# Development of Prediction Models for Performance of Flexible Pavements in Kansas with Emphasis on the Effects of Subgrade and Unbound Layers

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## Abstract

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## 1. Introduction

_{R}) of undisturbed soils using soils index properties. Rahman et al. [18] developed performance evaluation models for asphalt concrete (AC) pavements and jointed-plain concrete pavements (JPCP) using multiple regression techniques for different distress indicators including PSI, pavement distress index (PDI), pavement quality index (PQI), and international roughness index (IRI). Osorio-Lird et al. [19] proposed a methodology for the development of urban pavement performance models based on probabilistic trends observed from field evaluations applying Markov chains and Monte Carlo simulation.

#### Objectives and Scope

## 2. Development of Pavement Performance Prediction Models

#### 2.1. Input Data

_{current}is AADTT in the current year and AADTT

_{initial}is AADTT in the initial map year. Additional data were provided for all selected pavement sections including the initial map year and corresponding AADTT, as well as AADTT in the year when DCP tests were conducted. Based on this information TTVGR can be computed and thus AADTT in any year of interest can be calculated. The average AADTT since the last pavement treatment was used as an input into statistical analyses for Clay County. The average value was computed based on AADTT values for years 2012, 2013 and 2014.

#### 2.2. Output Data

#### 2.2.1. Total Rutting

#### 2.2.2. Fatigue Cracking

#### 2.2.3. Transverse Cracking

#### 2.2.4. Pavement Roughness

#### 2.3. Type of Analyses

## 3. Results

#### 3.1. PCRA Analysis

#### 3.2. RA Analysis

#### 3.3. MPCRA Analysis

_{min}is minimum DPI value (mm/blow) in depth 0 to 6.3 cm for a given segment, DPI2

_{min}is minimum DPI value (mm/blow) in depth 6.3 to 12.7 cm, DPI3

_{min}is minimum DPI value (mm/blow) in depth 12.7 to 19.05 cm, DPI4

_{min}is minimum DPI value (mm/blow) in depth 19.05 to 25.4 cm and DPI5

_{min}is minimum DPI value (mm/blow) in depth 25.4 to 31.75 cm.

_{min}is minimum CBR value (%) in depth 0 to 6.3 cm for a given segment, CBR2

_{min}is minimum CBR value (%) in depth 6.3 to 12.7 cm, CBR3

_{min}is minimum CBR value (%) in depth 12.7 to 19.05 cm, CBR4

_{min}is minimum CBR value (%) in depth 19.05 to 25.4 cm and CBR5

_{min}is minimum CBR value (%) in depth 25.4 to 31.75 cm.

#### 3.4. MRA Analysis

#### 3.5. Applications and Implementations

#### 3.5.1. Rutting

#### 3.5.2. Core Quality

## 4. Discussion

#### 4.1. DPI Correlations

#### 4.2. AADTT Correlations

#### 4.3. Adjusted R-Squared Values

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Schwartz, C.W.; Li, R.; Ceylan, H.; Kim, S.; Gopalakrishnan, K. Global sensitivity analysis of mechanistic–empirical performance predictions for flexible pavements. Transp. Res. Rec.
**2013**, 2368, 12–23. [Google Scholar] [CrossRef] [Green Version] - Waseem, A.; Yuan, X.X. Longitudinal local calibration of MEPDG permanent deformation models for reconstructed flexible pavements using PMS data. Int. J. Pavement Res. Technol.
**2013**, 6, 304–312. [Google Scholar] - Orobio, A.; Zaniewski, J.P. Sampling-based sensitivity analysis of the mechanistic–empirical pavement design guide applied to material inputs. Transp. Res. Rec.
**2011**, 2226, 85–93. [Google Scholar] [CrossRef] - Baus, R.L.; Stires, N.R. Mechanistic-Empirical Pavement Design Guide Implementation; Report No. GT006-10; Report Number: FHWA-SC-10-01; Department of Civil and Environmental Engineering, University of South Carolina: Columbia, SC, USA, 2010. [Google Scholar]
- Transportation Officials. Mechanistic-Empirical Pavement Design Guide: A Manual of Practice; AASHTO: Washington, DC, USA, 2008. [Google Scholar]
- Puppala, A.J.; Saride, S.; Chomtid, S. Experimental and modeling studies of permanent strains of subgrade soils. J. Geotech. Geoenviron. Eng.
**2009**, 135, 1379–1389. [Google Scholar] [CrossRef] - Meegoda, J.N.; Gao, S. Roughness progression model for asphalt pavements using long-term pavement performance data. J. Transp. Eng.
**2014**, 140, 04014037. [Google Scholar] [CrossRef] - Madanat, S. Incorporating inspection decisions in pavement management. Transp. Res. Part B Methodol.
**1993**, 27, 425–438. [Google Scholar] [CrossRef] - Johnson, K.D.; Cation, K.A. Performance prediction development using three indexes for North Dakota pavement management system. Transp. Res. Rec.
**1992**, 1344, 22–30. [Google Scholar] - Chan, P.K.; Oppermann, M.C.; Wu, S.S. North Carolina’s experience in development of pavement performance prediction and modeling. Transp. Res. Rec.
**1997**, 1592, 80–88. [Google Scholar] [CrossRef] - DeLisle, R.R.; Sullo, P.; Grivas, D.A. Network-level pavement performance prediction model incorporating censored data. Transp. Res. Rec.
**2003**, 1853, 72–79. [Google Scholar] [CrossRef] - Prozzi, J.A.; Madanat, S.M. Development of pavement performance models by combining experimental and field data. J. Infrastruct. Syst.
**2004**, 10, 9–22. [Google Scholar] [CrossRef] - Kim, S.H.; Kim, N. Development of performance prediction models in flexible pavement using regression analysis method. KSCE J. Civ. Eng.
**2006**, 10, 91–96. [Google Scholar] [CrossRef] - Mills, L.N.; Attoh-Okine, N.O.; McNeil, S. Developing pavement performance models for Delaware. Transp. Res. Rec.
**2012**, 2304, 97–103. [Google Scholar] [CrossRef] - Henning, T.F.; Costello, S.B.; Dunn, R.C.; Parkman, C.C.; Hart, G. The establishment of a long-term pavement performance study on the New Zealand state highway network. Road Transp. Res.
**2004**, 13, 17–32. [Google Scholar] - Isa, A.H.; Ma’some, D.M.; Hwa, L.T. Pavement performance model for federal roads. In Proceedings of the Eastern Asia Society for Transportation Studies, Bangkok, Thailand, 21–24 September 2005; pp. 428–440. [Google Scholar]
- Rahman, M.M.; Uddin, M.M.; Gassman, S.L. Pavement performance evaluation models for South Carolina. KSCE J. Civ. Eng.
**2017**, 21, 2695–2706. [Google Scholar] [CrossRef] - Rahman, M.M.; Gassman, S.L. Effect of resilient modulus of undisturbed subgrade soils on pavement rutting. Int. J. Geotech. Eng.
**2019**, 13, 152–161. [Google Scholar] [CrossRef] - Osorio-Lird, A.; Chamorro, A.; Videla, C.; Tighe, S.; Torres-Machi, C. Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Struct. Infrastruct. Eng.
**2018**, 14, 1169–1181. [Google Scholar] [CrossRef] - Qadir, A.; Gazder, U. Statistical analysis for comparing and predicting rutting resistance of asphalt pavements with rigid and flexible geogrid layers. Constr. Build. Mater.
**2021**, 302, 124136. [Google Scholar] [CrossRef] - Ismael, M.Q.; Fattah, M.Y.; Jasim, A.F. Improving the rutting resistance of asphalt pavement modified with the carbon nanotubes additive. Ain Shams Eng. J.
**2021**, 12, 3619–3627. [Google Scholar] [CrossRef] - Zachariah, J.P.; Sarkar, P.P.; Pal, M.A. Study on the moisture damage and rutting resistance of polypropylene modified bituminous mixes with crushed brick aggregate wastes. Constr. Build. Mater.
**2021**, 269, 121357. [Google Scholar] [CrossRef] - Shanbara, H.K.; Ruddock, F.; Atherton, W. Predicting the rutting behavior of natural fibre-reinforced cold mix asphalt using the finite element method. J. Infrastruct. Preserv. Resil.
**2018**, 167, 907–917. [Google Scholar] - Alimohammadi, H.; Zheng, J.; Buss, A.; Schaefer, V.R.; Zheng, G. Rutting Performance Evaluation of Hot Mix Asphalt and Warm Mix Asphalt Mixtures by Using Dynamic Modulus, Hamburg Wheel Tracking Tests, and Viscoelastic Finite Element Simulations. In Proceedings of the International Conference on Transportation and Development, Seattle, WA, USA, 26–29 May 2020. [Google Scholar]
- Al-Rub, R.K.A.; Darabi, M.K.; Huang, C.-W.; Masad, E.A.; Little, D.N. Comparing finite element and constitutive modelling techniques for predicting rutting of aslphalt pavements. J. Pavement Eng.
**2012**, 13, 332–338. [Google Scholar] - Lu, Y.; Hajj, R. Investigation of flexible pavement maintenance patching factors using a finite element model. J. Infrastruct. Preserv. Resil.
**2021**, 2, 30. [Google Scholar] [CrossRef] - Herrick, J.E.; Jones, T.L. A dynamic cone penetrometer for measuring soil penetration resistance. Soil Sci. Soc. Am. J.
**2002**, 66, 1320–1324. [Google Scholar] [CrossRef] - Siekmeier, J.; Pinta, C.; Merth, S.; Jensen, J.; Davich, P.; Camargo, F.F.; Beyer, M. Using the Dynamic Cone Penetrometer and Light Weight Deflectometer for Construction Quality Assurance; Technical Report for Minnesota Department of Transportation; Office of Materials and Road Research: Saint Paul, MN, USA, 2009. [Google Scholar]
- Luo, X.; Gu, F.; Zhang, Y.; Lytton, R.L.; Zollinger, D. Mechanistic-empirical models for better consideration of subgrade and unbound layers influence on pavement performance. Transp. Geotech.
**2017**, 13, 52–68. [Google Scholar] [CrossRef] [Green Version] - Lee, J.S.; Kim, S.Y.; Hong, W.T.; Byun, Y.H. Assessing subgrade strength using an instrumented dynamic cone penetrometer. Soils Found.
**2019**, 59, 930–941. [Google Scholar] [CrossRef] - Mohammad, L.N.; Gaspard, K.; Herath, A.; Nazzal, M.D. Comparative Evaluation of Subgrade Resilient Modulus from Nondestructive, In-Situ, and Laboratory Methods; No. FHWA/LA. 06/417; Louisiana Transportation Research Center: Baton Rouge, LA, USA, 2007. [Google Scholar]
- Ikechukwu, A.F.; Mostafa, M.H. Performance assessment of pavement structure using dynamics cone penetrometer (DCP). Int. J. Pavement Res. Technol.
**2020**, 13, 466–476. [Google Scholar] [CrossRef] - Powell, W.D.; Potter, J.F.; Mayhew, H.C.; Nunn, M.E. The Structural Design of Bituminous Roads; Transport and Road Research Laboratory: Wokingham, UK, 1984. [Google Scholar]
- Webster, S.L.; Brown, R.W.; Porter, J.R. Force Projection Site Evaluation Using the Electric Cone Penetrometer (ECP) and the Dynamic Cone Penetrometer (DCP); Technical Report for US Army Corps of Engineers: Vicksburg, MS, USA, 1994. [Google Scholar]
- Jahren, C.T.; Ellsworth, B.J.; Bergeson, K. Constructability Test for Cold In-Place Asphalt Recycling. J. Constr. Eng. Manag.
**1999**, 125, 325–329. [Google Scholar] [CrossRef] - Shahin, M.Y. Pavement Management for Airports, Roads and Parking Lots; Springer: New York, NY, USA, 2005; pp. 1–572. [Google Scholar]
- Federal Highway Administration (FHWA). Pavement Smoothness Methodologies; FHWA-HRT-04-061 145-91; FHWA: McLean, VA, USA, 2004.
- Jolliffe, I.T. A note on the use of principal components in regression. J. R. Stat. Soc. C
**1982**, 31, 300–303. [Google Scholar] [CrossRef] - Draper, N.R.; Smith, H. Applied Regression Analysis; John Wiley & Sons: Hoboken, NJ, USA, 1998; pp. 1–736. [Google Scholar]
- Sun, J. A multivariate principal component regression analysis of NIR data. J. Chemom.
**1996**, 10, 1–9. [Google Scholar] [CrossRef] - Mardia, K.; Kent, J.; Bibby, J. Multivariate Analysis; Academic Press: Amsterdam, The Netherlands, 1979; pp. 1–521. [Google Scholar]
- Kansas Department of Transportation (KDOT). Geotechnical Manual; Kansas Department of Transportation (KDOT): Topeka, KS, USA, 2007.
- Shahji, S. Sensitivity Analysis of AASHTO’s 2002 Flexible and Rigid Pavement Design Methods. Master’s Thesis, University of Central Florida, Orlando, FL, USA, 2002. [Google Scholar]
- Rahman, M.M. Characterization of Subgrade Resilient Modulus for MEPDG and the Effects on Pavement Rutting. Ph.D. Dissertation, University of South Carolina, Columbia, SC, USA, 2017. [Google Scholar]
- Masad, S.A.; Little, D.N. Sensitivity Analysis of Flexible Pavement Response and AASHTO 2002 Design Guide to Properties of Unbound Layers; International Center for Aggregate Research; The University of Texas at Austin: Austin, TX, USA, 2004. [Google Scholar]

**Figure 1.**Location of counties where flexible pavement sections were selected for statistical analyses.

**Figure 4.**Transverse cracking codes ne (TC1), two (TC2) and three (TC3) versus time for Clay County.

**Figure 6.**Time since the last treatment required for unit increase in rutting severity code versus average DPI1.

**Figure 7.**Time since the last treatment required for unit increase in rutting severity code versus average CBR1.

County | Location | Thickness of Unbound Layer (mm) | DCP Year | Year of Last Treatment |
---|---|---|---|---|

Cherokee | US 166, EB & WB | 152.4 | 2017 | 2016 |

Clay | US 24, EB & WB | 0 | 2015 | 2011 |

Douglas | KS 10, EB & WB | 0 | 2016 | 2013 |

Ford | US 50, EB & WB | 0 | 2017 | 2012 |

Gove | I 70, EB & WB | 0 | 2017 | 2009 |

Harper | US 160, EB & WB | 0 | 2017 | 2007 |

Johnson3 | I 435, NB & SB | 0 | 2016 | 2013 |

Reno | KS 14, EB & WB | 0 | 2017 | 2012 |

Shawnee1 | US 24, EB & WB | 0 | 2014 | 2012 |

Shawnee2 | US 24, WB | 101.6 | 2014 | 2012 |

Thomas | I 70, EB & WB | 0 | 2017 | 2011 |

Output Data | Input Data | |||||
---|---|---|---|---|---|---|

↑DPI | $\uparrow \overline{\mathit{A}\mathit{A}\mathit{D}\mathit{T}\mathit{T}}$ | ↑Th | Adjusted R-Square | Analysis Type | Remark | |

$\dot{Rt}$ | $\uparrow \alpha =0.01$ $p\le 0.00696$ | N/A | N/A | 0.5473 | PCRA | 21 data points, mean DPI for each depth |

$\dot{Rt}$ | $\uparrow \alpha =0.01$ $p\le 0.0001$ | N/A | N/A | 0.6504 | RA | 21 data points, mean DPI1 |

$\overline{FC1}$ | $\downarrow \alpha =0.05$ $p=0.01613$ | N/A | N/A | 0.2984 | MPCRA | 21 data points, minimum DPI for each depth |

$\overline{TC1}$ | N/A | $\uparrow \alpha =0.05$ $p=0.0329$ | N/A | 0.1698 | MPCRA | 21 data points, minimum DPI for each depth |

$\overline{TC2}$ | N/A | $\uparrow \text{}\alpha =0.01$ $p=0.00243$ | N/A | 0.3732 | MPCRA | 21 data points, minimum DPI for each depth |

$\overline{IRI}$ | N/A | N/A | $\downarrow \alpha =0.01$ $p\le 0.0001$ | 0.6279 | MPCRA | 21 data points, minimum DPI for each depth |

%GC | $\uparrow \alpha =0.01$ $p\le 0.0001$ | N/A | $\uparrow \alpha =0.01$ $p\le 0.0001$ | 0.159 | MRA | 146 data points, separately considering DPI 5 |

%PC | $\downarrow \alpha =0.01$ $p\le 0.00057$ | N/A | $\downarrow \alpha =0.01$ $p\le 0.00057$ | 0.1772 | MRA | 146 data points, separately considering DPI 5 |

Output Data | Input Data | ||||||
---|---|---|---|---|---|---|---|

DPI1 ↑ | DPI2 ↑ | DPI3 ↑ | DPI4 ↑ | DPI5 ↑ | DPI Selection | Adjusted R-Squared | |

$\dot{Rt}$-PCRA | Y ↑ | Y ↑ | Y ↑ | Y ↑ | Y ↑ | mean DPI for each depth (21 data points) | 0.5473 |

$\dot{Rt}$-RA | Y ↑ | N | N | N | N | mean DPI1 (21 data points) | 0.6504 |

$\overline{FC1}$-MPCRA | Y ↓ | Y ↓ | Y ↓ | Y ↓ | Y ↓ | min DPI for each depth (21 data points) | 0.2984 |

%GC-MRA | N | N | N | N | Y ↑ | separately considering each individual DPI 5 (146 data points) | 0.1590 |

%PC-MRA | N | N | N | N | Y ↓ | separately considering each individual DPI 5 (146 data points) | 0.1772 |

No. | County | Bound | Number of DCP Tests |
---|---|---|---|

1 | CHEROKEE | EB | 11 |

2 | CHEROKEE | WB | 12 |

3 | CLAY | EB | 10 |

4 | CLAY | WB | 11 |

5 | DOUGLAS | EB | 7 |

6 | DOUGLAS | WB | 10 |

7 | FORD | EB | 3 |

8 | FORD | WB | 3 |

9 | GOVE | EB | 9 |

10 | GOVE | WB | 6 |

11 | HARPER | EB | 5 |

12 | HARPER | WB | 4 |

13 | JOHNSON3 | NB | 5 |

14 | JOHNSON3 | SB | 5 |

15 | RENO | EB | 13 |

16 | RENO | WB | 7 |

17 | SHAWNEE1 | EB | 3 |

18 | SHAWNEE1 | WB | 2 |

19 | SHAWNEE2 | WB | 2 |

20 | THOMAS | EB | 8 |

21 | THOMAS | WB | 10 |

Outcome | Used Predictors | Adjusted R-Squared |
---|---|---|

Good Core (%) | DCP5 & Thickness | 0.159 |

Poor Core (%) | DCP5 & Thickness | 0.1772 |

Good Core (%) | DCP5 | 0.04271 |

Poor Core (%) | DCP5 | 0.04364 |

Good Core (%) | Thickness | 0.0964 |

Poor Core (%) | Thickness | 0.1119 |

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**MDPI and ACS Style**

Perić, D.; Goh, G.; Saeidaskari, J.; Rashk Olia, A.S.; Ayar, P.
Development of Prediction Models for Performance of Flexible Pavements in Kansas with Emphasis on the Effects of Subgrade and Unbound Layers. *Sustainability* **2022**, *14*, 9020.
https://doi.org/10.3390/su14159020

**AMA Style**

Perić D, Goh G, Saeidaskari J, Rashk Olia AS, Ayar P.
Development of Prediction Models for Performance of Flexible Pavements in Kansas with Emphasis on the Effects of Subgrade and Unbound Layers. *Sustainability*. 2022; 14(15):9020.
https://doi.org/10.3390/su14159020

**Chicago/Turabian Style**

Perić, Dunja, Gyuhyeong Goh, Javad Saeidaskari, Arash Saeidi Rashk Olia, and Pooyan Ayar.
2022. "Development of Prediction Models for Performance of Flexible Pavements in Kansas with Emphasis on the Effects of Subgrade and Unbound Layers" *Sustainability* 14, no. 15: 9020.
https://doi.org/10.3390/su14159020