Flexible Pavement Distresses Prediction Models using AASHTOWare | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 1, Volume 51, Issue 6, November and December 2023, Page 345-364 PDF (1.03 MB) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2023.211235.1228 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Nedaa Mahran  ; Ghada Moussa ; Hassan Younis
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| Civil Engineering Dept., Faculty of Engineering, Assiut University, Egypt | ||||
| Abstract | ||||
| This study mainly focuses on the implementation of the mechanistic-empirical (M-E) analysis method using AASHTOWare for flexible pavement distresses prediction-models generation. To achieve that four steps were followed. First, the most accurate assessment that shows the combined impact of the most important parameters that affect flexible pavement performance was considered in berforming the AASHTOware runs. In which, 378 design combinations of (3 traffic speed levels × 3 traffic load levels ×3 climatic zones × 7 Surface HMA mixes widely used in Egypt) at two input levels of the MEPDG hierarchy (levels 1 &2) that typically are required for binders and hot-mix-asphalt (HMA) were used. Second, a sensitivity analysis to study the combined effect and impact of the investigated parameters on MEPDG-predicted performance (cracking, rutting, and roughness) was conducted at the two input levels. Third, a Multiple Linear Regression (MLR) as a modeling approach to develop five performance prediction models for flexible pavements based on the MEPDG software results was implemented. The proposed MLR models predicted each distress as a function of climatic factors, the surface HMA properties, different regions' speed levels, and traffic volume levels. Finally, a validation process of the proposed MLR prediction models was conducted. Results indicated that the proposed models yield an overall good prediction, asserting the robustness of the proposed process. This study provides a procedure to develop the performance prediction models of flexible pavements based on the MEPDG approach and in accordance with different regions’ input levels on pavement performance | ||||
| Keywords | ||||
| Pavement Management Systems (PMS); AASHTOware Pavement ME Design; Sensitivity analysis; Prediction distress models; Performance models | ||||
| References | ||||
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[1] Al-Haddad, A. H. (2015). Fatigue evaluation of Iraqi asphalt binders based on the dissipated energy and viscoelastic continuum damage (VECD) approaches. Journal of Civil Engineering and Construction Technology, 6(3), 27-50.
[2] Li, J., Zofka, A., & Yut, I. (2012). Evaluation of dynamic modulus of typical asphalt mixtures in Northeast US region. Road materials and pavement design, 13 (2), 249–265.
[3] Moussa, G. S., & Owais, M. (2020). Pre-trained deep learning for hot-mix asphalt dynamic modulus prediction with laboratory effort reduction. Construction and Building Materials, 265, 120239.
[4] Moussa, G. S., & Owais, M. (2021). Modeling Hot-Mix asphalt dynamic modulus using deep residual neural Networks: Parametric and sensitivity analysis study. Construction and Building Materials, 294, 123589.
[5] Rahman, M. M., & Gassman, S. L. (2018). Data collection experience for preliminary calibration of the AASHTO pavement design guide for flexible pavements in South Carolina. International Journal of Pavement Research and Technology, 11(5), 445-457.
[6] Quintus, H. L. V., Darter, M. I., & Mallela, J. (2007). Recommended Practice for Local Calibration of the ME Pavement Design Guide. Draft report to National Cooperative Highway Research Program, Project.
[7] Caliendo, C. (2012). Local calibration and implementation of the mechanistic-empirical pavement design guide for flexible pavement design. Journal of Transportation Engineering, 138(3), 348-360.
[8] Plescan, E. L., & Plescan, C. (2014). Implementation of mechanistic-empirical pavement design guide ME-PDG in Romania. Bull. Transilvania Univ. Braşov, 7(56), 323-329.
[9] Kaya, O. (2015). Investigation of AASHTOWare Pavement ME Design/Darwin-ME TM performance prediction models for Iowa pavement analysis and design (Doctoral dissertation, Iowa State University).
[10] Rahman, M. S., Podolsky, J. H., & Scholz, T. (2019). Preliminary local calibration of performance prediction models in AASHTOWare pavement ME design for flexible pavement rehabilitation in oregon. Journal of Transportation Engineering, Part B: Pavements, 145(2), 05019002.
[11] Islam, S., Hossain, M., Jones, C. A., Bose, A., Barrett, R., & Velasquez Jr, N. (2019). Implementation of AASHTOWare Pavement ME Design Software for Asphalt Pavements in Kansas. Transportation Research Record, 2673(4), 490-499.
[12] El-Badawy, S. M. (2012). Recommended changes to designs not meeting criteria using the mechanistic-empirical pavement design guide. International Journal of Pavement Research and Technology, 5(1), 54.
[13] Kim, S., Ceylan, H., & Heitzman, M. (2005, August). Sensitivity study of design input parameters for two flexible pavement systems using the mechanistic-empirical pavement design guide. In Proceedings of the 2005 mid-continent transportation research symposium, Ames, Iowa
[14] Mohammad, L. N., Kim, M., Raghavendra, A., & Obulareddy, S. (2014). Characterization of Louisiana asphalt mixtures using simple performance tests and MEPDG (No. FHWA/LA. 11/499). Louisiana. Dept. of Transportation and Development.
[15] Aguib, A. A., & Khedr, S. (2016). The mechanistic-empirical pavement design: An Egyptian perspective. In Functional Pavement Design (pp. 933-942). CRC Press.
[16] Sadek, H. A., Masad, E. A., Sirin, O., Al-Khalid, H., Sadeq, M. A., & Little, D. (2014). Implementation of mechanistic-empirical pavement analysis in the State of Qatar. International Journal of Pavement Engineering, 15(6), 495-511.
[17] Khattab, A. M., El-Badawy, S. M., & Elmwafi, M. (2014). Evaluation of Witczak E* predictive models for the implementation of AASHTOWare-Pavement ME Design in the Kingdom of Saudi Arabia. Construction and Building Materials, 64, 360-369.
[18] Alqaili, A. H., & Alsoliman, H. A. (2017). Preparing data for calibration of mechanistic-empirical pavement design guide in central Saudi Arabia. International Journal of Urban and Civil Engineering, 11(2), 248-255.
[19] Masad, E., Kassem, E., & Little, D. (2011). Characterization of asphalt pavement materials in the State of Qatar: A case study. Road Materials and Pavement Design, 12(4), 739-765.
[20] Bayomy, F., & Safwan, K. (2010). Dynamic characterization of egyptian hot mix asphalt concrete for highway pavement design and evaluation. University of Idaho, US-Egypt Cooperative Research, Final Report, 612630.
[21] Elshaeb, M. A., El-Badawy, S. M., & Shawaly, E. S. A. (2014). Development and impact of the Egyptian climatic conditions on flexible pavement performance. American Journal of Civil Engineering and Architecture, 2(3), 115-121.
[22] Aguib, A. A. (2021). Flexible pavement design AASHTO 1993 versus mechanistic-empirical pavement design.
[23] Chehab, G. R., Chehade, R. H., Houssami, L., & Mrad, R. (2018). Implementation initiatives of the mechanistic-empirical pavement design guide in countries with insufficient design input data–the case of lebanon. In Advancement in the Design and Performance of Sustainable Asphalt Pavements: Proceedings of the 1st GeoMEast International Congress and Exhibition, Egypt 2017 on Sustainable Civil Infrastructures 1 (pp. 147-167). Springer International Publishing.
[24] Ghosh, A., Padmarekha, A., & Krishnan, J. M. (2013). Implementation and proof-checking of mechanistic-empirical pavement design for Indian highways using AASHTOWare Pavement ME Design software. Procedia-Social and Behavioral Sciences, 104, 119-128.
[25] Shakhan, M. R., Topal, A., & ŞENGÖZ, B. (2021). Data collection for implementation of the mechanistic-empirical pavement design guide (MEPDG) in Izmir, Turkey. Teknik Dergi, 32(6), 11361-11380.
[26] Eyada, S. O., & Celik, O. N. (2018). A Plan for the implementation of Mechanistic-Empirical Pavement Design Guide in Turkey. Pertanika Journal of Science & Technology, 26(4).
[27] Transportation Officials. (2008). Mechanistic-empirical pavement design guide: a manual of practice. AASHTO.
[28] Olidis, C., & Hein, D. (2004, September). Guide for the mechanistic-empirical design of new and rehabilitated pavement structures materials characterization: Is your agency ready. In 2004 annual conference of the transportation association of Canada.
[29] H.a.B.N.R. Center, Egyptian Code for Urban and Rural Roads, Road Material and its Tests, Table 2-1-1., 2008, p. p. 101.
[30] General Authority for Roads Bridges and Land Transport (GARBLT), www.garblt.com.eg.
[31] Khattab, A. 2015. “Dynamic modulus predictive models for superpave asphalt concrete mixtures.” M.Sc. thesis, Mansoura Univ.
[32] El-Badawy, S., Abd El-Hakim, R., & Awed, A. (2018). Comparing artificial neural networks with regression models for hot-mix asphalt dynamic modulus prediction. Journal of Materials in Civil Engineering, 30(7), 04018128.
[33] Alghrafy, Y. M., Abd Alla, E. S. M., & El-Badawy, S. M. (2021). Rheological properties and aging performance of sulfur extended asphalt modified with recycled polyethylene waste. Construction and Building Materials, 273, 121771.
[34] Aguib, A. A., & Khedr, S. (2016). The mechanistic-empirical pavement design: An Egyptian perspective. In Functional Pavement Design (pp. 933-942). CRC Press.
[35] Bayomy, F., El-Badawy, S., & Awed, A. (2012). Implementation of the MEPDG for flexible pavements in Idaho (No. FHWA-ID-12-193). Idaho. Transportation Dept..
[37] Di Stefano, J. (2004). A confidence interval approach to data analysis. Forest Ecology and Management, 187(2-3), 173-183. | ||||
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