# Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar

^{*}

^{†}

## Abstract

**:**

^{2}> 0.95). The corrected modulus was obtained by approximating the error between simulated and measured strains. Then, the finite element analysis was performed to calculate key mechanical index values under various working conditions and predict the fatigue life of asphalt and base layers. Finally, ground-penetrating radar (GPR) detection was performed, and the internal pavement condition index was defined for quantitative assessment of structure conditions. The results show that there is a good correlation between the internal pavement condition index (IPCI) and remaining life of pavement structure. Therefore, our works solve the problems of the parameter reliability of pavement structures and quantitative assessment for structural conditions, which could support the performance prediction and maintenance analysis on asphalt pavement with a semi-rigid base.

## 1. Introduction

- (1)
- To investigate the relationship between pavement temperature and atmospheric temperature in the depth direction.
- (2)
- To analyze the influencing factors of mechanical responses of pavement structure layer and reveal its influencing law.
- (3)
- To explore the establishment of more reliable parameters in FE simulation for fatigue life prediction under more complex conditions.
- (4)
- To reveal the relationship between the pavement structure conditions and remaining life of different structural layers.

## 2. Materials and Method

#### 2.1. Field Investigation

#### 2.1.1. Dynamic Modulus Tests

_{T}denotes the displacement factor, T denotes the test temperature, C

_{1}and C

_{2}represent the fitted parameters, and T

_{s}denotes the reference temperature [42,43].

_{r}represents the adjusted frequency, and α

_{T}, α, β, γ and δ are the regression constants, respectively.

#### 2.1.2. Field Temperature Monitoring

_{ave}denotes the average of the maximum and minimum temperatures during one day, ΔT is the difference between them, ω is the angular frequency, t

_{0}is the initial phase, and a, b, c, and d represent the regression coefficients.

_{0}was obtained by fitting the temperature under different months and depths, the regression analysis was performed again to obtain the initial phase t

_{0}(z) at other z. Similarly, a and b were used to perform the regression analysis again after the first fitting to obtain the initial phase a (z) and b (z), respectively. After obtaining these regression relationships, the average values of a (z), b (z), c (z), and t

_{0}(z) were obtained by means of different z fitting in each month and were functionally fitted with z as an independent variable, respectively.

#### 2.1.3. Accelerated Pavement Testing

_{T}was defined in Equation (5) to calculate the strain transformation corresponding to the change in unit temperature [47].

_{1}and T

_{2}are the minimum and maximum values of the temperature range, and ε

_{1}and ε

_{2}are the corresponding strains, respectively. Combined with the measured strain values, the prediction equation of the strain amplitude (three-way: lateral, longitudinal, and vertical) $\left|{\u2206}_{\mathsf{\epsilon}}\right|$ for the bottom of the measured layers is described in Equation (6) [42].

#### 2.1.4. GPR Investigation

_{i}refers to the area of the ith distress (crack, looseness, and interlayer separation) in a structural layer (m

^{2}). S

_{0}represent the area of the asphalt layer (0.18 × 100 = 18 m

^{2}) or the base layer (0.36 × 100 = 36 m

^{2}), as shown in Figure 4. In this study, only the structural conditions of asphalt surface and CSM base layers were analyzed. GPR investigation was performed for several sections of road on the G15 expressway of Jiangsu province, China, with different service lives. The length of the test road was 5 km, and the pavement structure was the same as in Figure 1. The IPCI index of the structural layers per 100 m of each road section was calculated to analyze the relationship between it and the remaining life of the structural layers.

_{e}(million times) of a single lane under the standard axle load (100 kN). Then, the remaining life ratio (RLR, %) was taken as an evaluation index, which represented the ratio of the service life of existing pavement to the upper service life limit of newly built pavement.

#### 2.2. Numerical Simulation

#### 2.2.1. FE Modelling

^{2}(18.6 cm × 19.2 cm) in this FE model.

#### 2.2.2. Viscoelastic Parameters

_{i}is the material constant, n is the number of items, and τ

_{i}is the delay time [42].

#### 2.2.3. Material Parameter Inversion

_{s}and the measured strains ε

_{M}was obtained according to Equation (15).

#### 2.2.4. Fatigue Life Indexes

_{f}

_{1}and base layer N

_{f}

_{2}was calculated via Equations (16) and (17), respectively.

_{a}denotes the adjusted coefficient of seasonally frozen ground, k

_{b}denotes the coefficient of fatigue loading modes, E

_{a}represents a E* at 20 °C, VFA represents the saturation of asphalt mixtures, k

_{T1}is the adjusted coefficient of temperature, and β is the reliability coefficient.

_{T}

_{2}denotes the adjusted temperature coefficient, σ

_{t}denotes the tension stress at the base layer bottom, R

_{S}represents the tension strength at the base layer, k

_{c}denotes the field comprehensive correction factor, and a and b denote fitted parameters.

## 3. Results and Discussion

#### 3.1. Field Temperature Monitoring

_{0}(z) are presented in Figure 7. Then, the temperature-field prediction model was obtained using Equation (18). The relationship between the measured and fitted temperatures was further explored. The correlation coefficient was above 0.95. This illustrates that the correlation was highly significant, which could predict the pavement structural temperatures.

#### 3.2. Dynamic Responses to Different Influencing Factors

#### 3.2.1. Field Temperature

_{T}. This figure illustrates that the ε

_{T}of vertical compression strain was the largest, indicating that it is most sensitive to temperature changes in different regions. Under the high-temperature condition, the vertical compression strain increased by 59.4 με for every 1 °C temperature increase, being 8.1 times that under the low-temperature condition. The second was the longitudinal tension-strain: it increased by 4.0 με and 18.7 με for each 1 °C increase in temperature under the middle- and high-temperature conditions, respectively. In addition, the longitudinal compression-strain and vertical tension-strain were little affected by temperature, and the ε

_{T}was in the range of 1 to 4 με/°C under three temperature conditions.

#### 3.2.2. Loading Weight

#### 3.2.3. Loading Speed

#### 3.3. Dynamic Response Prediction

#### 3.4. Finite Element Simulation

#### 3.4.1. Results of Material Parameter Inversion

_{M}of the tested road according to Equations (19) and (20) under different working conditions, including the three-way strain at the bottom of the two measured layers.

#### 3.4.2. Prediction Results of Fatigue Failure and Critical Conditions

_{f}

_{1}of the asphalt layer is between 2.24 × 10

^{7}and 1.69 × 10

^{13}equivalent-axle times. In the light of a previous study [41], the maximum shear strain of a typical structure occurred at the top of the middle surface layer (6 cm below the pavement), and the change rule of shear strain with time under the most unfavorable and favorable conditions was further simulated. Figure 13 suggests that the maximum shear strain of the asphalt layer of a semi-rigid pavement ranged from 5.53 με to 133.48 με.

_{f}

_{2}of the base layer is between 5.33 × 10

^{8}and 4.60 × 10

^{10}equivalent-axle times.

#### 3.5. Relationship between Pavement Structural Conditions and Remaining Life

- Linear fitting was performed for the data with IPCI less than 10, IPCI greater than 80 and the intermediate segment, respectively. IPCI and RLR showed a roughly negative relationship: when the IPCI was less than 10, the slope k of the line was small (about 0.2). It was significant larger when the IPCI was greater than 10 (between 1.1 to 1.3); however, it became small again after the IPCI was greater than 80.
- With the increase in traffic grade, the IPCI of different structural layers decreased to different degrees, and the IPCI of the base layer decreased more obviously due to load accumulation. On the other hand, the fitting relationship between IPCI and RLR was slightly weakened, which may be because the thickness of pavement structural layers changes under repeated load, thus affecting the calculation result of IPCI.
- Under the same IPCI value, the RLR of the base layer was lower than that of the asphalt surface layer, and this difference was more evident with the increase in traffic grade. This may be due to the increase in the distress ratio, as the performance of the CSM base material decreases significantly, and the modulus attenuation is greater.

## 4. Conclusions

- (1)
- Temperatures were predicted using a dual sinusoidal model for pavement structures based on the measured atmospheric temperature and structural temperatures. The good linear correlation (coefficient > 0.95) indicates that this model is reliable.
- (2)
- The asphalt surface layer showed a three-way strain increase with increasing temperature and load weight, but a decrease with increasing loading speed. It was excellently correlated with the measured values to predict dynamic responses under multivariate factors.
- (3)
- The material parameter inversion of the asphalt surface layer was proposed by controlling the average error of six strains between the FE-simulated and APT-measured values. Based on the established FE model, key mechanical index values can be analyzed under different conditions, along with the fatigue life of pavement structural layers.
- (4)
- There is a good negative correlation between the IPCI and the RLR of the pavement structure. Therefore, the RLR of the pavement structure can be predicted via GPR detection and quantitative assessment of structure conditions.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Workflow of the APT, laboratory tests, numerical simulations, and GPR detection on pavement.

**Figure 7.**Fitting results of parameters in different months and the relationship between measured temperatures and fitted temperatures.

**Figure 8.**Strain–time-history curves at the bottom of the middle asphalt layer under different temperature fields: (

**a**) longitudinal strain; (

**b**) vertical strain.

**Figure 9.**Index change laws with temperature conditions at the bottom of the middle asphalt layer: (

**a**) three-way ε

_{T}; (

**b**) three-way strain amplitudes.

**Figure 10.**Strain curves at the bottom of the middle asphalt layer: (

**a**) longitudinal and (

**b**) vertical strain.

**Figure 11.**Strain–time-history curves at the bottom of the middle asphalt layer: (

**a**) transverse, (

**b**) longitudinal and (

**c**) vertical strain.

**Figure 12.**The measured and the predicted strain amplitudes: (

**a**) the bottom of the middle layer; (

**b**) the bottom of the lower layer.

**Figure 13.**Maximum shear strain of asphalt layer under (

**a**) the most adverse and (

**b**) most favorable conditions.

**Figure 15.**Relationship between the RLR and IPCI of pavement structures: (

**a**) light, (

**b**) moderate, (

**c**) slightly heavy and (

**d**) heavy traffic.

Bandwidth (MHz) | Detection Depth (m) | Sampling Point in Horizontal Direction | Time Window (ns) | Ranging Method | Signal-To-Noise Ratio | Sampling Interval |
---|---|---|---|---|---|---|

400 MHz (channel 1) 800 MHz (channel 2) | 4.5 m for channel 1 1.5 m for channel 2 | 400 | 26 | DMI | >100 dB | 5 cm |

RLR of pavement (%) | Performance classification | Traffic grades | ||||

Light (N_{e} < 300) | Moderate (300 ≤ N _{e} < 1200) | Slightly heavy (1200 ≤ N _{e} < 2500) | Heavy (N _{e} ≥ 2500) | |||

Excellent | Capital repair | 23.5 | 31.0 | 25.3 | 29.7 | |

Partial repair | 59.2 | 55.7 | 65.6 | 65.5 | ||

Average | Capital repair | 33.7 | 42.5 | 38.3 | 43.4 | |

Partial repair | 69.4 | 67.2 | 78.6 | 79.3 | ||

Poor | Capital repair | 43.9 | 54.0 | 51.3 | 91.6 | |

Partial repair | 79.6 | 78.7 | 91.6 | 93.1 |

Structure Layer | Direction | Loading Speed (km/h) | Temperature (°C) | |||
---|---|---|---|---|---|---|

20 | 30 | 40 | 50 | |||

Bottom of the middle layer at asphalt surface | Vertical | 10 | 177.8 | 376.3 | 796.6 | 1686.2 |

15 | 157.7 | 333.8 | 706.5 | 1495.6 | ||

22 | 133.3 | 282.1 | 597.3 | 1264.3 | ||

Transverse | 10 | 17.3 | 49.5 | 141.6 | 404.5 | |

15 | 15.3 | 43.7 | 124.9 | 356.9 | ||

22 | 12.8 | 36.7 | 104.9 | 299.7 | ||

Longitudinal | 10 | 42.1 | 101.5 | 244.7 | 589.9 | |

15 | 34.7 | 83.5 | 201.4 | 485.4 | ||

22 | 26.4 | 63.6 | 153.2 | 369.5 | ||

Bottom of the lower layer at asphalt surface | Vertical | 10 | 116.1 | 248.3 | 530.9 | 1135.3 |

15 | 100.9 | 215.9 | 461.6 | 986.9 | ||

22 | 83.0 | 177.5 | 379.5 | 811.3 | ||

Transverse | 10 | 8.0 | 20.6 | 52.6 | 134.7 | |

15 | 6.8 | 17.5 | 44.8 | 114.7 | ||

22 | 5.5 | 13.9 | 35.8 | 91.7 | ||

Longitudinal | 10 | 17.8 | 40.4 | 91.8 | 208.3 | |

15 | 16.1 | 36.6 | 83 | 188.5 | ||

22 | 14 | 31.8 | 72.2 | 163.9 |

T (°C) | v (km/h) | Inversion Results and Error | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

20 | 10 | E | 7220 | 7240 | 7260 | 7280 ^{1} | 7300 | 7320 | 7340 | 7360 |

S | 6.3 | 5.5 | 5.1 | 4.6 | 5.2 | 5.9 | 6.7 | 7.9 | ||

15 | E | 7640 | 7660 | 7680 | 7700 | 7720 | 7740 | 7760 | 7780 | |

S | 7.9 | 6.7 | 5.7 | 5.1 | 4.8 | 5.2 | 5.7 | 6.6 | ||

22 | E | 8080 | 8100 | 8120 | 8140 | 8160 | 8180 | 8200 | 8220 | |

S | 7.5 | 6.1 | 5.3 | 4.9 | 5.3 | 5.8 | 6.7 | 8.1 | ||

30 | 10 | E | 2630 | 2640 | 2650 | 2660 | 2670 | 2680 | 2690 | 2700 |

S | 5.7 | 5.2 | 4.8 | 5.1 | 5.8 | 6.7 | 8.1 | 9.5 | ||

15 | E | 2820 | 2830 | 2840 | 2850 | 2860 | 2870 | 2880 | 2890 | |

S | 8.9 | 7.6 | 6.5 | 5.7 | 5.1 | 4.8 | 5.2 | 5.9 | ||

22 | E | 3310 | 3320 | 3330 | 3340 | 3350 | 3360 | 3370 | 3380 | |

S | 5.8 | 5.0 | 4.6 | 4.9 | 5.5 | 6.4 | 7.6 | 8.9 | ||

40 | 10 | E | 940 | 945 | 950 | 955 | 960 | 965 | 970 | 975 |

S | 6.3 | 5.5 | 5.0 | 4.7 | 4.9 | 5.3 | 5.9 | 6.7 | ||

15 | E | 1735 | 1740 | 1745 | 1750 | 1755 | 1760 | 1765 | 1770 | |

S | 5.7 | 5.2 | 4.8 | 5.1 | 5.5 | 6.1 | 6.8 | 7.7 | ||

22 | E | 2200 | 2205 | 2210 | 2215 | 2220 | 2225 | 2230 | 2235 | |

S | 7.2 | 6.1 | 5.2 | 4.9 | 5.1 | 5.5 | 6.1 | 6.9 | ||

50 | 10 | E | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 |

S | 5.2 | 5.0 | 4.8 | 4.9 | 5.1 | 5.4 | 5.7 | 6.1 | ||

15 | E | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | |

S | 5.1 | 5.1 | 5.0 | 4.9 | 4.8 | 4.9 | 5.0 | 5.1 | ||

22 | E | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | |

S | 5.2 | 4.9 | 4.7 | 4.8 | 4.9 | 5.1 | 5.3 | 5.6 |

^{1}The bolded value is the optimal solution.

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## Share and Cite

**MDPI and ACS Style**

Liu, Z.; Yang, Q.; Gu, X.
Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar. *Remote Sens.* **2023**, *15*, 4620.
https://doi.org/10.3390/rs15184620

**AMA Style**

Liu Z, Yang Q, Gu X.
Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar. *Remote Sensing*. 2023; 15(18):4620.
https://doi.org/10.3390/rs15184620

**Chicago/Turabian Style**

Liu, Zhen, Qifeng Yang, and Xingyu Gu.
2023. "Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar" *Remote Sensing* 15, no. 18: 4620.
https://doi.org/10.3390/rs15184620