Integrating non-destructive testing data to produce asphalt pavement critical strains

References

• Nasimifar M, Thyagarajan S, Siddharthan RV, et al. Robust deflection indices from traffic-speed deflectometer measurements to predict critical pavement responses for network-level pavement management system application. J Transp Eng. 2016;142(1–11):04016004.
   Google Scholar
• Noureldin A, Zhu K, Li S, et al. Network pavement evaluation with falling-weight deflectometer and ground-penetrating radar. Transp Res Rec. 2003;1860(1):90–99.
   Google Scholar
• Plati C, Loizos A, Gkyrtis K. Assessment of modern roadways using non-destructive geophysical surveying techniques. Surv Geophys. 2019;41(2020):395–430. DOI:https://doi.org/10.1007/s10712-019-09518-y
   Google Scholar
• Gkyrtis K, Loizos A, Plati C. A mechanistic framework for field response assessment of asphalt pavements. Int J Pave Res Tech. 2020. https://doi.org/10.1007/s42947-020-0097–x
   Google Scholar
• Marecos V, Fontul S, Antunes ML, et al. Evaluation of a highway pavement using non-destructive tests: falling weight deflectometer and ground penetrating radar. Constr Build Mater. 2017;154:1164–1172.
   Web of Science ®Google Scholar
• Kavussi A, Abbasghorbani M, Moghadas-Nejad F, et al. A new method to determine maintenance and repair activities at network level pavement management using falling weight deflectometer. J Civ Eng Manage. 2017;23(3):338–346.
   Google Scholar
• Chen DH, Bilyeu J, Lin HH, et al. Temperature correction on falling weight deflectometer measurements. Transp Res Rec. 2000;1716(1):30–39.
   Google Scholar
• Zhang Z, Claros G, Manuel L, et al. Evaluation of the pavement structural condition at network level using falling weight deflectometer (FWD) Data. Paper presented at 82nd Annual Meeting of the Transportation Research Board, 12–16 January 2003, Washington, D.C. USA.
   Google Scholar
• Kim MY, Kim DY, Murphy MR. Improved method for evaluating the pavement structural number with falling weight deflectometer deflections. Transp Res Rec. 2013;2366(1):120–126.
   Google Scholar
• Crook AL, Montgomery SR, Guthrie WS. Use of falling weight deflectometer data for network-level flexible pavement management. Transp Res Rec. 2012;2304(1):75–85.
   Google Scholar
• Applied Research Associates (ARA). Guide for mechanistic empirical design of new and rehabilitated pavement structures. NCHRP 1-37A Final Report, Transportation Research Board, Washington DC, 2004.
   Google Scholar
• Horak E. Benchmarking the structural condition of flexible pavements with deflection bowl parameters. J South Afr Inst Civil Eng. 2008;50:2–9.
   Web of Science ®Google Scholar
• Horak E, Emery S Falling weight deflectometer bowl parameters as analysis tool for pavement structural evaluations. Proceedings of 22nd ARRB conference: research into practice: proceedings, 2006 October–November 29–2; Australia.
   Google Scholar
• Leiva-Villacorta F, Timm D Falling weight deflectometer loading pulse duration and its effect on predicted pavement responses. Paper presented at 92nd Annual Meeting of the Transportation Research Board, Washington, D.C.: 2013.
   Google Scholar
• Goktepe S, Agar E, Lav AH. Advances in backcalculating the mechanical properties of flexible pavements. Adv Eng Software. 2006;37(7):421–431.
   Web of Science ®Google Scholar
• Pais JC, Pereira P; A model to adjust the falling weight deflections due to temperature variations. Proceedings of the 10th International Conference on the Bearing Capacity of Roads, Railways and Airfields (BCRRA); 2017 Jun 28-30; Athens. Loizos (eds.)2017, p. 751–756.
   Google Scholar
• Akbarzadeh H, Bayat A, Soleymani HR. Analytical review of the HMA temperature correction factors from laboratory and falling weight deflectometer tests. Int J Pavement Res Technol. 2012;5:30–39.
   Google Scholar
• Zang G, Li L, Chen Z, et al. A nondestructive evaluation method for semi-rigid base cracking condition. Paper presented at 96th Annual Meeting of the Transportation Research Board; Washington, D.C.: 2017.
   Google Scholar
• Li M, Wang H. Prediction of asphalt pavement responses from FWD surface deflections using soft computing methods. J Transp Eng, Part B: Pavements. 2018;144(1–12):04018014.
   Google Scholar
• Van-Geem C, Nigro P, Berlemont B The use of deflection measurements in pavement management of the primary road network of Wallonia, Belgium. Proceedings of the 9th International Conference on Managing Pavement Assets (ICMPA); 2015 May 18- 21; United States.
   Google Scholar
• Losa M, Bacci R, Leandri P. A statistical model for prediction of critical strains in pavements from deflection measurements. Road Mater Pavement Des. 2008;9(sup1):373–396.
   Google Scholar
• Hassan H, Mousa RM. Evaluation of non-destructive testing data using AASHTO and WESDEF backcalculation approaches. J Eng Appl Sci. 2003;50:75–93.
   Google Scholar
• AASHTO, Guide for design of pavement structures, american association of state highway and transportation officials, Washington, DC, USA, 1993.
   Google Scholar
• Elbagalati O, Elseifi MA, Gaspard K, et al. Implementation of the structural condition index into the louisiana pavement management system based on rolling wheel deflectometer testing. Paper presented at 96th Annual Meeting of the Transportation Research Board; Washington, D.C.: 2017.
   Google Scholar
• Zofka A, Sudyka J, Sybilski D ;Assessment of pavement structures at traffic speed. Proceedings of the 10th International Conference on the Bearing Capacity of Roads, Railways and Airfields (BCRRA); 2017 Jun 28-30; Athens . Loizos (eds.)2017, p. 585.
   Google Scholar
• Nasimifar M, Siddharthan RV, Rada GR, et al. Dynamic analyses of traffic speed deflection devices. Int J Pavement Eng. 2017;18(5):381–390.
   Web of Science ®Google Scholar
• Nasimifar M, Siddharthan RS, Hajj EY, et al. Investigation of 3D-move responses under traffic speed deflection devices (TSDDs). Proceedings of the 5th International Conference on Accelerated Pavement Testing; 2016 Sept 19-21; Costa R, Moya A (eds.); 2017, p. 161–176.
   Google Scholar
• Ai C, Rahman A, Xiao C, et al. Analysis of measured strain response of asphalt pavements and relevant prediction models. Int J Pavement Eng. 2017;18(12):1089–1097.
   Web of Science ®Google Scholar
• Willis JR, Timm D. Repeatability of asphalt strain measurements under falling weight deflectometer loading. Transp Res Rec. 2009;2094(1):3–11.
   Google Scholar
• Loulizi A, Al-Qadi IL, Elseifi M. Difference between in situ flexible pavement measured and calculated stresses and strains. J Transp Eng. 2006;132:574–579.
   Web of Science ®Google Scholar
• Molenaar AAA. Structural evaluation and strengthening of flexible pavements using deflection measurements and visual condition surveys. In: Structural design of pavements – part IV. San Jose, Costa Rica; 2006.
   Google Scholar
• Jung FW. Direct calculation of maximum curvature and strain in asphalt concrete layers of pavements from load deflection basin measurements. Transp Res Rec. 1988;1196:125–132.
   Google Scholar
• Garg N, Thompson MR. Structural response of LVR Flexible pavements at Mn/road project. J Transp Eng. 1999;125(3):238–244.
   Google Scholar
• Kim YR, Park H Use of FWD multi-load data for pavement strength estimation. Report No. FHWA/NC/2002-006, North Carolina Department of Transportation, 2002.
   Google Scholar
• Plati C, Loizos A. Using ground-penetrating radar for assessing the structural needs of asphalt pavements. Case Stud NondestrTest Eval. 2012;27(3):273–284.
   Google Scholar
• Maser KR. Condition assessment of transportation infrastructure using ground penetrating radar. J Infrastruct Syst. 1996;2(2):94–101.
   Google Scholar
• Smith KD, Bruinsma JE, Wade MJ, et al. Using falling weight deflectometer data with mechanistic-empirical design and analysis, volume I: final report. Report No. FHWA-HRT-16-009. Federal Highway Administration, McLean, VA, 2017.
   Google Scholar
• Domitrović J, Rukavina T. Application of GPR and FWD in assessing pavement bearing capacity. Rom J Transp Infrastruct. 2013;2(2):11–21.
   Google Scholar
• EVERSERIES© USER’S GUIDE. Pavement analysis computer software and case studies, Washington State Department of Transportation, 2005.
   Google Scholar
• BISAR. Shell pavement design method, BISAR PC user manual, shell international petroleum, Company Limited, London. 1998.
   Google Scholar
• Ji R, Siddiki N, Nantung T, et al. Evaluation of resilient modulus of subgrade and base materials in indiana and its implementation in MEPDG. Sci World J. 2014;1–14. Article ID 372838. DOI:https://doi.org/10.1155/2014/372838
   Google Scholar
• Abed A, Thom N, Neves L. Probabilistic prediction of asphalt pavement performance. Road Mater Pavement Des. 2019;20(sup1):S247-S264.
   Web of Science ®Google Scholar
• Plati C, Pomoni M, Georgouli K. Quantification of skid resistance seasonal variation in asphalt pavements. J Traffic Transp Eng. 2020;7(2):237–248. DOI:https://doi.org/10.1016/j.jtte.2018.07.003
   Google Scholar
• Przybyla CP, McDowell DL. Simulation-based extreme value marked correlations in fatigue of advanced engineering alloys. Procedia Eng. 2010;2(1):1045–1056.
   Google Scholar
• Irwin LH, Yang WS, Stubstad RN (1989) Deflection Reading Accuracy and Layer Thickness Accuracy in Backcalculation of Pavement Layer Modul. First international symposium on nondestructive testing of pavements and backcalculation of Moduli, ASTM STP 1026. ASTM International, West Conshohocken, PA.
   Google Scholar
• Irwin LH Backcalculation: an overview and perspective. Presented at the 2002 FWD User Group Annual Meeting, 2002, Roanoke, VA.
   Google Scholar
• Tarefder RA, Ahmed MU. Consistency and accuracy of selected FWD backcalculation software for computing layer modulus of airport pavements. Int J Geotech Eng. 2013;7(1):21–35.
   Google Scholar
• Fontul S, Paixao A, Solla M, et al. Railway track condition assessment at network level by frequency domain analysis of GPR data. Remote Sens. 2018;10(4):559. https://www.mdpi.com/2072-4292/10/4/559
   Google Scholar
• Plati C, Loizos A, Gkyrtis K. Integration of non-destructive testing methods to assess asphalt pavement thickness. NDT & E Int. 2020, https://doi.org/10.1016/j.ndteint.2020.102292
   Google Scholar