Calibration of Witczak and Modified Witczak Models for Prediction of Dynamic Modulus of In-Service Asphalt Layers

Document Type : Research Article

Author

Urmia University

Abstract

One of the important input parameters of the Mechanistic-Empirical Pavement Design Guide (MEPDG) for asphalt pavements is the dynamic modulus (|E*|) that can be defined as the viscoelastic property of asphalt materials. For the determination of dynamic modulus of in-service asphalt layers, MEPDG uses results of both Falling Weight Deflectometer (FWD) and laboratory dynamic modulus predictive models. This method in some cases lacks precision. Hence, it is needed to improve the current method and develop accurate predictive models. In this research, ten asphalt pavement sites, having various structures, ages, and conditions, were selected in Khuzestan and Kerman provinces in Iran. Field and laboratory testing were performed and the dynamic modulus of in-service asphalt layers was determined. Developed predictive models for dynamic modulus of asphalt mixes including Witczak and Modified Witczak were calibrated and new models were constructed for predicting in-situ dynamic modulus of asphalt layers. These two calibrated models entitled “In-situ Witczak Model” and “In-situ Modified Witczak Model” could be directly used for the prediction of dynamic modulus of in-service asphalt layers from volumetric properties of asphalt mixes and viscosity characteristics of extracted binders without any need for FWD testing. Performance evaluation and validation of new models showed high accuracy and low bias with a very good correlation between predicted and measured values (R2=0.93).

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[1]    AASHTO. (2001). “AASHTO Pavement Management Guide”, American Association of State Highway and Transportation Officials, Washington, D.C.
[2]    ARA. (2004). “Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures”, NCHRP 1-37A, National Cooperative Highway Research Program, Transportation Research Board, National Research Council, Washington, D.C.
[3]    Solatifar, N. (2018). Analysis of Conventional Dynamic Modulus Predictive Models of Asphalt Mixtures, Amirkabir Journal of Civil Engineering, In press. doi: http://dx.doi.org/10.22060/ceej.2018.15006.5811
[4]    Andrei, D., Witczak, M. W. and Mirza, M. W. (1999). “Development of a Revised Predictive Model for the Dynamic (Complex) Modulus of Asphalt Mixtures”, Inter Team Technical Rep. prepared for the NCHRP 1-37A Project, University of Maryland, College Park, MD.
[5]    Bari, J. and Witczak, M. W. (2006). “Development of a New Revised Version of the Witczak E* Predictive Model for Hot Mix Asphalt Mixtures”, Journal of the Association of Asphalt Paving Technologists, Vol. 75, pp. 381-423.
[6]    Christensen, D. W., Pellinen, T. and Bonaquist, R. F. (2003). “Hirsch Model for Estimating the Modulus of Asphalt Concrete”, Journal of the Association of Asphalt Paving Technologists, Vol. 72, pp. 97-121.
[7]    Al-Khateeb, G., Shenoy, A., Gibson, N. and Harman, T. (2006). “A New Simplistic Model for Dynamic Modulus Predictions of Asphalt Paving Mixtures”, Journal of the Association of Asphalt Paving Technologists, Vol. 75, pp. 1254-1293.
[8]    Sakhaeifar, M. S., Kim, Y. R. and Kabir, P. (2015). “New Predictive Models for the Dynamic Modulus of Hot Mix Asphalt”, Construction and Building Materials, No. 76, pp. 221-231. doi: http://dx.doi.org/10.1016/j.conbuildmat.2014.11.011
[9]    Sakhaeifar, M. S., Underwood, B. S., Ranjithan, S., Kim, Y. R. and Jackson, N. C. (2009). “Application of Artificial Neural Networks for Estimating Dynamic Modulus of Asphalt Concrete”, Transportation Research Record: Journal of the Transportation Research Board, No. 2127, pp. 173–186. doi:       http://dx.doi.org/10.3141/2127-20
[10] Sakhaeifar, M. S., Underwood, B. S., Kim, Y. R., Puccinelli, J. and Jackson, N. (2010). “Development of Artificial Neural Network Predictive Models for Populating Dynamic Moduli of Long-Term Pavement Performance Sections”, Transportation Research Record: Journal of Transportation Research Board, No. 2181, pp. 88–97. doi: http://dx.doi.org/10.3141/2181-10
[11] Ceylan, H., Gopalakrishnan, K. and Kim. S. (2008). “Advanced Approaches to Hot-Mix Asphalt Dynamic Modulus Prediction”, Canadian Journal of Civil Engineering, Vol. 35, No. 7, pp. 699-707. doi: http://dx.doi.org/10.1139/L08-016
[12] Ceylan, H., Schwartz, C. W., Kim. S. and Gopalakrishnan, K. (2009). “Accuracy of Predictive Models for Dynamic Modulus of Hot-Mix Asphalt”, Journal of Materials in Civil Engineering, Vol. 21, No. 6, pp. 286–293. doi: http://dx.doi.org/10.1061/(ASCE)0899-1561(2009)21:6(286)
[13] Seo, J., Kim, Y., Cho, J. and Jeong, S. (2013). “Estimation of In Situ Dynamic Modulus by Using MEPDG Dynamic Modulus and FWD Data at Different Temperatures”, International Journal of Pavement Engineering, Vol. 14, No. 4, pp. 343–353. doi: https://doi.org/10.1080/10298436.2012.664274
[14] Georgouli, K., Pomoni, M., Cliatt, B. and Loizos, A. (2015). “A Simplified Approach for the Estimation of HMA Dynamic Modulus for In Service Pavements”, 6th International Conference ‘Bituminous Mixtures and Pavements’, Thessaloniki, Greece: 10-12 Jun.
[15] Solatifar, N., Kavussi, A., Abbasghorbani, M. and Sivilevičius, H. (2017). “Application of FWD Data in Developing Dynamic Modulus Master Curves of In-Service Asphalt Layers”, Journal of Civil Engineering and Management, Vol. 23, No. 5, pp. 661-671. doi: https://doi.org/10.3846/13923730.2017.1292948
[16] Loulizi, A., Flintsch, G. W. and McGhee, K. (2007). “Determination of the In-place Hot-Mix Asphalt Layer Modulus for Rehabilitation Projects by a Mechanistic-Empirical Procedure”, Transportation Research Record: Journal of the Transportation Research Board, No. 2037, pp. 53–62. doi: https://doi.org/10.3141/2037-05
[17] Kavussi, A., Solatifar, N. and Abbasghorbani, M. (2016). “Mechanistic-Empirical Analysis of Asphalt Dynamic Modulus for Rehabilitation Projects in Iran”, Journal of Rehabilitation in Civil Engineering, Vol. 4, No. 1, pp. 18-29. doi: https://doi.org/10.22075/jrce.2016.488
[18] Solatifar, N., Kavussi, A., Abbasghorbani, M. and Katicha, S. W. (2019). “Development of Dynamic Modulus Master Curves of In-service Asphalt Layers Using MEPDG Models”, Road Materials and Pavement Design. Vol 20, No. 1, pp. 225-243. doi: https://doi.org/10.1080/14680629.2017.1380688
[19] Kutay, E., Chatti, K. and Lei, L. (2011). “Backcalculation of Dynamic Modulus Master Curve from Falling Weight Deflectometer Surface Deflections”, Transportation Research Record: Journal of the Transportation Research Board, No. 2227, pp. 87–96. doi: https://doi.org/10.3141/2227-10
[20] Varma, S., Kutay, M. E. and Levenberg, E. (2013). “Viscoelastic Genetic Algorithm for Inverse Analysis of Asphalt Layer Properties from Falling Weight Deflections”, Transportation Research Record: Journal of the Transportation Research Board, No. 2369, pp. 38–46. doi: https://doi.org/10.3141/2369-05
[21] Varma, S. and Kutay, M. E. (2016). “Backcalculation of Viscoelastic and Nonlinear Flexible Pavement Layer Properties from Falling Weight Deflections”, International Journal of Pavement Engineering, Vol. 17, No. 5, pp. 388–402. doi: https://doi.org/10.1080/10298436.2014.993196
[22] Gopalakrishnan, K., Kim, S., Ceylan, H. and Kaya, O. (2014). “Development of Asphalt Dynamic Modulus Master Curve Using Falling Weight Deflectometer Measurements”, Technical Report: TR-659. Institute for Transportation, Iowa State University.
[23] Gopalakrishnan, K., Kim, S., Ceylan, H. and Kaya, O. (2015). “Use of Neural Networks Enhanced Differential Evolution for Backcalculating Asphalt Concrete Viscoelastic Properties from Falling Weight Deflectometer Time Series Data”, 6th International Conference “Bituminous Mixtures and Pavements”, Thessaloniki, Greece: 10–12 Jun.
[24] Kim, Y. R., Underwood, B. S., Sakhaeifar, M. S., Jackson, N. and Puccinelli, J. (2011). “LTPP Computed Parameter: Dynamic Modulus”, Final Report for Project: DTFH61-02-D-00139, Federal Highway Administration, Washington, D.C.
[25] ASTM. (2009). “Standard Viscosity-Temperature Chart for Asphalts (D2493/D2493M-09)”, West Conshohocken, PA. doi: http://dx.doi.org/10.1520/D2493_D2493M-09
[26] Solatifar, N., Abbasghorbani, M., Kavussi, A. and Sivilevičius, H. (2018). “Prediction of Depth Temperature of Asphalt Layers in Hot Climate Areas”, Journal of Civil Engineering and Management, Vol. 24, No. 7, pp. 516-525. doi: https://doi.org/10.3846/jcem.2018.6162
[27] Ullidtz, P. (2000). “Will Nonlinear Backcalculation Help?”, Nondestructive Testing of Pavements and Backcalculation of Moduli, Third Volume, ASTM STP 1375, S. D. Tayabji and E. O. Lukanen, Eds., American Society for Testing and Materials, West Conshohocken, PA. doi: http://dx.doi.org/10.1520/STP14757S
[28] ASTM. (2011). “Standard Test Methods for Quantitative Extraction of Bitumen from Bituminous Paving Mixtures (D2172/D2172M-11)”, West Conshohocken, PA. doi: http://dx.doi.org/10.1520/D2172_D2172M-11
[29] AASHTO. (2014). “Standard Method of Test for Quantitative Extraction of Asphalt Binder from Hot-Mix Asphalt (HMA)”, AASHTO Designation: T 164-14.
[30] ASTM. (2015). “Standard Test Method for Determining the Rheological Properties of Asphalt Binder Using a Dynamic Shear Rheometer (D7175-15)”, West Conshohocken, PA. doi: http://dx.doi.org/10.1520/D7175-15
[31] AASHTO. (2012). “Standard Method of Test for Determining the Rheological Properties of Asphalt Binder Using a Dynamic Shear Rheometer (DSR)”, AASHTO Designation: T 315-12.
[32] Lytton, R. L., Germann, F. P., Chou, Y. J. and Stoffels, S. M. (1990). “Determining Asphaltic Concrete Pavement Structural Properties by Nondestructive Testing”, National Cooperative Highway Research Program (NCHRP), Report 327, Transportation Research Board, Washington D.C.