Model development for moisture content and density prediction for non-dry asphalt concrete using GPR data

References

• Abufares, L., et al., 2021. GPR application for moisture content prediction of cold in-place recycling. ASCE International Airfield and Highway Pavements Conference 2021, doi:10.1061/9780784483503.023.
   Google Scholar
• Abufares, L., Cao, Q., and Al-Qadi, I.L, forthcoming. Prediction of moisture content in cold recycled asphalt concrete using ground penetrating radar data. The International Symposium on Non-destructive Testing in Civil Engineering, August 16–18, 2022. Zurich, Switzerland.
   Google Scholar
• Al-Qadi, I., et al., 2004. Effective approach to improve pavement drainage layers. Journal of Transportation Engineering, 130 (5), 658–664. doi:10.1061/(ASCE)0733-947X(2004)130:5(658).
   Web of Science ®Google Scholar
• Alharthi, A., and Lange, J, 1987. Soil water saturation: dielectric determination. Water Resources Research, 23 (4), 591–595. doi:10.1029/WR023i004p00591.
   Web of Science ®Google Scholar
• ARRA (Asphalt Recycling and Reclaiming Association), 2015. Basic asphalt recycling manual. 2nd Edition. Illinois, USA: US Department of Transportation, Federal Highway Administration.
   Google Scholar
• Benedetto, A., et al., 2017. An overview of ground-penetrating radar signal processing techniques for road inspections. Signal Processing, 132, 201–209. doi:10.1016/j.sigpro.2016.05.016.
   Web of Science ®Google Scholar
• Cao, Q., and Al-Qadi, I.L, 2021. Signal stability and the height-correction method for ground-penetrating radar in-situ asphalt concrete density prediction. Transportation Research Record: Journal of the Transportation Research Board, 2675 (9), 835–847. doi:10.1177/03611981211004585.
   Web of Science ®Google Scholar
• Dong, Y., and Ansar, F, 2011. Non-destructive testing and evaluation (NDT/NDE) of civil structures rehabilitated using fiber reinforced polymer (FRP) composites. Woodhead Publishing Series in Civil and Structural Engineering, 193-222, ISBN 9781845693985, doi:10.1533/9780857090928.2.193.
   Google Scholar
• Fernandes, F. M., Fernandes, A., and Pais, J, 2017. Assessment of the density and moisture content of asphalt mixtures of road pavements. Construction and Building Materials, 154, 1216–1225. doi:10.1016/j.conbuildmat.2017.06.119.
   Web of Science ®Google Scholar
• FHWA (Federal Highway Administration), 2017. Final report 2015-2017: A life-cycle methodology for energy-use by in-place pavement recycle techniques. Illinois Center for Transportation, University of Illinois at Urbana–Champaign, doi:10.36501/0197-9191/20-018.
   Google Scholar
• Hoegh, K., et al., 2019. Toward core-free pavement compaction evaluation: An innovative method relating asphalt permittivity to density. Geosciences, 9 (7), 280. doi:10.3390/geosciences9070280.
   Web of Science ®Google Scholar
• Lahouar, S., and Al-Qadi, I. L, 2008. Automatic detection of multiple pavement layers from GPR data. NDT & E International, 41 (2), 69–81. doi:10.1016/j.ndteint.2007.09.001.
   Web of Science ®Google Scholar
• Leng, Z., 2011. Prediction of in-situ asphalt mixture density using ground penetrating radar: theoretical development and field verification. (Order No. 3503652, University of Illinois at Urbana-Champaign). ProQuest Dissertations and Theses, 150. https://www.proquest.com/dissertations-theses/prediction-situ-asphalt-mixture-density-using/docview/1009712029/se-2.
   Google Scholar
• Leng, Z., et al., 2012. Field application of ground-penetrating radar for measurement of asphalt mixture density: case study of Illinois route 72 overlay. Transportation Research Record: Journal of the Transportation Review Board, 2304 (1), 133–141. doi:10.3141/2304-15.
   Google Scholar
• Leng, Z., and Al-Qadi, I.L, 2011. Flexible pavement quality assurance using ground penetrating radar. ASCE First Congress of Transportation and Development Institute Conference, March 13–16, 2011, Chicago, IL, doi:10.1061/41167(398)59.
   Google Scholar
• Liu, H., and Sato, M, 2014. In-situ measurement of pavement thickness and dielectric permittivity by GPR using an antenna array. NDT & E International, 64, 65–71. doi:10.1016/j.ndteint.2014.03.001.
   Web of Science ®Google Scholar
• Loizos, A., and Plati, C, 2007. Accuracy of pavement thicknesses estimation using different ground penetrating radar analysis approaches. NDT & E International, 40 (2), 147–157. doi:10.1016/j.ndteint.2006.09.001.
   Web of Science ®Google Scholar
• Plati, C., Georgiou, P., and Loizos, A, 2015. A comprehensive approach for the assessment of HMA compactability using GPR technique. Near Surface Geophysics, 14 (2), 117–126. doi:10.3997/1873-0604.2015043.
   Web of Science ®Google Scholar
• Shangguan, P., et al., 2016. Algorithm development for the application of ground-penetrating radar on asphalt pavement compaction monitoring. International Journal of Pavement Engineering, 17 (3), 189–200. doi:10.1080/10298436.2014.973027.
   Web of Science ®Google Scholar
• Sun, M., et al., 2019. Advanced signal processing methods for ground penetrating radar: applications to civil engineering. IEEE, 36 (4), 74–84. doi:10.1109/MSP.2019.2900454.
   Google Scholar
• Wang, S., and Al-Qadi, I.L, 2022. Impact and removal of ground-penetrating radar vibration on continuous asphalt concrete pavement density prediction. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14. doi:10.1109/TGRS.2021.3106041.
   Web of Science ®Google Scholar
• Wang, S., Zhao, S., and Al-Qadi, I.L, 2020. Real-time density and thickness estimation of thin asphalt pavement overlay during compaction using ground penetrating radar data. Surveys in Geophysics, 41 (3), 431–445. doi:10.1007/s10712-019-09556-6.
   Web of Science ®Google Scholar
• Zhao, S., Al-Qadi, I.L., and Wang, S, 2018. Prediction of thin asphalt concrete overlay thickness and density using nonlinear optimization of GPR data. NDT & E International, 100, 20–30. doi:10.1016/j.ndteint.2018.08.001.
   Web of Science ®Google Scholar