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Research Articles

Predicting pavement performance using distress deterioration curves

ORCID Icon, , , , &
Pages 1174-1190 | Received 16 Dec 2022, Accepted 13 Jul 2023, Published online: 13 Sep 2023

References

  • Abaza, K. A. (2014). Back-calculation of transition probabilities for Markovian-based pavement performance prediction models. International Journal of Pavement Engineering, 17(3), 253–264. https://doi.org/10.1080/10298436.2014.993185
  • Abaza, K. A. (2015). Simplified staged-homogenous Markov model for flexible pavement performance prediction. Road Materials and Pavement Design, 17(2), 365–381. https://doi.org/10.1080/14680629.2015.1083464
  • Abed, A., Thom, N., & Neves, L. (2019). Probabilistic prediction of asphalt pavement performance. Road Materials and Pavement Design, 20(sup1), S247–S264. https://doi.org/10.1080/14680629.2019.1593229
  • Abu Al-Rub, R. K., Darabi, M. K., Huang, C.-W., Masad, E. A., & Little, D. N. (2012). Comparing finite element and constitutive modelling techniques for predicting rutting of asphalt pavements. International Journal of Pavement Engineering, 13(4), 322–338. https://doi.org/10.1080/10298436.2011.566613
  • Ahlin, K., & Granlund, N. O. J. (2011). Relating road roughness and vehicle speeds to human whole body vibration and exposure limits. International Journal of Pavement Engineering, 3(4), 207–216. https://doi.org/10.1080/10298430210001701
  • Alavi, M., Hajj, E. Y., & Sebaaly, P. E. (2015). A comprehensive model for predicting thermal cracking events in asphalt pavements. International Journal of Pavement Engineering, 18(9), 871–885. https://doi.org/10.1080/10298436.2015.1066010
  • Alimoradi, S., Golroo, A., & Asgharzadeh, S. M. (2020). Development of pavement roughness master curves using Markov Chain. International Journal of Pavement Engineering, 23(2), 453–463. https://doi.org/10.1080/10298436.2020.1752917
  • ARA. (2004). Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures.
  • Asphalt Industry Alaiance. (2022). Annual Local Authority Road Maintenance Survey.
  • Athiappan, K., Kandasamy, A., Mohamed, M. J. S., Parthiban, P., & Balasubramanian, S. (2022). Prediction modeling of skid resistance and texture depth on flexible pavement for urban roads. Materials Today: Proceedings, 52, 923–929. https://doi.org/10.1016/j.matpr.2021.10.304
  • Chan, C. Y., Huang, B., Yan, X., & Richards, S. (2010). Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS). Journal of Advanced Transportation, 44(3), 150–161. https://doi.org/10.1002/atr.129
  • Chu, C., Wang, L., & Xiong, H. (2022). A review on pavement distress and structural defects detection and quantification technologies using imaging approaches. Journal of Traffic and Transportation Engineering (English Edition), 9(2), 135–150. https://doi.org/10.1016/j.jtte.2021.04.007
  • Darter, M. I., & Hudson, W. R. (1973). Probabilistic design concepts applied to flexible pavement system design.
  • Dave, E. V., & Buttlar, W. G. (2010). Thermal reflective cracking of asphalt concrete overlays. International Journal of Pavement Engineering, 11(6), 477–488. https://doi.org/10.1080/10298430903578911
  • Department for Transport. (2012). Guidance on road classification and the primary route network. Department for Transport. Retrieved February 23, from https://www.gov.uk/government/publications/guidance-on-road-classification-and-the-primary-route-network/guidance-on-road-classification-and-the-primary-route-network
  • Department for Transport. (2021). Technical Note: Road condition and maintenance data.
  • Di Benedetto, H., De La Roche, C., Baaj, H., Pronk, A., & Lundström, R. (2004). Fatigue of Bituminous Mixtures. RILEM TC 182-PEB ‘Performance testing and evaluation of bituminous materials.
  • Dinegdae, Y. H., & Birgisson, B. (2016). Effects of truck traffic on top-down fatigue cracking performance of flexible pavements using a new mechanics-based analysis framework. Road Materials and Pavement Design, 19(1), 182–200. https://doi.org/10.1080/14680629.2016.1251958
  • Epps, A. (2000). Design and analysis system for thermal cracking in Asphalt Concrete. Journal of Transportation Engineering, 126(4), 300–307. https://doi.org/10.1061/(ASCE)0733-947X(2000)126:4(300)
  • Guha, S., & Hossain, K. (2022). An economic approach to road condition assessment using road user feedback: A new model and its application. International Journal of Pavement Engineering, 1–16. https://doi.org/10.1080/10298436.2021.2022673
  • Kargah-Ostadi, N., & Stoffels, S. M. (2015). Framework for development and comprehensive comparison of empirical pavement performance models. Journal of Transportation Engineering, 141(8), https://doi.org/10.1061/(ASCE)TE.1943-5436.0000779
  • Kargah-Ostadi, N., Zhou, Y., & Rahman, T. (2019). Developing performance prediction models for pavement management systems in local governments in absence of age data. Transportation Research Record: Journal of the Transportation Research Board, 2673(3), 334–341. https://doi.org/10.1177/0361198119833680
  • Kim, H. B., Buch, N., & Park, D.-Y. (2000). Mechanistic-Empirical rut prediction model for In-service pavements. Transportation Research Record, 1730(1), 99–109. https://doi.org/10.3141/1730-12
  • Luo, X., Gu, F., Ling, M., & Lytton, R. L. (2018). Review of mechanistic-empirical modeling of top-down cracking in asphalt pavements. Construction and Building Materials, 191, 1053–1070. https://doi.org/10.1016/j.conbuildmat.2018.10.005
  • Luo, X., Gu, F., Zhang, Y., Lytton, R. L., & Zollinger, D. (2017). Mechanistic-empirical models for better consideration of subgrade and unbound layers influence on pavement performance. Transportation Geotechnics, 13, 52–68. https://doi.org/10.1016/j.trgeo.2017.06.002
  • Mansura, D. A., Thom, N. H., & Beckedahl, H. J. (2018). Numerical and experimental predictions of texture-related influences on rolling resistance. Transportation Research Record: Journal of the Transportation Research Board, 2672(40), 430–439. https://doi.org/10.1177/0361198118776114
  • Marcelino, P., de Lurdes Antunes, M., Fortunato, E., & Gomes, M. C. (2019). Machine learning approach for pavement performance prediction. International Journal of Pavement Engineering, 22(3), 341–354. https://doi.org/10.1080/10298436.2019.1609673
  • Mbarki, R., Kutay, M. E., Gibson, N., & Abbas, A. R. (2012). Comparison between fatigue performance of horizontal cores from different asphalt pavement depths and laboratory specimens. Road Materials and Pavement Design, 13(3), 422–432. https://doi.org/10.1080/14680629.2012.685843
  • Mcrobbie, S., Walter, L., Read, C., Viner, H., & Wright, A. (2007). Developing SCANNER road condition indicator parameter thresholds and weightings-version 1. TRL Published Project Report.
  • McRobbie, S., & Wright, A. (2006). TTS Research-Crack detection on local roads. Phase 1.
  • Múčka, P. (2016). International Roughness Index specifications around the world. Road Materials and Pavement Design, 18(4), 929–965. https://doi.org/10.1080/14680629.2016.1197144
  • Perraton, D., Di Benedetto, H., Sauzéat, C., De La Roche, C., Bankowski, W., Partl, M., & Grenfell, J. (2010). Rutting of bituminous mixtures: Wheel tracking tests campaign analysis. Materials and Structures, 44(5), 969–986. https://doi.org/10.1617/s11527-010-9680-y
  • Perrotta, F., Parry, T., Neves, L. C., Buckland, T., Benbow, E., & Mesgarpour, M. (2019). Verification of the HDM-4 fuel consumption model using a Big data approach: A UK case study. Transportation Research Part D: Transport and Environment, 67, 109–118. https://doi.org/10.1016/j.trd.2018.11.001
  • PTS. (2022). Surface Condition Assessment for the National Network of Roads. Pavement Testing Services. Retrieved April 27, from http://www.ptsinternational.co.uk/scanner-2/
  • Ragnoli, A., De Blasiis, M., & Di Benedetto, A. (2018). Pavement distress detection methods: A review. Infrastructures, 3(4), 58–77. https://doi.org/10.3390/infrastructures3040058
  • Sandra, A. K., & Sarkar, A. K. (2013). Development of a model for estimating international roughness index from pavement distresses. International Journal of Pavement Engineering, 14(8), 715–724. https://doi.org/10.1080/10298436.2012.703322
  • Shtayat, A., Moridpour, S., Best, B., Shroff, A., & Raol, D. (2020). A review of monitoring systems of pavement condition in paved and unpaved roads. Journal of Traffic and Transportation Engineering (English Edition), 7(5), 629–638. https://doi.org/10.1016/j.jtte.2020.03.004
  • Svenson, G., & Fjeld, D. (2015). The impact of road geometry and surface roughness on fuel consumption of logging trucks. Scandinavian Journal of Forest Research, 31(5), 526–536. https://doi.org/10.1080/02827581.2015.1092574
  • Tamakloe, R., Lim, S., Sam, E. F., Park, S. H., & Park, D. (2021). Investigating factors affecting bus/minibus accident severity in a developing country for different subgroup datasets characterised by time, pavement, and light conditions. Accident Analysis & Prevention, 159, 106268. https://doi.org/10.1016/j.aap.2021.106268
  • UK Roads Board. (2011a). SCANNER surveys for Local Roads - User Guide and Specification - Advice to Local Authorities: Using SCANNER survey results.
  • UK Roads Board. (2011b). SCANNER surveys for Local Roads - User Guide and Specification - Introduction to SCANNER surveys.
  • Wang, C., Xu, S., Liu, J., Yang, J., & Liu, C. (2022). Building an improved artificial neural network model based on deeply optimizing the input variables to enhance rutting prediction. Construction and Building Materials, 348. https://doi.org/10.1016/j.conbuildmat.2022.128658
  • Wang, F., Machemehl, R. B., & Popova, E. (2010). Toward Monte Carlo simulation-based mechanistic-empirical prediction of Asphalt pavement performance. Journal of Transportation Engineering, 136(7), 678–688. https://doi.org/10.1061/(ASCE)0733-947X(2010)136:7(678)
  • Xiao, S.-Q., Tan, T., Xing, C., & Tan, Y. (2020). A contribution to texture analysis of pavements under simulated polishing: Some new findings. International Journal of Pavement Engineering, 23(7), 2370–2379. https://doi.org/10.1080/10298436.2020.1855351
  • Yu, J., Chou, E. Y. J., & Luo, Z. (2007). Development of linear mixed effects models for predicting individual pavement conditions. Journal of Transportation Engineering, 133(6), 347–354. https://doi.org/10.1061/(ASCE)0733-947X(2007)133:6(347)
  • Zhou, Q., Okte, E., & Al-Qadi, I. L. (2021). Predicting pavement roughness using Deep Learning algorithms. Transportation Research Record: Journal of the Transportation Research Board, 2675(11), 1062–1072. https://doi.org/10.1177/03611981211023765
  • Ziari, H., Sobhani, J., Ayoubinejad, J., & Hartmann, T. (2015). Analysing the accuracy of pavement performance models in the short and long terms: GMDH and ANFIS methods. Road Materials and Pavement Design, 17(3), 619–637. https://doi.org/10.1080/14680629.2015.1108218