A mechanistic perspective for airfield pavements evaluation focusing on the asphalt layers’ behaviour

References

• Ahmed, A. and Erlingsson, S., 2016. Viscoelastic response modeling of a pavement under moving load. Transportation Research Procedia, 14, 748–757.
   Google Scholar
• Al-Qadi, I.L., et al., 2008. Accuracy of current complex modulus selection procedure from vehicular load pulse. Transportation Research Record, 2087, 81–90.
   Google Scholar
• Andrei, D., Witczak, M.W., and Mirza, W., 1999. Appendix CC-4: Development of a revised predictive model for the dynamic (complex) modulus of asphalt mixtures. Development of the 2002 guide for the design of new and rehabilitated pavement structures. Washington, DC: Transportation Research Board of the National Academies, 66–204. Final Document, NCHRP Project1-37A.
   Google Scholar
• ARA (Applied Research Associates), 2004a. Guide for mechanistic empirical design of new and rehabilitated pavement structures. Washington, DC: Transportation Research Board, National Research Council, NCHRP 1-37A.
   Google Scholar
• ARA (Applied Research Associates), 2004b. Appendix CC-3: updated traffic frequency calculation for asphalt layers. Guide for mechanistic–empirical design of new and rehabilitated pavement structures. Washington, DC: Transportation Research Board of the National Academies, Final Report, NCHRP Project 1-37A. Available from: http://www.trb.org/mepdg/guide.htm.
   Google Scholar
• 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, 75, 381–417.
   Google Scholar
• Bell, H.P., et al., 2012. Evaluation of remaining fatigue life model for hot-mix asphalt airfield pavements. International Journal of Pavement Engineering, 13 (4), 281–296.
   Web of Science ®Google Scholar
• Birgisson, B., Sholar, G., and Roque, R., 2005. Evaluation of a predicted dynamic modulus for Florida mixtures. Transportation Research Record, 1929, 200–207.
   Google Scholar
• Carpenter, S.H., Ghuzlan, K., and Shen, S., 2003. A fatigue endurance limit for highway and airport pavement. Transportation Research Record, 1832 (1), 131–138.
   Google Scholar
• Chabot, A., et al., 2010. Viscoroute 2.0: a tool for the simulation of moving load effects on asphalt pavement. Road Materials and Pavement Design, 11 (2), 227–250.
   Web of Science ®Google Scholar
• Crook, A.L., Montgomery, S.R., and Guthrie, W.S., 2012. Use of falling weight deflectometer data for network-level flexible pavement management. Transportation Research Record, 2304 (1), 75–85.
   Google Scholar
• CROW, 1998. Crow record: deflection profile-not a pitfall anymore. Survey and interpretation methodology-falling weight deflection measurements. Crow, EDE.
   Google Scholar
• Doerr, L., et al., 2019. How new airport infrastructure promotes tourism: evidence from a synthetic control approach in German regions. Regional Studies, 54 (10), 1402–1412.
   Web of Science ®Google Scholar
• Elseifi, M.A., Al-Qadi, I.L., and Yoo, P.J., 2006. Viscoelastic modeling and field validation of flexible pavements. Journal of Engineering Mechanics, 132, 172–178.
   Web of Science ®Google Scholar
• FAA (Federal Aviation Administration), 2011. Use of nondestructive testing in the evaluation of airport pavements. U.S. Department of Transportation, Advisory Circular 150/5370-11B.
   Google Scholar
• FAA (Federal Aviation Administration), 2016. Airport pavement design and evaluation. U.S. Department of Transportation, Advisory Circular 150/5320-6F.
   Google Scholar
• FAARFIELD (Federal Aviation Administration Rigid and Flexible Iterative Elastic Layer Design program), 2017. Version 1.42.
   Google Scholar
• Garcia, G. and Thompson, M.R., 2008. Strain and pulse duration considerations for extended-life hot-mix asphalt pavement design. Transportation Research Record, 2087, 3–11.
   Google Scholar
• Georgouli, K., et al., 2015. A simplified approach for the estimation of HMA dynamic modulus for in service pavements. In: Proceedings of the 6th international conference on bituminous mixtures and pavements (ICONFBMP), 10–12 June, Thessaloniki, Greece. Taylor & Francis Group, 661–670.
   Google Scholar
• Georgouli, K., Loizos, A., and Plati, C., 2016. Calibration of dynamic modulus predictive model. Construction and Building Materials, 102, 65–75.
   Web of Science ®Google Scholar
• Ghanizadeh, A.R. and Fakhri, M., 2014. Prediction of frequency for simulation of asphalt mix fatigue tests using MARS and ANN. The Scientific World Journal, 2014, Article ID 515467. doi:10.1155/2014/515467.
   Google Scholar
• Gkyrtis, K., Loizos, A., and Plati, C., 2021a. A mechanistic framework for field response assessment of asphalt pavements. International Journal of Pavement Research and Technology, 14 (2), 174–185.
   Google Scholar
• Gkyrtis, K., Loizos, A., and Plati, C., 2021b. Integrating pavement sensing data for pavement condition evaluation. Sensors, 21 (9), 3104.
   PubMed Web of Science ®Google Scholar
• Hernandez, J.A. and Al-Qadi, I.L., 2015. Airfield pavement response caused by heavy aircraft takeoff. Transportation Research Record, 2471, 40–47.
   Google Scholar
• Irfan, M., et al., 2015. Framework for airfield pavements management—an approach based on cost-effectiveness analysis. European Transport Research Review, 7, 13.
   Web of Science ®Google Scholar
• Kavinmathi, K. and Atul Narayan, S.P., 2019. Viscoelastic response of asphalt pavements subjected to dynamic loading. In: Proceedings of the international airfield and highway pavements conference 2019, 21-24 July, Chicago, IL, 101–110.
   Google Scholar
• Koohmishi, M., 2013. Comparison of pavement layers responses with considering different models for asphalt concrete viscoelastic properties. Slovak Journal of Civil Engineering, 21, 15–20.
   Google Scholar
• Loizos, A., et al., 2019. New challenges in evaluating bearing capacity of airfield pavements. In: Proceedings of the international airfield and highway pavements conference, Transportation & Development Institute (T&DI), 21–24 July. Chicago, IL: American Society of Civil Engineers (ASCE), 279–289.
   Google Scholar
• Loizos, A., Armeni, A., and Plati, C., 2017. Evaluation of airfield pavements using FAARFIELD. In: Proceedings of the international conference on highway pavements and airfield technology, Transportation & Development Institute (T&DI), 27–30 August. Philadelphia, PA: American Society of Civil Engineers (ASCE), 82–91.
   Google Scholar
• Loizos, A., Gkyrtis, K., and Plati, C., 2020. Modelling asphalt pavement responses based on field and laboratory data. In: A. Chabot et al. eds. Accelerated pavement testing to transport infrastructure innovation, 6th international conference on accelerated pavement testing (APT), 27–29 September 2021, France, LNCE, Vol. 96, 438–447.
   Google Scholar
• Losa, M. and Di Natale, A., 2012. Evaluation of representative loading frequency for linear elastic analysis of asphalt pavements. Transportation Research Record, 2305, 150–161.
   Web of Science ®Google Scholar
• Loulizi, A., Flintsch, G.W., and McGhee, K., 2007. Determination of in-place hot-mix asphalt layer modulus for rehabilitation projects by a mechanistic–empirical procedure. Transportation Research Record, 2037, 53–62.
   Web of Science ®Google Scholar
• Maggiore, C., Airey, G., and Marsac, P., 2014. A dissipated energy comparison to evaluate fatigue resistance using 2-point bending. Journal of Traffic and Transportation Engineering (English Edition), 1 (1), 49–54.
   Google Scholar
• Marecos, V., et al., 2017. Evaluation of a highway pavement using non-destructive tests: falling weight deflectometer and ground penetrating radar. Construction and Building Materials, 154, 1164–1172.
   Web of Science ®Google Scholar
• Mejlun, L., Judycki, J., and Dolzycki, B., 2017. Comparison of elastic and viscoelastic analysis of asphalt pavement at high temperature. Procedia Engineering, 172, 746–753.
   Google Scholar
• Mirza, M.W. and Witczak, M.W., 1995. Development of a global aging system for short and long term aging of asphalt cements. Journal of the Association of Asphalt Paving Technologists, 64, 393–418.
   Google Scholar
• Monismith, C.L. and Deacon, J.A., 1969. Fatigue of asphalt paving mixtures. Transportation Engineering Journal of ASCE, 95 (2), 317–346.
   Google Scholar
• NCHRP, 2004. Final report: guide for mechanistic-empirical design of new and rehabilitated pavement structures, part 2: design inputs, chapter 2: material characterization. Washington, DC: Transportation Research Board National Council, NCHRP 1–37A Project.
   Google Scholar
• Oh, J.H., et al., 2012. Correlation of asphalt concrete layer moduli determined from laboratory and nondestructive field tests. Journal of Transportation Engineering, 138 (3), 361–370.
   Google Scholar
• Oshone, M., et al., 2017. Prediction of phase angles from dynamic modulus data and implications for cracking performance evaluation. Road Materials and Pavement Design, 18, 491–513.
   Web of Science ®Google Scholar
• Pellinen, T.K., Witczak, M.W., and Bonaquist, R.F., 2004. Asphalt mix master curve construction using sigmoidal fitting function with non-linear least squares optimization. In: Erol Tutumluer, Yacoub M. Najjar, and Eyad Masad, eds. Recent advances in materials characterization and modeling of pavement systems. New York: American Society of Civil Engineers, 83–101.
   Google Scholar
• Plati, C., Gkyrtis, K., and Loizos, A., 2021. Integrating non-destructive testing data to produce asphalt pavement critical strains. Nondestructive Testing and Evaluation, 36 (5), 546–570.
   Web of Science ®Google Scholar
• Pronk, A.C., 2005. The Huet–Sayegh model: a simple and excellent rheological model for master curves of asphaltic mixes. In Proceedings of the R. Lytton symposium on mechanics of flexible pavements, Baton Rouge, LA, 73–82.
   Google Scholar
• Redles, T.A., et al., 2018. Estimating fatigue endurance limits of flexible airfield pavements. International Journal of Pavement Engineering, 19 (6), 534–542.
   Web of Science ®Google Scholar
• Saboo, N., Pratim Das, B., and Kumar, P., 2016. New phenomenological approach for modeling fatigue life of asphalt mixes. Construction and Building Materials, 121, 134–142.
   Web of Science ®Google Scholar
• Seo, J., et al., 2013. Estimation of in situ dynamic modulus by using MEPDG dynamic modulus and FWD data at different temperatures. International Journal of Pavement Engineering, 14 (4), 343–353.
   Web of Science ®Google Scholar
• Shen, S., et al., 2006. A dissipated energy approach to fatigue evaluation. Road Materials and Pavement Design, 7 (1), 47–69.
   Web of Science ®Google Scholar
• Shen, S. and Carpenter, S.H., 2007. Development of an asphalt fatigue model based on energy principles. Journal of the Association of Asphalt Paving Technologists, 76, 525–574.
   Google Scholar
• Siddhartan, R., Krishnamenon, N., and Sebaaly, P., 2000. Pavement response evaluation using finite-layer approach. Transportation Research Record, 1709, 43–49.
   Google Scholar
• Solatifar, N., et al., 2019. Development of dynamic modulus master curves of in-service asphalt layers using MEPDG models. Road Materials and Pavement Design, 20 (1), 225–243.
   Web of Science ®Google Scholar
• Solatifar, N., Kavussi, A., and Abbasghorbani, M., 2021. Dynamic modulus predictive models for In-service asphalt layers in hot climate areas. Journal of Materials in Civil Engineering, 33 (2), 0402038: 1–14.
   Web of Science ®Google Scholar
• Ulloa, A., et al., 2013. Equivalent loading frequencies for dynamic analysis of asphalt pavements. Journal of Materials in Civil Engineering, 25 (9), 1162–1170.
   Web of Science ®Google Scholar
• Wang, H., et al., 2020. Multi-wheel gear loading effect on load-induced failure potential of airfield flexible pavement. International Journal of Pavement Engineering, 21 (6), 805–816.
   Web of Science ®Google Scholar