References
- Ai, C., Rahman, A., Song, J., Gao, X., & Lu, Y. (2017). Characterization of interface bonding in asphalt pavement layers based on direct shear tests with vertical loading. Journal of Materials in Civil Engineering, 29(9), 04017102. doi: 10.1061/(ASCE)MT.1943-5533.0001952
- Al Hakim, B. (1997). An improved back-calculation method to predict flexible pavement layers moduli and bonding condition between wearing course and base course (Doctoral dissertation). Liverpool John Moores University.
- Al Hakim, B., Cheung, L. W., & Armitage, R. J. (1999). Use of FWD data for prediction of bonding between pavement layers. International Journal of Pavement Engineering, 1(1), 49–59. doi: 10.1080/10298439908901696
- Autret, P., Baucheron de Boissoudy, A., & Marchand, J. P. (1982). ALIZE III practice. In Proceedings of the 5th international conference on structural design of asphalt pavements (pp. 174–191). Ann Arbor, MI: International Society for Asphalt Pavements.
- Bayrak, M., & Ceylan, H. (2006). Backcalculation of rigid pavement layer parameters using artificial neural networks. Intelligent Engineering Systems Through Artificial Neural Networks, 16, 349–354. doi: 10.1115/1.802566.paper53
- Bitumen Business Group. (1998). BISAR 3.0 user manual.
- Brown, S. F., Tam, W. S., & Brunton, J. M. (1987). Structural evaluation and overlay design: Analysis and implementation. In Proceedings of the 6th international conference on the structural design of asphalt pavements (Vol. 1, pp. 1013–1028). Ann Arbor, MI: International Society for Asphalt Pavements.
- Celaya, M., Mejía, D., Ertem, S., Nazarian, S., Rao, C., Von Quintus, H., & Shokouhi, P. (2009). Evaluation of NDT technologies to assess presence and extent of delamination of HMA airfield pavements: Verification study. AAPTP Report for Project, 6–4.
- Celaya, M., Nazarian, S., Rao, C., & Von Quintus, H. L. (2011). Delamination detection of asphalt pavements with nondestructive testing devices. In Transportation research board 90th annual meeting (No. 11-1487). Washington: Transportation Research Board, National Academy of Science.
- Ceylan, H., Bayrak, M. B., & Gopalakrishnan, K. (2014). Neural networks applications in pavement engineering: A recent survey. International Journal of Pavement Research and Technology, 7(6), 434.
- Ceylan, H., Guclu, A., Tutumluer, E., & Thompson, M. R. (2005). Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior. International Journal of Pavement Engineering, 6(3), 171–182. doi: 10.1080/10298430500150981
- Chun, S., Kim, K., Park, B., & Greene, J. (2017). Evaluation of structural benefits of prime coat application for flexible pavements using accelerated pavement testing (APT). KSCE Journal of Civil Engineering, 21(1), 141–149. doi: 10.1007/s12205-016-0746-4
- Davies, T. G., & Mamlouk, M. S. (1985). Theoretical response of multi-layered pavement systems to dynamic non-destructive testing. In Transportation research board, record 1022 (pp. 1–7). Washington, DC: Transportation Research Board, National Academy of Science.
- Elseifi, M. A., Gaspard, K., Wilke, P. W., Zhang, Z., & Hegab, A. (2015). Evaluation and validation of a model for predicting pavement structural number with rolling wheel deflectometer data. Transportation Research Record: Journal of the Transportation Research Board, 2525, 13–19. doi: 10.3141/2525-02
- Goodman, J. R., & Popov, E. P. (1968). Layered beam systems with interlayer slip. Journal of the Structural Division, 94(11), 2535–2548.
- Gopalakrishnan, K., Thompson, M. R., & Manik, A. (2006). Rapid finite-element based airport pavement moduli solutions using neural networks. International Journal of Computational Intelligence, 3(1), 63–71.
- Hossain, A. S. M., & Zaniewski, J. P. (1991). Detection and determination of depth of rigid bottom in backcalculation of layer moduli from falling weight deflectometer data. Transportation Research Record, 1293, 124–135.
- Hu, X., & Walubita, L. F. (2011). Effects of layer interfacial bonding conditions on the mechanistic responses in asphalt pavements. Journal of Transportation Engineering, 137(1), 28–36. doi: 10.1061/(ASCE)TE.1943-5436.0000184
- Kim, M. Y., Burton, M., Prozzi, J. A., & Murphy, M. (2014). Maintenance and rehabilitation project selection using artificial neural networks. In Transportation research board 93rd annual meeting (No. 14-3620). Washington, DC: Transportation Research Board, National Academy of Science.
- Kim, Y., Lee, Y.-C., & Ranjithan, S. (2000). Flexible pavement condition evaluation using deflection basin parameters and dynamic finite element analysis implemented by artificial neural networks. In Nondestructive testing of pavements and backcalculation of moduli: Third volume. Philadelphia, PA: The Society.
- Kim, Y. R., & Park, H. G. (2002). Use of falling weight deflectometer multi-load data for pavement strength estimation (No. FHWA/NC/2002-006).
- Kruntcheva, M. R., Collop, A. C., & Thom, N. H. (2000). Theoretical and practical aspects of the importance of bonding in a pavement structure. Project report PGR, 8.
- Ktari, R., Millien, A., Fouchal, F., Pop, I. O., & Petit, C. (2016). Pavement interface damage behavior in tension monotonic loading. Construction and Building Materials, 106, 430–442. doi: 10.1016/j.conbuildmat.2015.12.020
- Lawrence, S., Giles, C. L., & Tsoi, A. C. (1997, July). Lessons in neural network training: Overfitting may be harder than expected. In AAAI/IAAI (pp. 540–545). Menlo Park, CA: AAAI Press.
- Lee, Y. H., Ker, H. W., Lin, C. H., & Wu, P. H. (2010). Study of back-calculated pavement layer moduli from the LTPP database. Tamkang Journal of Science and Engineering, 13(2), 145156.
- Leng, Z., Ozer, H., Al-Qadi, I., & Carpenter, S. (2008). Interface bonding between hot-mix asphalt and various Portland cement concrete surfaces: Laboratory assessment. Transportation Research Record: Journal of the Transportation Research Board, 2057, 46–53. doi: 10.3141/2057-06
- Lepert, P., Poilane, J. P., & Bats-Villard, M. (1992). Evaluation of various field measurement techniques for the assessment of pavement interface condition. In International conference on asphalt pavements, 7th, (Vol. 3) Nottingham, UK: International Society for Asphalt Pavements.
- Lukanen, E. O., Stubstad, R., & Briggs, R. (2000). Temperature predictions and adjustment factors for asphalt pavement (No. FHWA-RD-98-085).
- Maestas, J. M., & Mamlouk, M. (1992). Comparison of pavement deflection analysis methods using overlay design. In Transportation research board, record 1377 (pp. 17–25). Washington, DC: Transportation Research Board, National Academy of Science.
- Mehta, Y. (2007). Evaluation of interlayer bonding in HMA pavements (No. WHRP 07-07).
- Mehta, Y., & Siraj, N. (2008). Preventing interlayer bonding failures in asphalt pavement. Wisconsin Highway Research Program.
- Mogawer, W., Austerman, A., Bonaquist, R., & Roussel, M. (2011). Performance characteristics of thin-lift overlay mixtures. Transportation Research Record: Journal of the Transportation Research Board, 2208, 17–25. doi: 10.3141/2208-03
- Nguyen, A. D. (2016). Nondestructive evaluation of bonding condition of asphalt pavement based on measured deformation of the road. In Transportation research board 95th annual meeting (No. 16-0211).
- Ozer, H., Al-Qadi, I. L., Wang, H., & Leng, Z. (2011). Characterisation of interface bonding between hot-mix asphalt overlay and concrete pavements: Modelling and in-situ response to accelerated loading. International Journal of Pavement Engineering, 13(2), 181–196. doi: 10.1080/10298436.2011.596935
- Rada, G. R., Richter, C. A., & Jordahl, P. (1994). SHRP’s layer moduli back-calculation procedure. In nondestructive testing of pavements and back-calculation of moduli: Second volume. ASTM International.
- Rakesh, N., Jain, A., Reddy, M. A., & Reddy, K. S. (2006). Artificial neural networks—genetic algorithm based model for backcalculation of pavement layer moduli. International Journal of Pavement Engineering, 7(3), 221–230. doi: 10.1080/10298430500495113
- Romanoschi, S. A., & Metcalf, J. B. (2002). The characterization of pavement layer interfaces. In Ninth international conference on asphalt pavements.
- Romanoschi, S. A., & Metcalf, J. B. (2003). Errors in pavement layer moduli back-calculation due to improper modeling of the layer interface condition. In Proceedings, transportation research board, TRB 2003 annual meeting.
- Saltan, M., Uz, V. E., & Aktas, B. (2012). Artificial neural networks–based backcalculation of the structural properties of a typical flexible pavement. Neural Computing and Applications, 23(6), 1703–1710. doi: 10.1007/s00521-012-1131-y
- Tutumluer, E., & Sarker, P. (2014). Development of improved pavement rehabilitation procedures based on FWD back-calculation (No. NEXTRANS Project No. 094IY04).
- Uzan, J. (1994). Advanced backcalculation techniques, nondestructive testing of pavements and backcalculation of moduli (second volume), ASTM STP 1198. In H. L. Von Quintus, A. J. Bush, III, & G. Y. Baladi (Eds.), American society for testing and materials. Philadelphia.
- Von Quintus, H., & Killingsworth, B. (1997). Design pamphlet for the backcalculation of pavement layer moduli in support of the 1993 AASHTO Guide for the Design of Pavement Structures (No. FHWA-RD-97-076).
- Wolfe, R. K., Randolph, B. W., & Colony, D. C. (1995, March/April). Standardized elastic moduli of pavement layers for overlay design. Journal of Transportation Engineering, 121(2), 221–232. doi: 10.1061/(ASCE)0733-947X(1995)121:2(221)