A simplified pavement condition index regression model for pavement evaluation

References

• AASHTO, Mechanistic-Empirical Pavement Design Guide, 2008. A Manual of Practice, Interim Edition. Washington, D.C: American Association of State Highways and Transportation Officials.
   Google Scholar
• Abd El-Aziz, N., et al., 2017. Validation and improvement of pavement ME flexible pavement roughness prediction model using extended LTPP database. 96th Annual Meeting of the Transportation Research Board Washington D.C., USA.
   Google Scholar
• Abd El-Aziz, N., et al., 2018. International roughness Index prediction model for flexible pavements. International Journal of Pavement Engineering. doi:10.1080/10298436.2018.1441414.
   Web of Science ®Google Scholar
• American Society for Testing and Materials, 2007. Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys. ASTM D6433-07.
   Google Scholar
• ARA Inc, 2004. ERES consultants division, guide for mechanistic-empirical design of new and rehabilitated pavement structures, NCHRP 1–37A Final Report, Transportation Research Board, National Research Council, Washington, DC.
   Google Scholar
• Arhin, A., et al., 2015. Predicting pavement condition Index using international roughness Index in a dense urban area. Journal of Civil Engineering Research, 5 (1), 10–17.
   Google Scholar
• Austral roads, 2007. Guide to asset management part 5B: Roughness (Report No. AGAM05B-07), 55 p.
   Google Scholar
• Beckley, M., et al., 2016. Pavement Deterioration Modeling Using Historical Roughness Data. Master's Thesis, Arizona State University, USA.
   Google Scholar
• Cantisani, G. and Loprencipe, G, 2010. Road roughness and whole-body vibration: evaluation tools and comfort limits. ASCE Journal of Transportation Engineering, 136 (9), 818–826. doi: 10.1061/(ASCE)TE.1943-5436.0000143
   Web of Science ®Google Scholar
• Chikezie, C., Abejide, S., and Taiwo, A, 2013. Multiobjective optimization for pavement maintenance and rehabilitation programming using genetic algorithms. World Scholars Research Library, 5, 76–83.
   Google Scholar
• DataPave 3.0, 2001. LTPP-DataPave 3.0. ERES Consultants, Federal Highway Administration.
   Google Scholar
• Dewan, S.A., and Smith, R.E., 2002. Estimating IRI from Pavement Distresses to Calculate Vehicle Operating Costs for the Cities and Counties of San Francisco Bay Area. Transportation Research Record No. 1816.
   Google Scholar
• El-Ashwah, A., et al., 2019. An innovative approach for developing resilient modulus master surface to characterize granular pavement materials and Subgrade soils. Construction and Building Materials, 194 (10), 372–385. doi: 10.1016/j.conbuildmat.2018.10.212
   Google Scholar
• Federal Highway Administration, 2003. Pavement Preservation Compendium, FHWA-IF-03-21.
   Google Scholar
• Gao, L., et al., 2012. Network-level road pavement maintenance and rehabilitation scheduling for optimal performance improvement and budget utilization. Computer-Aided Civil and Infrastructure Engineering, 27, 278–287. doi: 10.1111/j.1467-8667.2011.00733.x
   Web of Science ®Google Scholar
• Goenaga, B., Fuentes, L., and Mora, O., 2017. Evaluation of the methodologies used to generate random pavement profiles based on the power spectral density: An approach based on the International roughness index. Ingeniería e Investigación, 37 (1), 49–57. doi:10.15446/ing.investig.v37n1.57277
   Web of Science ®Google Scholar
• INVIAS - National Institute of Roads, 2007. General Road Construction Specifications, Bogotá DC, Colombia.
   Google Scholar
• ISO 8608, 2016. Mechanical Vibrations - road surface Profiles - reported of measured data, ISO 8608: 2016. Dubai: International Organization for Standardization.
   Google Scholar
• Johari, A., Habibagahi, G., and Ghahramanib, A., 2011. Prediction of SWCC using artificial Intelligent systems. A Comparative Study, Scientia Iranica, 18 (5), 1002–1008. doi: 10.1016/j.scient.2011.09.002
   Web of Science ®Google Scholar
• Kan Wu, K., et al., 2015. Development of PCI-based Pavement Performance Model for Management of Road Infrastructure System. Master's Thesis, Arizona State University, USA.
   Google Scholar
• Lin, J., Yau, J., and Hsiao, L, 2003. Correlation analysis between international roughness index (IRI) and pavement distress by neural network. In 82nd Annual Meeting of the Transportation Research Board, Washington, D.C.
   Google Scholar
• Mactutis, A., Alavi, S., and Ott, W, 2000. Investigation of Relationship Between Roughness and Pavement Surface Distress Based on Wes Track Project. Transportation Research Record, Journal of the Transportation Research Board, No. 1699, TRB, National Research Council, Washington, D.C.
   Google Scholar
• Magyar Útügyi Társaság, 2008. Út-pályaszerkezeti aszfaltrétegek [Asphalt road pavement structure layers] (ÚT 2–3.302:2008). Budapest: Author (in Hungarian).
   Google Scholar
• Mascio, P., Piccolo, I., and Cera, L, 2007. Automated Distress Evaluation. In Proceedings of 4th International SIIV Congress, Palermo, Italy.
   Google Scholar
• Ministerstvo dopravy, výstavby a regionálneho rozvoja, 2012. Meranie a hodnotenie nerovnosti vozoviek pomocou zariadenia Profilograph GE [Measurement and evaluation of pavement roughness using Profilograph GE] (TP 04/2012). Author, 20 p. (in Slovak).
   Google Scholar
• Ministerstvo regional’nogo razvitija Rossijskoj Federacii, 2012. Avtomobil’nye dorogi [Roads] (SP 78.13330). Moscow: Author, 117 p. (in Russian).
   Google Scholar
• Ministerstvo transporta i kommunikacij Respubliki Belarus, 2012. ˇ Ť echnĭ ceskij koďeks ustanovivšejsja praktiki. Avtomobil’nye dorogi. Pravila ustrojstva.TKP 059–2012 (02191). Minsk: Departament ˇ “Belavtodor” Ministerstva transporta i kommunikacij Respubliki Belarus (MTiKRB), 55 p. (in ˇ Russian).
   Google Scholar
• Morcous, G. and Lounis, Z, 2004. Maintenance optimization of infrastructure networks using genetic algorithms. Automation in Construction, 14, 129–142. doi: 10.1016/j.autcon.2004.08.014
   Web of Science ®Google Scholar
• Norwegian Public Roads Administration, 2014. Standard for drift og vedlikehold av riksveger [Standard for operation and maintenance of roads] (Håndbok R610). Author, 158 p. (in Norwegian).
   Google Scholar
• Park, K., Thomas, N., and Lee, K, 2007. Applicability of the International roughness Index as a predictor of asphalt pavement condition. Journal of Transportation Engineering-ASCE, 133 (12), 706. doi:10.1061/(ASCE)0733-947X doi: 10.1061/(ASCE)0733-947X(2007)133:12(706)
   Google Scholar
• Perera, R. and Kohn, S., 2002. Issues in pavement smoothness: a summary report, national cooperative highway research program. Transportation Research Board, National Research Council, March.
   Google Scholar
• Robbins, T, 2016. A synthesis report: value of pavement smoothness and ride quality to roadway users and the impact of pavement roughness on vehicle operating costs. 16–03.
   Google Scholar
• Sayers, M. W., Gillespie, T. D., and Paterson, W. D. O, 1986. Guidelines for the conduct and calibration of road roughness measurements. World Bank Technical Paper No. 46. The World Bank, Washington, D.C.
   Google Scholar
• Shah, Y.U., et al., 2013. Development of overall pavement condition Index for urban road network. Proceedings of 2nd Conference of Transportation Research Group of India, 104, 332–341.
   Google Scholar
• Shahin, M.Y., and Kohn, S.D., 1979. Development of a pavement condition rating procedure for roads, streets, and parking lots. CERL-TR-M-268, Final Report, US Army Construction Engineering Research Laboratory.
   Google Scholar
• Vidya, R., Santhankumar, Moses S., and Samson, M, 2013. Estimation of IRI from PCI in construction work zones. ACEE International Journal on Civil and Environmental Engineering, 2 (1), 1–5.
   Google Scholar
• Wang, K., et al., 2015. Network level pavement evaluation with 1 mm 3D survey system. Journal of Traffic Transportation Engineering, 2, 391–398.
   Google Scholar
• Wotring, D.C., et al., 1998. Pavement distress and selection of rehabilitation alternatives. Transportation Research Record 1629, TRB, National Research Council, Washington D.C., 214–225.
   Google Scholar
• Yousefzadeh, M., Azadi, S., and Soltani, A, 2010. Road profile estimation using neural network algorithm. Journal of Mechanical Science and Technology, 24 (3), 743–754. doi: 10.1007/s12206-010-0113-1
   Web of Science ®Google Scholar