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Methods on Calculating the International Roughness Index: A Literature Review

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Sustainable Development Approaches

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 243))

Abstract

The pavement management system determines the optimum maintenance techniques for each mile of the highway network. The total irregularities in the pavement surface per linear travel unit distance affect the ride quality. As a result, the safety of road users is known as pavement roughness or roughness index. The research aims to discover the various methods and procedures for computing IRI. It also wants to investigate how IRI affects the driver, the vehicle, and road conditions. The connection between road condition, roughness, and quality can impact the IRI values. Finally, considerations to various challenges and recommendations are in the proposal for evaluating IRI.

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References

  1. Arbabpour Bidgoli M, Golroo A, Sheikhzadeh Nadjar H, Ghelmani Rashidabad A, Ganji MR (2019, August) Road roughness measurement using a cost-effective sensor-based monitoring system. Autom Constr, vol 104. Doi: https://doi.org/10.1016/j.autcon.2019.04.007

  2. Loprencipe G, Zoccali P, Cantisani G (2019, April) Effects of vehicular speed on the assessment of pavement road roughness. Appl Sci 9(9). Doi: https://doi.org/10.3390/app9091783

  3. Zeng H, Park H, Smith BL, Parkany E (2018, August) Feasibility assessment of a smartphone-based application to estimate road roughness. KSCE J Civ Eng 22(8). Doi: https://doi.org/10.1007/s12205-017-1008-9

  4. Venkatesulu S, Sudarshan E, Korra SN, Raghava Kumari D, Yadav BP, Mahender K (2020, December) Real time fitness analysis of Bitumen Road and vehicle through their acoustic signals. IOP Conf Ser: Mater Sci Eng 981(3). Doi: https://doi.org/10.1088/1757-899X/981/3/032004

  5. Pawar PR, Mathew AT, Saraf MR (2018) IRI (International Roughness Index): an indicator of vehicle response. Mater Today: Proc 5(5). Doi: https://doi.org/10.1016/j.matpr.2018.02.143

  6. Múčka P (2017, June) Road roughness limit values based on measured vehicle vibration. J Infrastruct Syst 23(2). Doi: https://doi.org/10.1061/(ASCE)IS.1943-555X.0000325

  7. Nguyen X, Nguyen T, Hoa Tran P (2020, July) The effect of road surface roughness to recommended speed of vehicles. IOP Conf Ser: Mater Sci Eng, vol 886. Doi: https://doi.org/10.1088/1757-899X/886/1/012014

  8. Ziari H, Sobhani J, Ayoubinejad J, Hartmann T (2015) Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods. Int J Pavement Eng 17(9):776–788. https://doi.org/10.1080/10298436.2015.1019498

    Article  Google Scholar 

  9. Savnns MW On the calculation of international roughness index from longitudinal road profile

    Google Scholar 

  10. Nurhadiansyah R, Hadiana A (2019, November) Toll road roughness index forecasting with combination grey forecasting model and similarity spatial data. IOP Conf Ser: Mater Sci Eng, vol 662. Doi: https://doi.org/10.1088/1757-899X/662/2/022065

  11. Chen SL, Lin CH, Tang CW, Chu LP, Cheng CK (2020, December) Research on the international roughness index threshold of road rehabilitation in metropolitan areas: a case study in Taipei city. Sustainability (Switzerland) 12(24):1–19. https://doi.org/10.3390/su122410536

  12. Ziari H, Sobhani J, Ayoubinejad J, Hartmann T (2016, October) Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods. Int J Pavement Eng 17(9). Doi: https://doi.org/10.1080/10298436.2015.1019498

  13. Janani L, Sunitha V, Mathew S (2020, January) Influence of surface distresses on smartphone-based pavement roughness evaluation. Int J Pavement Eng. https://doi.org/10.1080/10298436.2020.1714045

  14. Abeygunawardhana C, Sandamal RMK, Pasindu HR (2020, July) Identification of the impact on road roughness on speed patterns for different roadway segments. Doi: https://doi.org/10.1109/MERCon50084.2020.9185387

  15. Achmadi F, Suprapto M, Setyawan A (2017, February) The Priority of Road Rehabilitation in Karanganyar Regency Using IRI Estimation from Roadroid. IOP Conf Ser: Mater Sci Eng, vol 176. Doi: https://doi.org/10.1088/1757-899X/176/1/012033

  16. Hossain MI, Tutumluer E, Nikita, Grimm C (2019, August) Evaluation of android-based cell phone applications to measure international roughness index of rural roads. Doi: https://doi.org/10.1061/9780784482575.034

  17. Arianto T, Suprapto M, and Syafi’I (2018, March) Pavement condition assessment using IRI from roadroid and surface distress index method on national road in sumenep regency. IOP Conf Ser: Mater Sci Eng, vol 333. Doi: https://doi.org/10.1088/1757-899X/333/1/012091

  18. Li J, Zhang Z, Wang W (2019, March) New Approach for Estimating International Roughness Index Based on the Inverse Pseudo Excitation Method. J Transp Eng, Part B: Pavements 145(1). Doi: https://doi.org/10.1061/JPEODX.0000093

  19. Zhang C et al (2019, July) Study on the applicability of physiological method for evaluating pavement roughness. Doi: https://doi.org/10.1061/9780784482292.081

  20. Khalifeh V, Golroo A, Ovaici K (2018, July) Application of an inexpensive sensor in calculating the international roughness index. J Comput Civ Eng 32(4). Doi: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000761

  21. Padilla JA, Victoria AN, dela Cruz OG, Despabeladera CT, Creencia CJN Evaluation of international roughness index by speed-related quality criteria in the Philippines. Proc Annu Int Conf Arch Civ Eng, pp 160–164. Doi: https://doi.org/10.5176/2301-394X_ACE19.523

  22. dela Cruz OG, Mendoza CA, Lopez KD (2021, July) International roughness index as road performance indicator: a literature review. IOP Conf Ser: Earth Environ Sci 822(1):012016. Doi: https://doi.org/10.1088/1755-1315/822/1/012016

  23. Bridgelall R (2014, March) A participatory sensing approach to characterize ride quality. Doi: https://doi.org/10.1117/12.2046854

  24. Semnarshad M, Elyasi M, Saffarzadeh M, Saffarzadeh A (2018) Identification and prioritization of accident-prone segments using international roughness index identification and prioritization of accident-prone segments using …,”

    Google Scholar 

  25. Wessels I, Steyn WJvdM (2020, March) Continuous, response-based road roughness measurements utilising data harvested from telematics device sensors. Int J Pavement Eng 21(4). Doi: https://doi.org/10.1080/10298436.2018.1483505

  26. Hu J, Gao X, Wang R, Sun S (2017) Research on Comfort and safety threshold of pavement roughness. Transp Res Rec 2641(1):149–155. https://doi.org/10.3141/2641-17

    Article  Google Scholar 

  27. Kawamura A, Tomiyama K, Rossi R, Gastaldi M, Mulatti C (2017) Driving on rough surface requires care and attention. Transp Res Procedia, vol 22. Doi: https://doi.org/10.1016/j.trpro.2017.03.008

  28. Lee J, Abdel-Aty M, Nyame-Baafi E (2020, February) Investigating the Effects of Pavement Roughness on Freeway Safety using Data from Five States. Transp Res Rec: J Transp Res Board 2674(2). Doi: https://doi.org/10.1177/0361198120905834

  29. Zhao Y, Wang ML (2015, June) Measurement through dynamic tire pressure sensor inside the tire. Doi: https://doi.org/10.1061/9780784479216.026

  30. Zhustareva Ev, Bochkarev VI (2020, June) The complex method of estimation of highway maintenance quality taking into account the International Roughness Index. IOP Conf Ser: Mater Sci Eng, vol 832. Doi: https://doi.org/10.1088/1757-899X/832/1/012035

  31. Ghasemi P, Aslani M, Rollins DK, Christopher Williams R, Schaefer VR (2018, January) Modeling rutting susceptibility of asphalt pavement using principal component pseudo inputs in regression and neural networks. Int J Pavement Res Technol. Doi: https://doi.org/10.1016/j.ijprt.2018.01.003

  32. Loprencipe G, Zoccali P (2017, March) Use of generated artificial road profiles in road roughness evaluation. J Mod Transp 25(1). Doi: https://doi.org/10.1007/s40534-017-0122-1

  33. Loprencipe G, Zoccali P (2017, April) Ride quality due to road surface irregularities: comparison of different methods applied on a set of real road profiles. Coatings 7(5). Doi: https://doi.org/10.3390/coatings7050059

  34. Abulizi N, Kawamura A, Tomiyama K, Fujita S (2016, October) Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS. J Traffic Transp Eng (English Edition) 3(5). Doi: https://doi.org/10.1016/j.jtte.2016.09.004

  35. Evans RP, Arulrajah A, Horpibulsuk S (2015, December) Detecting gilgai relief beneath sealed flexible pavements using road profile and road roughness measurements. Indian Geotech J 45(4). Doi: https://doi.org/10.1007/s40098-015-0164-4

  36. Abudinen D, Fuentes LG, Carvajal Muñoz JS (2017, January) Travel quality assessment of urban roads based on international roughness index: case study in Colombia. Transp Res Rec: J Transp Res Board 2612(1). Doi: https://doi.org/10.3141/2612-01

  37. Mamlouk M, Vinayakamurthy M, Underwood BS, Kaloush KE (2018, December) Effects of the international roughness index and rut depth on crash rates. Transp Res Rec: J Transp Res Board 2672(40). Doi: https://doi.org/10.1177/0361198118781137

  38. Radović N, Jokanović I, Matić B, Šešlija M (2016, June) A measurement of roughness as indicator of road network condition – case study Serbia. Teh Vjesn-Tech Gaz 23(3). Doi: https://doi.org/10.17559/TV-20150212204747

  39. Hassan R, Mcmanus K, Holden J (1999, January) Predicting Pavement deterioration modes using waveband analysis. Transp Res Rec: J Transp Res Board 1652(1). Doi: https://doi.org/10.3141/1652-57

  40. Lu P, Tolliver D (2012, November) Pavement treatment short-term effectiveness in IRI change using long-term pavement program data. J Transp Eng 138(11). Doi: https://doi.org/10.1061/(ASCE)TE.1943-5436.0000446

  41. Jia X, Huang B, Zhu D, Dong Q, Woods M (2018, June) Influence of measurement variability of international roughness index on uncertainty of network-level pavement evaluation. J Transp Eng, Part B: Pavements 144(2). Doi: https://doi.org/10.1061/JPEODX.0000034

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Acknowledgements

The researchers would like to thank ES Family, namely; Christian A. Mendoza, Kristel D. Lopez, Jobelle S. Dajac, John Pual J. Pauya, Ely D. Biago, and Armando N. Victoria Jr, for their continuous support in conducting this paper.

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Correspondence to Orlean G. dela Cruz .

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Padilla, J.A., dela Cruz, O.G. (2022). Methods on Calculating the International Roughness Index: A Literature Review. In: Nia, E.M., Farshchi, I., Yola, L., Awang, M. (eds) Sustainable Development Approaches. Lecture Notes in Civil Engineering, vol 243. Springer, Cham. https://doi.org/10.1007/978-3-030-99979-7_2

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  • DOI: https://doi.org/10.1007/978-3-030-99979-7_2

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