Research Article
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Modelling the effects of flexible pavement distresses in the long-term pavement performance database on performance

Year 2023, Volume: 4 Issue: 2, 42 - 53, 31.12.2023

Ufuk Kırbaş Fazlullah Himat

https://doi.org/10.53635/jit.1309963

Abstract

Evaluating flexible pavement performance is mandatory for managing transport infrastructure. This study focuses on modeling the relationships between international roughness index (IRI) and a total of 10 types of pavement distress, including alligator, block, wheel path length, wheel path longitudinal, non-wheel path longitudinal, transverse crackings, patches, bleeding, raveling areas, and pumping. The data recorded under the Long-Term Pavement Performance was used to develop the models. Data sets covering General Pavement Studies from seven states of the United States were used in modeling. The study used modeling approaches, including nonlinear regression analysis, multivariate adaptive regression splines, and artificial neural networks (ANN), in which IRI was the dependent variable and pavement distress was the independent variable. In the developed models, 0.516, 0.623, and 0.684 regression coefficients values were obtained for nonlinear regression analysis, multivariate adaptive regression splines, and artificial neural networks approaches, respectively. The analysis results have determined that the artificial neural networks technique performs more successfully than the other techniques. The statistical error analyses of the root mean square error, Nash-Sutcliffe coefficient of efficiency, mean absolute error, and normalized root mean square error also showed that the same modeling approach performs more successfully. With these data generated from a universally used database, it has been determined once again that ANN is the most efficient mathematical approach in modeling the relationships between surface distresses and IRI.

Keywords

International Roughness Index Pavement Distress Long-Term Pavement Performance Distress Effect

References

  • Solatifar, N., & Lavasani, S. M. (2020). Development of an Artificial Neural Network model for asphalt pavement deterioration using LTPP data. Journal of Rehabilitation in Civil Engineering, 8(1), 121-132. https://doi.org/10.22075/JRCE.2019.17120.1328
  • Zaltuom, A. M. A., & Yulipriyono, E. (2011). Evaluation Pavement Distresses using Pavement Condition Index. Magister Teknik Sipil (Doctoral dissertation).
  • Alsheyari, K. A. O. (2017). A Case Study of Investigation the impact of International Roughness Index in developing pavement deterioration model in the United Arab Emirates. The British University in Dubai (BUiD)) (Doctoral dissertation).
  • Fang, X. (2017). Development of distress and performance models of composite pavements for pavement management. The University of North Carolina at Charlotte (Doctoral dissertation).
  • Heanue, K. (2007). Integrating Freight into Transportation Planning and Project-Selection Processes (No. NCHRP Project 8-53). Washington, DC: National Academies Press. https://doi.org/10.17226/23139
  • Kumar, R., Suman, S. K., & Prakash, G. (2021). Evaluation of pavement condition index using artificial neural network approach. Transportation in Developing Economies, 7(2), 20. https://doi.org/10.1007/s40890-021-00130-7
  • Terzi, S. (2013). Modeling for pavement roughness using the ANFIS approach. Advances in Engineering Software, 57, 59–64. https://doi.org/10.1016/j.advengsoft.2012.11.013
  • Abdelaziz, N., Abd El-Hakim, R. T., El-Badawy, S. M., & Afify, H. A. (2020). International Roughness Index prediction model for flexible pavements. International Journal of Pavement Engineering, 21(1), 88-99. https://doi.org/10.1177/03611981211017906
  • Shrestha, S., & Khadka, R. (2021). Assessment of Relationship Between Road Roughness And Pavement Surface Condition. Journal of Advanced College of Engineering and Management, 6, 177-185. https://doi.org/10.3126/jacem.v6i0.38357
  • Al-Omari, B., & Darter, I. (1995). Effect of pavement deterioration types on IRI and rehabilitation. Transportation Research Record, 1505, 57.
  • Mactutis, J. A., Alavi, S. H., & Ott, W. C. (2000). Investigation of relationship between roughness and pavement surface distress based on WesTrack project. Transportation Research Record, 1699, 107-113. https://doi.org/10.3141/1699-15
  • Dewan, S. A., & Smith, R. E. (2002). Estimating International Roughness Index from pavement distresses to calculate vehicle operating costs for the San Francisco Bay area. Transportation Research Record, 1816, 65-72. https://doi.org/10.3141/1816-08
  • Lin, J. D., Yau, J. T., & Hsiao, L. H. (2003). Correlation analysis between international roughness index (IRI) and pavement distress by neural network. Transportation Research Board, 12(16), 1-21.
  • Aultman-Hall, L., Jackson, E., Dougan, C. E., & Choi, S. N. (2004). Models relating pavement quality measures. Transportation Research Record, 1869, 119-125. https://doi.org/10.3141/1869-14
  • Hozayen, H. A., & Alrukaibi, F. (2008). Development of acceptance measures for long term performance of BOT highway projects. Al-Qadi, I.L., Sayed, T., Alnuaimi, N., Masad, E., In Efficient Transportation and Pavement Systems: Characterization, Mechanisms, Simulation, and Modeling, 347-360. London: CRC Press. https://doi.org/10.1201/9780203881200
  • Prasad, J. R., Kanuganti, S., Bhanegaonkar, P. N., Sarkar, A. K., & Arkatkar, S. (2013). Development of relationship between roughness (IRI) and visible surface distresses: a study on PMGSY roads. Procedia-Social and Behavioral Sciences, 104, 322-331. https://doi.org/10.1016/j.sbspro.2013.11.125
  • Meegoda, J. N., & Gao, S. (2014). Roughness progression model for asphalt pavements using long-term pavement performance data. Journal of Transportation Engineering, 140(8), 04014037. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000682
  • Kırbaş, U., & Karaşahin, M. (2016). Performance models for hot mix asphalt pavements in urban roads. Construction and Building Materials, 116, 281-288. https://doi.org/10.1016/j.conbuildmat.2016.04.118
  • Kırbaş, U. (2018). IRI sensitivity to the influence of surface distress on flexible pavements. Coatings, 8(8), 271. https://doi.org/10.3390/coatings8080271
  • Chandra, S., Sekhar, C. R., Bharti, A. K., & Kangadurai, B. (2013). Relationship between pavement roughness and distress parameters for Indian highways. Journal of Transportation Engineering, 139(5), 467-475. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000512
  • 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
  • Mubaraki, M. (2016). Highway subsurface assessment 2using pavement surface distress and roughness data. International Journal of Pavement Research and Technology, 9(5), 393-402. https://doi.org/10.3850/978-981-11-0449-7-329-cd
  • Joni, H. H., Hilal, M. M., & Abed, M. S. (2020, February). Developing International Roughness Index (IRI) Model from visible pavement distresses. Materials Science and Engineering, 737(1), 012119. https://doi.org/10.1088/1757-899X/737/1/012119
  • Qiao, Y., Chen, S., Alinizzi, M., Alamaniotis, M., & Labi, S. (2022). Estimating IRI based on pavement distress type, density, and severity: Insights from machine learning techniques. Transportation Reseach Board, 2022. https://doi.org/10.48550/arXiv.2110.05413
  • Zeiada, W., Hamad, K., Omar, M., Underwood, B. S., Khalil, M. A., & Karzad, A. S. (2019). Investigation and modelling of asphalt pavement performance in cold regions. International Journal of Pavement Engineering, 20(8), 986-997. https://doi.org/10.1080/10298436.2017.1373391
  • Elkins, G. E., & Ostrom, B. (2021). Long-term pavement performance information management system user guide (No. FHWA-HRT-21-038). United States: Federal Highway Administration. Office of Infrastructure Research and Development.
  • Ross, S. M. (2020). Introduction to probability and statistics for engineers and scientists. United States: Academic Press.
  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1-67. https://doi.org/10.1214/aos/1176347963
  • Weber, G. W., Batmaz, I., Köksal, G., Taylan, P., & Yerlikaya-Özkurt, F. (2012). CMARS: a new contribution to non-parametric regression with multivariate adaptive regression splines supported by continuous optimization. Inverse Problems in Science and Engineering, 20(3), 371-400. https://doi.org/10.1080/17415977.2011.624770
  • Attoh-Okine, N. O., Cooger, K., & Mensah, S. (2009). Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling. Construction and Building Materials, 23(9), 3020-3023. https://doi.org/10.1016/j.conbuildmat.2009.04.010
  • Attoh-Okine, N. O., Mensah, S., & Nawaiseh, M. (2003). A new technique for using multivariate adaptive regression splines (MARS) in pavement roughness prediction. In Proceedings of the Institution of Civil Engineers-Transport, 156(1), 51-55. https://doi.org/10.1680/tran.2003.156.1.51
  • Rounaghi, M. M., Abbaszadeh, M. R., & Arashi, M. (2015). Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique. Physica A: Statistical Mechanics and its Applications, 438, 625-633. https://doi.org/10.1016/j.physa.2015.07.021
  • Zhang, W. G., & Goh, A. T. C. (2013). Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Computers and Geotechnics, 48, 82-95. https://doi.org/10.1016/j.compgeo.2012.09.016
  • Soni, A., Yusuf, M., Beg, M., & Hashmi, A. W. (2022). An application of Artificial Neural Network (ANN) to predict the friction coefficient of nuclear grade graphite. Materials Today: Proceedings, 68, 701-709. https://doi.org/10.1016/j.matpr.2022.05.567
  • Juan, N. P., & Valdecantos, V. N. (2022). Review of the application of Artificial Neural Networks in ocean engineering. Ocean Engineering, 259, 111947. https://doi.org/10.1016/j.oceaneng.2022.111947
  • Kim, T., Shin, J. Y., Kim, H., Kim, S., & Heo, J. H. (2019). The use of large-scale climate indices in monthly reservoir inflow forecasting and its application on time series and artificial intelligence models. Water, 11(2), 374. https://doi.org/10.3390/w11020374
  • Fakhri, M., & Dezfoulian, R. S. (2019). Pavement structural evaluation based on roughness and surface distress survey using neural network model. Construction and Building Materials, 204, 768-780. https://doi.org/10.1016/j.conbuildmat.2019.01.142
  • Frost, J. (2020). Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models. U.S.A.: Statistics By Jim Publishing.
  • Rodriguez, R.N. (2023). Building Regression Models with SAS: A Guide for Data Scientists. Newyork City, U.S.A.: SAS Institute.
  • Aggarwal, C.C. (2023). Neural Networks and Deep Learning: A Textbook. Newyork City, U.S.A.: Springer International Publishing.
  • Kırbaş, U., Karaşahin, M., Demir, B., Komut, M., Ünal, E.N., (2018). Some approaches to the modeling of relationships between surface distresses and roughness in hot-mixed asphalts. Süleyman Demirel University Journal of Natural and Applied Sciences, 22(2), 901-912. https://doi.org/10.19113/sdufbed.32804

Year 2023, Volume: 4 Issue: 2, 42 - 53, 31.12.2023

Ufuk Kırbaş Fazlullah Himat

https://doi.org/10.53635/jit.1309963

Abstract

References

  • Solatifar, N., & Lavasani, S. M. (2020). Development of an Artificial Neural Network model for asphalt pavement deterioration using LTPP data. Journal of Rehabilitation in Civil Engineering, 8(1), 121-132. https://doi.org/10.22075/JRCE.2019.17120.1328
  • Zaltuom, A. M. A., & Yulipriyono, E. (2011). Evaluation Pavement Distresses using Pavement Condition Index. Magister Teknik Sipil (Doctoral dissertation).
  • Alsheyari, K. A. O. (2017). A Case Study of Investigation the impact of International Roughness Index in developing pavement deterioration model in the United Arab Emirates. The British University in Dubai (BUiD)) (Doctoral dissertation).
  • Fang, X. (2017). Development of distress and performance models of composite pavements for pavement management. The University of North Carolina at Charlotte (Doctoral dissertation).
  • Heanue, K. (2007). Integrating Freight into Transportation Planning and Project-Selection Processes (No. NCHRP Project 8-53). Washington, DC: National Academies Press. https://doi.org/10.17226/23139
  • Kumar, R., Suman, S. K., & Prakash, G. (2021). Evaluation of pavement condition index using artificial neural network approach. Transportation in Developing Economies, 7(2), 20. https://doi.org/10.1007/s40890-021-00130-7
  • Terzi, S. (2013). Modeling for pavement roughness using the ANFIS approach. Advances in Engineering Software, 57, 59–64. https://doi.org/10.1016/j.advengsoft.2012.11.013
  • Abdelaziz, N., Abd El-Hakim, R. T., El-Badawy, S. M., & Afify, H. A. (2020). International Roughness Index prediction model for flexible pavements. International Journal of Pavement Engineering, 21(1), 88-99. https://doi.org/10.1177/03611981211017906
  • Shrestha, S., & Khadka, R. (2021). Assessment of Relationship Between Road Roughness And Pavement Surface Condition. Journal of Advanced College of Engineering and Management, 6, 177-185. https://doi.org/10.3126/jacem.v6i0.38357
  • Al-Omari, B., & Darter, I. (1995). Effect of pavement deterioration types on IRI and rehabilitation. Transportation Research Record, 1505, 57.
  • Mactutis, J. A., Alavi, S. H., & Ott, W. C. (2000). Investigation of relationship between roughness and pavement surface distress based on WesTrack project. Transportation Research Record, 1699, 107-113. https://doi.org/10.3141/1699-15
  • Dewan, S. A., & Smith, R. E. (2002). Estimating International Roughness Index from pavement distresses to calculate vehicle operating costs for the San Francisco Bay area. Transportation Research Record, 1816, 65-72. https://doi.org/10.3141/1816-08
  • Lin, J. D., Yau, J. T., & Hsiao, L. H. (2003). Correlation analysis between international roughness index (IRI) and pavement distress by neural network. Transportation Research Board, 12(16), 1-21.
  • Aultman-Hall, L., Jackson, E., Dougan, C. E., & Choi, S. N. (2004). Models relating pavement quality measures. Transportation Research Record, 1869, 119-125. https://doi.org/10.3141/1869-14
  • Hozayen, H. A., & Alrukaibi, F. (2008). Development of acceptance measures for long term performance of BOT highway projects. Al-Qadi, I.L., Sayed, T., Alnuaimi, N., Masad, E., In Efficient Transportation and Pavement Systems: Characterization, Mechanisms, Simulation, and Modeling, 347-360. London: CRC Press. https://doi.org/10.1201/9780203881200
  • Prasad, J. R., Kanuganti, S., Bhanegaonkar, P. N., Sarkar, A. K., & Arkatkar, S. (2013). Development of relationship between roughness (IRI) and visible surface distresses: a study on PMGSY roads. Procedia-Social and Behavioral Sciences, 104, 322-331. https://doi.org/10.1016/j.sbspro.2013.11.125
  • Meegoda, J. N., & Gao, S. (2014). Roughness progression model for asphalt pavements using long-term pavement performance data. Journal of Transportation Engineering, 140(8), 04014037. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000682
  • Kırbaş, U., & Karaşahin, M. (2016). Performance models for hot mix asphalt pavements in urban roads. Construction and Building Materials, 116, 281-288. https://doi.org/10.1016/j.conbuildmat.2016.04.118
  • Kırbaş, U. (2018). IRI sensitivity to the influence of surface distress on flexible pavements. Coatings, 8(8), 271. https://doi.org/10.3390/coatings8080271
  • Chandra, S., Sekhar, C. R., Bharti, A. K., & Kangadurai, B. (2013). Relationship between pavement roughness and distress parameters for Indian highways. Journal of Transportation Engineering, 139(5), 467-475. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000512
  • 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
  • Mubaraki, M. (2016). Highway subsurface assessment 2using pavement surface distress and roughness data. International Journal of Pavement Research and Technology, 9(5), 393-402. https://doi.org/10.3850/978-981-11-0449-7-329-cd
  • Joni, H. H., Hilal, M. M., & Abed, M. S. (2020, February). Developing International Roughness Index (IRI) Model from visible pavement distresses. Materials Science and Engineering, 737(1), 012119. https://doi.org/10.1088/1757-899X/737/1/012119
  • Qiao, Y., Chen, S., Alinizzi, M., Alamaniotis, M., & Labi, S. (2022). Estimating IRI based on pavement distress type, density, and severity: Insights from machine learning techniques. Transportation Reseach Board, 2022. https://doi.org/10.48550/arXiv.2110.05413
  • Zeiada, W., Hamad, K., Omar, M., Underwood, B. S., Khalil, M. A., & Karzad, A. S. (2019). Investigation and modelling of asphalt pavement performance in cold regions. International Journal of Pavement Engineering, 20(8), 986-997. https://doi.org/10.1080/10298436.2017.1373391
  • Elkins, G. E., & Ostrom, B. (2021). Long-term pavement performance information management system user guide (No. FHWA-HRT-21-038). United States: Federal Highway Administration. Office of Infrastructure Research and Development.
  • Ross, S. M. (2020). Introduction to probability and statistics for engineers and scientists. United States: Academic Press.
  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1-67. https://doi.org/10.1214/aos/1176347963
  • Weber, G. W., Batmaz, I., Köksal, G., Taylan, P., & Yerlikaya-Özkurt, F. (2012). CMARS: a new contribution to non-parametric regression with multivariate adaptive regression splines supported by continuous optimization. Inverse Problems in Science and Engineering, 20(3), 371-400. https://doi.org/10.1080/17415977.2011.624770
  • Attoh-Okine, N. O., Cooger, K., & Mensah, S. (2009). Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling. Construction and Building Materials, 23(9), 3020-3023. https://doi.org/10.1016/j.conbuildmat.2009.04.010
  • Attoh-Okine, N. O., Mensah, S., & Nawaiseh, M. (2003). A new technique for using multivariate adaptive regression splines (MARS) in pavement roughness prediction. In Proceedings of the Institution of Civil Engineers-Transport, 156(1), 51-55. https://doi.org/10.1680/tran.2003.156.1.51
  • Rounaghi, M. M., Abbaszadeh, M. R., & Arashi, M. (2015). Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique. Physica A: Statistical Mechanics and its Applications, 438, 625-633. https://doi.org/10.1016/j.physa.2015.07.021
  • Zhang, W. G., & Goh, A. T. C. (2013). Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Computers and Geotechnics, 48, 82-95. https://doi.org/10.1016/j.compgeo.2012.09.016
  • Soni, A., Yusuf, M., Beg, M., & Hashmi, A. W. (2022). An application of Artificial Neural Network (ANN) to predict the friction coefficient of nuclear grade graphite. Materials Today: Proceedings, 68, 701-709. https://doi.org/10.1016/j.matpr.2022.05.567
  • Juan, N. P., & Valdecantos, V. N. (2022). Review of the application of Artificial Neural Networks in ocean engineering. Ocean Engineering, 259, 111947. https://doi.org/10.1016/j.oceaneng.2022.111947
  • Kim, T., Shin, J. Y., Kim, H., Kim, S., & Heo, J. H. (2019). The use of large-scale climate indices in monthly reservoir inflow forecasting and its application on time series and artificial intelligence models. Water, 11(2), 374. https://doi.org/10.3390/w11020374
  • Fakhri, M., & Dezfoulian, R. S. (2019). Pavement structural evaluation based on roughness and surface distress survey using neural network model. Construction and Building Materials, 204, 768-780. https://doi.org/10.1016/j.conbuildmat.2019.01.142
  • Frost, J. (2020). Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models. U.S.A.: Statistics By Jim Publishing.
  • Rodriguez, R.N. (2023). Building Regression Models with SAS: A Guide for Data Scientists. Newyork City, U.S.A.: SAS Institute.
  • Aggarwal, C.C. (2023). Neural Networks and Deep Learning: A Textbook. Newyork City, U.S.A.: Springer International Publishing.
  • Kırbaş, U., Karaşahin, M., Demir, B., Komut, M., Ünal, E.N., (2018). Some approaches to the modeling of relationships between surface distresses and roughness in hot-mixed asphalts. Süleyman Demirel University Journal of Natural and Applied Sciences, 22(2), 901-912. https://doi.org/10.19113/sdufbed.32804
There are 41 citations in total.

Details

Primary Language English
Subjects Transportation Engineering
Journal Section Research Articles
Authors

Ufuk Kırbaş 0000-0002-2389-425X

Fazlullah Himat 0000-0002-1362-5582

Publication Date December 31, 2023
Submission Date June 5, 2023
Acceptance Date December 19, 2023
Published in Issue Year 2023 Volume: 4 Issue: 2

Cite

APA Kırbaş, U., & Himat, F. (2023). Modelling the effects of flexible pavement distresses in the long-term pavement performance database on performance. Journal of Innovative Transportation, 4(2), 42-53. https://doi.org/10.53635/jit.1309963
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