# Effect of Mixture Design Parameters of Stone Mastic Asphalt Pavement on Its Skid Resistance

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## Abstract

**:**

## Featured Application

**This paper seeks to analyze the effect of gradation and asphalt content on the skid resistance of stone mastic asphalt pavement.**

## Abstract

_{NMSA}), the percentage of aggregates passing the sieve size which is only one smaller than the maximum size (P

_{NMSA-1}), the percentage of aggregates passing the control sieve size (P

_{CS}), the percentage of aggregates passing the sieve size which is only one smaller than the control sieve size (P

_{CS-1}), and asphalt content (AC), and each factor had four levels. The skid-resistance index (SI) obtained by the 3D measurement was used to evaluate skid resistance. The results show that the three parameters (P

_{NMSA}, P

_{CS}, and AC) are the key parameters to improve skid resistance. Among them, P

_{NMSA}has the greatest impact on the skid resistance, AC is the second, and the impact of P

_{CS}on skid resistance is the smallest. Moreover, the design parameters with best skid resistance are proposed.

## 1. Introduction

_{NMSA}), the percentage of aggregates passing the sieve size which is only one smaller than the maximum size (P

_{NMSA-1}), the percentage of aggregates passing the control sieve size (P

_{CS}), the percentage of aggregates passing the sieve size which is only one smaller than the control sieve size (P

_{CS-1}), and asphalt content (AC), and each factor had four levels. The skid-resistance index (SI) obtained by the 3D measurement was used to evaluate skid resistance.

## 2. Materials and Methods

#### 2.1. Materials

#### 2.1.1. Asphalt

#### 2.1.2. Aggregate

#### 2.2. Mixture Design

_{CS}) in this process. According to Chinese standard JTG F40-2004, P

_{CS}with both SMA-16 and SMA-13 are 4.75 mm, P

_{CS}with SMA-10 and SMA-5 are 2.36 mm and 1.18 mm respectively. P

_{NMSA}and P

_{CS}were key factors for SMA gradation design [32]. Moreover, surface texture is affected by AC [8]. Therefore, P

_{NMSA}, P

_{NMSA-1}, P

_{CS}, P

_{CS-1}, and AC were selected for mixture design factors. Each factor had four levels.

_{16}(4

^{5}) was selected. The value of four parameters (P

_{NMSA}, P

_{NMSA-1}, P

_{CS}, and P

_{CS-1}) associated with gradations was determined according to gradations ranges in Chinese standard JTG F40-2004. The values of AC were selected according to OAC. Table 5, Table 6, Table 7 and Table 8 show the factors and levels of different SMA types. Table 9 is the orthogonal experimental design table with five factors and four levels for each factor.

_{i}) and range (R) at corresponding levels could be calculated by the following formulas.

_{i}represents mean values of SI for each factor at a certain level i (i = 1, 2, 3, 4). The skid resistance is better with higher k

_{i}. For each factor, k

_{max}is maximal among four levels, while k

_{min}is the minimum. R reflects the effect order of different factors. The factor has stronger impact on skid resistance with higher R.

#### 2.3. Pavement Surface Texture Characterization

#### 2.3.1. Establishing 3D Models of Pavement Texture

#### 2.3.2. Calculation of SI

## 3. Results

_{i}represents mean values of SI for each factor at a certain level i (i = 1, 2, 3, 4), and R reflects the effect order of different factors. The above three parameters could be calculated by the corresponding formula. Table 10, Table 11, Table 12 and Table 13 show the calculation results.

- For different SMA types, the effect order of the three factors (P
_{NMSA}, P_{CS}, and AC) is constant. Among them, P_{NMSA}has the greatest impact on the skid resistance, AC is the second, and the impact of P_{CS}on skid resistance is the smallest. Moreover, the three parameters have higher impact on skid resistance than the other two parameters in general. It is obvious that the three parameters are the key parameters to improve skid resistance. - The effect order of P
_{NMSA-1}and P_{CS-1}with different SMA types is various. For SMA-16, P_{NMSA-1}has a greater impact on skid resistance than P_{CS-1}, but the conclusion is the opposite for SMA-13. - For P
_{NMSA-1}, SMA pavements with different mixture types have the best skid resistance at the level (i = 1). For other design parameters, the corresponding levels (i) with different mixture types are significantly different when the skid resistance is best. - Taking SMA-13 as an example, AC (5.6%) is lower than OAC (6.3%) when the skid resistance is best. It is possible that the surface texture of SMA pavement with lower AC is richer, resulting in better skid resistance. It is noticed that the pavement may have excellent skid resistance but poor pavement performance when AC is 5.6%. Therefore, the determination of the best design parameters should be considered both skid resistance and pavement performance.

_{i}with different levels. For each factor, the value increased with levels.

#### 3.1. The Influence of NMAS on Skid Resistance

_{NMAS}and the skid resistance of SMA pavement. It can be seen that the skid resistance gradually reduces with the increasing of P

_{NMAS}. P

_{NMAS}refers to percentage of aggregates passing of the maximum size. Lower P

_{NMAS}means more aggregates remaining on the sieve. The macrotexture of asphalt pavement is mainly contributed by the coarse aggregate. The content of aggregates with the size larger than nominal maximum size leads to richer macrotexture, resulting in better skid resistance.

#### 3.2. The Influence of Control Sieve Percent on Skid Resistance

_{CS}, the trend of k

_{i}with different mixture types stays the same, which first decreases and then increases. It means that the skid resistance of SMA pavement first decreases and then increases with increasing P

_{CS}. For P

_{NMAS-1}and P

_{CS-1}, k

_{i}with different mixture types and levels of factors is various. However, the value of k

_{i}does not change much, indicating that P

_{NMAS-1}and P

_{CS-1}have an insignificant effect on skid resistance of SMA pavement.

#### 3.3. The Influence of AC on Skid Resistance

_{i}is consistent for different mixture types in general with increasing AC. k

_{i}gradually decreases as AC increases, indicating that the skid resistance decreases. This is mainly due to the fact that when AC increases, the asphalt film is thicker, resulting in a smaller macrotexture, which leads to weaker skid resistance. It is noticed that the trend of k

_{i}of SMA-5 is significantly different from the other SMA types. k

_{i}first increases and then decreases with increasing AC. When the aggregates sizes are small enough, the compaction work during the specimen process may lead to rearrangement of aggregates, resulting in an abnormality in the gradation. It may have an impact on skid resistance.

## 4. Conclusions

- The three parameters (P
_{NMSA}, P_{CS}, and AC) are the key parameters to improve skid resistance. Among them, P_{NMSA}may have the greatest impact on the skid resistance, AC is the second, and the impact of P_{CS}on skid resistance is the smallest. Moreover, these three parameters have higher impact on skid resistance than the other two parameters (P_{NMSA-1}, P_{CS-1}) in general. - The skid resistance of SMA pavement decreases gradually with the increasing P
_{NMSA}and AC; the skid resistance of SMA pavement first decrease and then increase as P_{CS}increases. - For P
_{NMAS-1}and P_{CS-1,}the skid resistance of SMA pavement with different mixture types and levels of factors varies. However, there is no obvious difference between them, indicating that P_{NMAS-1}and P_{CS-1}have insignificant effect on skid resistance of SMA pavement. - When the aggregates sizes are small enough, experimental conditions may have an impact on the skid resistance.
- The best design parameters are proposed, but the results only consider skid resistance. In order to determine optimal design parameters, both skid resistance and pavement performance must be considered.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**The 3D models of pavement texture: (

**a**) the model before filtering; (

**b**) the model after filtering.

**Figure 3.**Relationship between the percentage passing several control sieves and skid resistance of SMA pavement: (

**a**) the influence of P

_{NMAS-1}on skid resistance; (

**b**) the influence of P

_{CS}on skid resistance; (

**c**) the influence of P

_{CS-1}on skid resistance.

Test Items | Unit | Value | Technical Requirements [30] | Specification [29] |
---|---|---|---|---|

25 °C penetration | 0.1 mm | 70.3 | 60–80 | T0604 |

Penetration Index | \ | 0.291 | ≥−0.4 | T0604 |

Softening Point (TR&B) | °C | 88.2 | ≥55 | T0606 |

Ductility at 15 °C | cm | ≥100 | ≥100 | T0605 |

Viscosity at 135 °C | Pa.s | 2.20 | 2.0–3 | T0625 |

Test Items | Unit | Value | Technical Requirements [30] | Specification [31] | ||
---|---|---|---|---|---|---|

10–15 mm | 5–10 mm | 3–5 mm | ||||

Crushing value | % | 13.1 | — | — | ≤26 | T0316 |

Los Angeles abrasion | % | 11.6 | — | — | ≤28 | T0317 |

Apparent relative density | — | 2.753 | 2.776 | 2.772 | ≥2.60 | T0304 |

Bulk relative density | — | 2.692 | 2.633 | 2.701 | T0304 | |

Water absorption | % | 0.285 | 0.46 | 0.41 | ≤2.0 | T0304 |

Flat or elongated | % | 4.4 | 5.3 | — | ≤15 | T0312 |

Test Items | Unit | Value | Technical Requirements [30] | Specification [31] |
---|---|---|---|---|

Apparent relative density | — | 2.775 | ≥2.50 | T0330 |

Mud content (percent of <0.075 mm) | % | 1.3 | ≤3 | T0333 |

Sand equivalent | % | 95.2 | ≥60 | T0334 |

Angularity | s | 57.6 | ≥30 | T0344 |

Test Items | Unit | Value | Technical Requirements [30] | Specification [31] |
---|---|---|---|---|

Apparent relative density | — | 2.719 | ≥2.50 | T0352 |

Water absorption | % | 0.2 | ≤1.0 | T0352 |

Grain sizes <0.6 mm | % | 100.0 | 100 | T0351 |

<0.15 mm | % | 96.0 | 90–100 | |

<0.075 mm | % | 89.3 | 75–100 | |

Hydrophilic coefficient | — | 0.69 | ≤1 | T0354 |

Level i | A | B | C | D | E |
---|---|---|---|---|---|

P_{NMSA}/% | P_{NMSA-1}/% | P_{CS}/% | P_{CS-1}/% | AC/% | |

16 mm | 13.2 mm | 4.75 mm | 2.36 mm | ||

1 | 90 | 65 | 20 | 15 | 5.6 |

2 | 93 | 71 | 24 | 18 | 5.9 |

3 | 96 | 78 | 28 | 21 | 6.2 |

4 | 100 | 85 | 32 | 24 | 6.5 |

Level i | A | B | C | D | E |
---|---|---|---|---|---|

P_{NMSA}/% | P_{NMSA-1}/% | P_{CS}/% | P_{CS-1}/% | AC/% | |

13.2 mm | 9.5 mm | 4.75 mm | 2.36 mm | ||

1 | 90 | 50 | 20 | 15 | 5.6 |

2 | 93 | 58 | 24 | 18 | 5.9 |

3 | 96 | 66 | 29 | 22 | 6.2 |

4 | 100 | 75 | 34 | 26 | 6.5 |

Level i | A | B | C | D | E |
---|---|---|---|---|---|

P_{NMSA}/% | P_{NMSA-1}/% | P_{CS}/% | P_{CS-1}/% | AC/% | |

9.5 mm | 4.75 mm | 2.36 mm | 1.18 mm | ||

1 | 90 | 28 | 20 | 14 | 5.6 |

2 | 93 | 39 | 24 | 18 | 5.9 |

3 | 96 | 50 | 28 | 22 | 6.2 |

4 | 100 | 60 | 32 | 26 | 6.5 |

Level i | A | B | C | D | E |
---|---|---|---|---|---|

P_{NMSA}/% | P_{NMSA-1}/% | P_{CS}/% | P_{CS-1}/% | AC/% | |

4.75 mm | 2.36 mm | 1.18 mm | 0.6 mm | ||

1 | 90 | 28 | 22 | 18 | 5.8 |

2 | 93 | 40 | 27 | 21 | 6.2 |

3 | 96 | 52 | 32 | 24 | 6.6 |

4 | 100 | 65 | 36 | 28 | 7.0 |

Experiment Number | Factors and Levels | ||||
---|---|---|---|---|---|

A | B | C | D | E | |

1 | 1 | 1 | 1 | 1 | 1 |

2 | 1 | 2 | 2 | 2 | 2 |

3 | 1 | 3 | 3 | 3 | 3 |

4 | 1 | 4 | 4 | 4 | 4 |

5 | 2 | 1 | 2 | 3 | 4 |

6 | 2 | 2 | 1 | 4 | 3 |

7 | 2 | 3 | 4 | 1 | 2 |

8 | 2 | 4 | 3 | 2 | 1 |

9 | 3 | 1 | 3 | 4 | 2 |

10 | 3 | 2 | 4 | 3 | 1 |

11 | 3 | 3 | 1 | 2 | 4 |

12 | 3 | 4 | 2 | 1 | 3 |

13 | 4 | 1 | 4 | 2 | 3 |

14 | 4 | 2 | 3 | 1 | 4 |

15 | 4 | 3 | 2 | 4 | 1 |

16 | 4 | 4 | 1 | 3 | 2 |

Experiment Number | A | B | C | D | E | SI |
---|---|---|---|---|---|---|

P_{NMSA}/% | P_{NMSA-1}/% | P_{CS}/% | P_{CS-1}/% | AC/% | ||

1 | 1(90) | 1(65) | 1(20) | 1(15) | 1(5.6) | 4.455 |

2 | 1 | 2(71) | 2(24) | 2(18) | 2(5.9) | 3.932 |

3 | 1 | 3(78) | 3(28) | 3(21) | 3(6.2) | 4.258 |

4 | 1 | 4(85) | 4(32) | 4(24) | 4(6.5) | 3.855 |

5 | 2(93) | 1 | 2 | 3 | 4 | 1.294 |

6 | 2 | 2 | 1 | 4 | 3 | 4.554 |

7 | 2 | 3 | 4 | 1 | 2 | 3.728 |

8 | 2 | 4 | 3 | 2 | 1 | 4.097 |

9 | 3(96) | 1 | 3 | 4 | 2 | 3.872 |

10 | 3 | 2 | 4 | 3 | 1 | 3.537 |

11 | 3 | 3 | 1 | 2 | 4 | 2.353 |

12 | 3 | 4 | 2 | 1 | 3 | 2.714 |

13 | 4(100) | 1 | 4 | 2 | 3 | 2.087 |

14 | 4 | 2 | 3 | 1 | 4 | 1.461 |

15 | 4 | 3 | 2 | 4 | 1 | 2.196 |

16 | 4 | 4 | 1 | 3 | 2 | 1.810 |

k_{1} | 4.13 | 2.93 | 3.29 | 3.12 | 3.57 | |

k_{2} | 3.42 | 3.37 | 2.53 | 3.12 | 3.34 | |

k_{3} | 3.12 | 3.13 | 3.42 | 3.03 | 3.40 | |

k_{4} | 1.89 | 3.12 | 3.30 | 3.31 | 2.24 | |

R | 2.24 | 0.44 | 0.89 | 0.28 | 1.33 | |

Effect order | P_{NMSA} > AC > P_{CS} > P_{NMSA-1} > P_{CS-1} | |||||

Optimum group | A1 + B2 + C3 + D4 + E1/P_{NMSA}(90) + P_{NMSA-1}(71) + P_{CS}(28) + P_{CS-1}(24) + AC(5.6) |

Experiment Number | A | B | C | D | E | SI |
---|---|---|---|---|---|---|

P_{NMSA}/% | P_{NMSA-1}/% | P_{CS}/% | P_{CS-1}/% | AC/% | ||

1 | 1(90) | 1(50) | 1(20) | 1(15) | 1(5.6) | 4.013 |

2 | 1 | 2(58) | 2(24) | 2(18) | 2(5.9) | 3.154 |

3 | 1 | 3(66) | 3(29) | 3(22) | 3(6.2) | 2.893 |

4 | 1 | 4(75) | 4(34) | 4(26) | 4(6.5) | 2.589 |

5 | 2(93) | 1 | 2 | 3 | 4 | 0.510 |

6 | 2 | 2 | 1 | 4 | 3 | 3.778 |

7 | 2 | 3 | 4 | 1 | 2 | 2.462 |

8 | 2 | 4 | 3 | 2 | 1 | 2.831 |

9 | 3(96) | 1 | 3 | 4 | 2 | 2.606 |

10 | 3 | 2 | 4 | 3 | 1 | 2.270 |

11 | 3 | 3 | 1 | 2 | 4 | 1.083 |

12 | 3 | 4 | 2 | 1 | 3 | 1.445 |

13 | 4(100) | 1 | 4 | 2 | 3 | 0.816 |

14 | 4 | 2 | 3 | 1 | 4 | 0.677 |

15 | 4 | 3 | 2 | 4 | 1 | 0.926 |

16 | 4 | 4 | 1 | 3 | 2 | 0.539 |

k_{1} | 3.16 | 1.99 | 2.35 | 1.83 | 2.51 | |

k_{2} | 2.40 | 2.47 | 1.51 | 2.10 | 2.19 | |

k_{3} | 1.85 | 1.84 | 2.25 | 1.89 | 2.23 | |

k_{4} | 0.74 | 1.85 | 2.03 | 2.60 | 1.21 | |

R | 2.42 | 0.63 | 0.84 | 0.77 | 1.30 | |

Effect order | P_{NMSA} > AC > P_{CS} > P_{CS-1} > P_{NMSA-1} | |||||

Optimum group | A1 + B2 + C1 + D4 + E1/P_{NMSA}(90) + P_{NMSA-1}(58) + P_{CS}(20) + P_{CS-1}(26) + AC(5.6) |

Experiment Number | A | B | C | D | E | SI |
---|---|---|---|---|---|---|

P_{NMSA}/% | P_{NMSA-1}/% | P_{CS}/% | P_{CS-1}/% | AC/% | ||

1 | 1(90) | 1(28) | 1(20) | 1(14) | 1(5.6) | 3.912 |

2 | 1 | 2(39) | 2(24) | 2(18) | 2(5.9) | 2.394 |

3 | 1 | 3(50) | 3(28) | 3(22) | 3(6.2) | 1.680 |

4 | 1 | 4(60) | 4(32) | 4(26) | 4(6.5) | 0.696 |

5 | 2(93) | 1 | 2 | 3 | 4 | -0.361 |

6 | 2 | 2 | 1 | 4 | 3 | 1.884 |

7 | 2 | 3 | 4 | 1 | 2 | 1.315 |

8 | 2 | 4 | 3 | 2 | 1 | 1.569 |

9 | 3(96) | 1 | 3 | 4 | 2 | 0.910 |

10 | 3 | 2 | 4 | 3 | 1 | 0.579 |

11 | 3 | 3 | 1 | 2 | 4 | 0.368 |

12 | 3 | 4 | 2 | 1 | 3 | 0.617 |

13 | 4(100) | 1 | 4 | 2 | 3 | 0.185 |

14 | 4 | 2 | 3 | 1 | 4 | -1.153 |

15 | 4 | 3 | 2 | 4 | 1 | 0.260 |

16 | 4 | 4 | 1 | 3 | 2 | 1.003 |

k_{1} | 2.17 | 1.16 | 1.47 | 1.14 | 1.58 | |

k_{2} | 1.10 | 0.93 | 0.73 | 1.11 | 1.41 | |

k_{3} | 0.62 | 0.91 | 0.75 | 0.63 | 1.09 | |

k_{4} | 0.07 | 0.97 | 0.86 | 0.80 | -0.11 | |

R | 2.10 | 0.26 | 0.74 | 0.51 | 1.69 | |

Effect order | P_{NMSA} > AC > P_{CS} > P_{CS-1} > P_{NMSA-1} | |||||

Optimum group | A1 + B1 + C1 + D1 + E1/P_{NMSA}(90) + P_{NMSA-1}(28) + P_{CS}(20) + P_{CS-1}(14) + AC(5.6) |

Experiment Number | A | B | C | D | E | SI |
---|---|---|---|---|---|---|

P_{NMSA}/% | P_{NMSA-1}/% | P_{CS}/% | P_{CS-1}/% | AC/% | ||

1 | 1(90) | 1(28) | 1(22) | 1(18) | 1(5.8) | 1.063 |

2 | 1 | 2(40) | 2(27) | 2(21) | 2(6.2) | 0.847 |

3 | 1 | 3(52) | 3(32) | 3(24) | 3(6.6) | 0.514 |

4 | 1 | 4(65) | 4(36) | 4(28) | 4(7.0) | 0.055 |

5 | 2(93) | 1 | 2 | 3 | 4 | -1.138 |

6 | 2 | 2 | 1 | 4 | 3 | 0.609 |

7 | 2 | 3 | 4 | 1 | 2 | -0.343 |

8 | 2 | 4 | 3 | 2 | 1 | 0.462 |

9 | 3(96) | 1 | 3 | 4 | 2 | -0.154 |

10 | 3 | 2 | 4 | 3 | 1 | 0 |

11 | 3 | 3 | 1 | 2 | 4 | -0.098 |

12 | 3 | 4 | 2 | 1 | 3 | 0.0178 |

13 | 4(100) | 1 | 4 | 2 | 3 | -0.184 |

14 | 4 | 2 | 3 | 1 | 4 | -1.808 |

15 | 4 | 3 | 2 | 4 | 1 | -0.848 |

16 | 4 | 4 | 1 | 3 | 2 | 0.198 |

k_{1} | 0.62 | -0.10 | 0.44 | -0.04 | 0.17 | |

k_{2} | -0.10 | -0.09 | -0.28 | 0.04 | 0.14 | |

k_{3} | -0.06 | -0.19 | -0.25 | -0.21 | 0.24 | |

k_{4} | -0.66 | 0.18 | -0.12 | -0.05 | -0.75 | |

R | 1.28 | 0.38 | 0.72 | 0.24 | 0.99 | |

Effect order | P_{NMSA} > AC > P_{CS} > P_{NMSA-1} > P_{CS-1} | |||||

Optimum group | A1 + B4 + C1 + D2 + E3/P_{NMSA}(90) + P_{NMSA-1}(65) + P_{CS}(22) + P_{CS-1}(21) + AC(6.6) |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Liu, Y.; Cheng, X.; Yang, Z.
Effect of Mixture Design Parameters of Stone Mastic Asphalt Pavement on Its Skid Resistance. *Appl. Sci.* **2019**, *9*, 5171.
https://doi.org/10.3390/app9235171

**AMA Style**

Liu Y, Cheng X, Yang Z.
Effect of Mixture Design Parameters of Stone Mastic Asphalt Pavement on Its Skid Resistance. *Applied Sciences*. 2019; 9(23):5171.
https://doi.org/10.3390/app9235171

**Chicago/Turabian Style**

Liu, Yamin, Xianpeng Cheng, and Zhen Yang.
2019. "Effect of Mixture Design Parameters of Stone Mastic Asphalt Pavement on Its Skid Resistance" *Applied Sciences* 9, no. 23: 5171.
https://doi.org/10.3390/app9235171