractal dimension D_{C} of the coarse aggregate gradation is relatively large. Therefore, a correlation model between the bending strength and the fractal dimension D_{C} can be established. But the regression coefficient is low.

4. Model Selection

As described above, a correlation model of low-temperature bending strain, bending strength and fractal dimension is established, and the results are summarized in Table 9.

It can be seen from Table 9 that the prediction accuracy of model 1, 3 and 4 is relatively high, and the model 1 and 3 are recommended as the prediction model of low temperature bending strain and the model 4 is recommended as the prediction model of low temperature bending strength through multi-model comparison.

5. Conclusion

The correlation model recommended between the fractal dimension and the evaluation index of low temperature performance can be used to predict the low temperature performance of cold-filled SMA-13 asphalt mixture according to the design gradation, which can reduce the test workload and improve the working efficiency.

Acknowledgements

This research was financially supported by Liaoning Provincial Expressway Operation Management Co., Ltd.

Cite this paper

Sun, Z. , Wang, S. , Ma, S. and Liu, S. (2018) Low Temperature Performance Prediction Model of Cold-Filled SMA-13 Asphalt Mixture.*Materials Sciences and Applications*, **9**, 1066-1072. doi: 10.4236/msa.2018.913077.

Sun, Z. , Wang, S. , Ma, S. and Liu, S. (2018) Low Temperature Performance Prediction Model of Cold-Filled SMA-13 Asphalt Mixture.

References

[1] Sun, Z.H. (2017) Asphalt Mixture Mix Design Method. People’s Communications Publishing Co., Ltd., Beijing.

[2] Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering. JTGE20-2011, Occupation Standard of the People’s Republic of China.

[3] Test Methods of Aggregate for Highway Engineering. JTGE42-2005, Occupation Standard of the People’s Republic of China.

[4] Li, Q. (2017) Cold-Filled Asphalt Mixture Design Method. Master Thesis, Shenyang Jianzhu University, Shenyang.

[5] Sun, Z.H., Yu, Q.B., Wang, T.B., Yu, B.Y., Zhu, G.Q. and Ma, J. (2014) The Effect of Asphalt and Aggregate Gradation on the Low-Temperature Performance of Asphalt Mixtures for Intermediate and Underlying Course. Applied Mechanics and Materials, 505-506, 251-254.

[6] Highway Science Research Institute and Ministry of Communications Technical Specifications for Construction of Highway Asphalt Pavements. JTGF40-2004.

[1] Sun, Z.H. (2017) Asphalt Mixture Mix Design Method. People’s Communications Publishing Co., Ltd., Beijing.

[2] Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering. JTGE20-2011, Occupation Standard of the People’s Republic of China.

[3] Test Methods of Aggregate for Highway Engineering. JTGE42-2005, Occupation Standard of the People’s Republic of China.

[4] Li, Q. (2017) Cold-Filled Asphalt Mixture Design Method. Master Thesis, Shenyang Jianzhu University, Shenyang.

[5] Sun, Z.H., Yu, Q.B., Wang, T.B., Yu, B.Y., Zhu, G.Q. and Ma, J. (2014) The Effect of Asphalt and Aggregate Gradation on the Low-Temperature Performance of Asphalt Mixtures for Intermediate and Underlying Course. Applied Mechanics and Materials, 505-506, 251-254.

[6] Highway Science Research Institute and Ministry of Communications Technical Specifications for Construction of Highway Asphalt Pavements. JTGF40-2004.