Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data
Abstract
For analysis, design, and rehabilitation purposes of flexible pavements, the temperature profile of asphalt layers should be determined. The predictive models as an alternative to in-situ measurements, are rapid and easy methods to determine the temperature of asphalt layer at various depths. These models are developed based on limited field data. Hence, there is a need for developing new models for prediction of temperature profile of asphalt layers in various climatic regions. In this study, climatic data was retrieved from the Long-Term Pavement Performance (LTPP) database. The information of 33 asphalt pavement test sections in 16 states in the United States was employed for calibrating the predictive models. Using the prepared data, the temperature profile of asphalt layers was predicted utilizing four regression-based models, including Ramadhan and Wahhab, Hassan et al., Albayati and Alani, and Park et al. models. Existing prediction models were calibrated, and to predict the temperature profile of asphalt layer, new models were developed. Performance evaluation and validation of newly developed models showed an excellent correlation between predicted and measured values. Results show the ability of the developed models in predicting the temperature profile of asphalt layers with very good prediction precision (R2 = 0.94) and low bias.
Keyword : asphalt pavement, temperature profile of asphalt layers, prediction models, regression-based models, long-term pavement performance (LTPP)
How to Cite
Sedighian-Fard, M., Solatifar, N., & Sivilevičius, H. (2023). Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data. Journal of Civil Engineering and Management, 29(4), 329–341. https://doi.org/10.3846/jcem.2023.18611
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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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.
Solatifar, N., Kavussi, A., & Abbasghorbani, M. (2021). Dynamic modulus predictive models for In-service asphalt layers in hot climate areas. Journal of Materials in Civil Engineering, 33(2), 04020438. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003511
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pp. 401–412), Minneapolis, Minnesota, USA.
Tabatabaie, S. A., Ziari, H., & Khalili, M. (2008). Modeling temperature and resilient modulus of asphalt pavements for tropic zones of Iran. Asian Journal of Scientific Research, 1(6), 579–588. https://doi.org/10.3923/ajsr.2008.579.588
Velasquez, R., Marasteanu, M., Clyne, T. R., & Worel, B. (2008). Improved model to predict flexible pavement temperature profile. In 3rd International Conference on Accelerated Pavement Testing (APT). ‘Impacts and Benefits from APT Programs’, Madrid, Spain.
Wang, D. (2012). Analytical approach to predict temperature profile in a multilayered pavement system based on measured surface temperature data. Journal of Transportation Engineering, 138(5), 674–679. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000362
Wang, T. H., Su, L. J., & Zhai, J. Y. (2014). A case study on diurnal and seasonal variation in pavement temperature. International Journal of Pavement Engineering, 15(5), 402–408. http://doi.org/10.1080/10298436.2012.752825
Zhang, H. (2012). Study on grades of freeway meteorological disasters by risk matrix. Applied Mechanics and Materials, 178–181, 2788–2792. https://doi.org/10.4028/www.scientific.net/AMM.178-181.2788
Ariawan, A., Sugeng Subagio, B., & Hario Setiadji, B. (2015). Development of asphalt pavement temperature model for tropical climate conditions in West Bali region. Procedia Engineering, 125, 474–480. https://doi.org/10.1016/j.proeng.2015.11.126
Asefzadeh, A., Hashemian, L., & Bayat, A. (2017). Development of statistical temperature prediction models for a test road in Edmonton, Alberta, Canada. International Journal of Pavement Research and Technology, 10(5), 369–382. https://doi.org/10.1016/j.ijprt.2017.05.004
Diefenderfer, B. K., Al-Qadi, I. L., & Reubush, S. D. (2002). Prediction of daily temperature profile in flexible pavements. In 81st Annual Meeting ‘Transportation Research Board’, Washington, D.C., USA.
Diefenderfer, B. K., Al-Qadi, I. L., & Diefenderfer, S. D. (2006). Model to predict pavement temperature profile: development and validation. Journal of Transportation Engineering, 132(2), 162–167. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:2(162)
Federal Highway Administration. (2017). The long-term pavement performance program (Publication No. FHWA-HRT-15-049). Turner-Fairbank Highway Research Center, McLean, VA, USA.
Gedafa, D. S., Hossain, M., & Romanoschi, S. A. (2014). Perpetual pavement temperature prediction model. Journal of Road Materials and Pavement Design, 15(1), 55–65. https://doi.org/10.1080/14680629.2013.852610
Hassan, H. F., Al-Nuaimi, A. S., Taha, R., & Jafar, T. M. A. (2005). Development of asphalt pavement temperature models for Oman. Journal of Engineering Research, 2(1), 32–42. https://doi.org/10.24200/tjer.vol2iss1pp32-42
Islam, M. R., Ahsan, S., & Tarefder, R. A. (2015). Modeling temperature profile of hot-mix asphalt in flexible pavement. Journal of Pavement Research and Technology, 8(1), 47–52.
Kim, Y. R., & Lee, Y. C. (1995). Interrelationships among stiffnesses of asphalt-aggregate mixtures. Journal of the Association of Asphalt Paving Technologists, 64, 575–609.
Lukanen, E. O., Chunhua, H., & Skok, E. L. (1998). Probabilistic method of asphalt binder selection based on pavement temperature. Transportation Research Record: Journal of the Transportation Research Board, 1609(1), 12–20. https://doi.org/10.3141/1609-02
Minhoto, J. C., Pais, J. C., & Pereira, A. A. (2005). Asphalt pavement temperature prediction. Transportation Research Record: Journal of the Transportation Research Board, 1919(1), 96–110. https://doi.org/10.3141/1919-11
Opara, K., & Zieliński, J. (2017). Road temperature modelling without in-situ sensors. The Baltic Journal of Road and Bridge Engineering, 12(4), 241–247. https://doi.org/10.3846/bjrbe.2017.30
Park, D. Y., Buch, N., & Chatti, K. (2001). Effective layer temperature prediction model and temperature correction via falling weight deflectometer deflections. Transportation Research Record: Journal of the Transportation Research Board, 1764(1), 97–111. https://doi.org/10.3141/1764-11
Park, H. M., Kim, Y. R., & Park, S. (2002). Temperature correction of multiload-level falling-weight deflectometer deflections. Transportation Research Record: Journal of the Transportation Research Board, 1806(1), 3–8. https://doi.org/10.3141/1806-01
Pellinen, T. K. (2001). Investigation of the use of dynamic modulus as an indicator of hot-mix asphalt performance [PhD thesis]. School of Sustainable Engineering and the Built Environment, Arizona State University, USA.
Podvezko, V., & Sivilevičius, H. (2013). The use of AHP and rank correlation methods for determining the significance of the interaction between the elements of a transport system having a strong influence on traffic safety. Transport, 28(4), 389–403. https://doi.org/10.3846/16484142.2013.866980
Ramadhan, R. H., & Wahhab, A. H. (1997). Temperature variation of flexible and rigid pavements in eastern Saudi Arabia. Building and Environment, 32(4), 367–373. https://doi.org/10.1016/S0360-1323(96)00072-8
Sedighian-Fard, M., & Solatifar, N. (2021). Analysis of regression-based models for prediction of depth temperature of asphalt layers – A review. Amirkabir Journal of Civil Engineering, 53(9), 3985–4006. https://doi.org/10.22060/ceej.2020.18131.6793
Sedighian-Fard, M., & Solatifar, N. (2022). Development of a non-linear regression-based model for prediction of depth temperature of asphalt layers using LTPP data – case study: Ohio, USA. Journal of Transportation Engineering, 13(3), 1587–1600. https://doi.org/10.22119/jte.2021.217101.2426
Shao, L., Park, S. W., & Kim, Y. R. (1997). Simplified procedure for prediction of asphalt pavement subsurface temperatures based on heat transfer theories. Transportation Research Record: Journal of the Transportation Research Board, 1568(1), 114–123. https://doi.org/10.3141/1568-14
Solatifar, N., Abbasghorbani, M., Kavussi, A., & Sivilevičius, H. (2018). Prediction of depth temperature of asphalt layers in hot climate areas. Journal of Civil Engineering and Management, 24(7), 516–525. https://doi.org/10.3846/jcem.2018.6162
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.
Solatifar, N., Kavussi, A., & Abbasghorbani, M. (2021). Dynamic modulus predictive models for In-service asphalt layers in hot climate areas. Journal of Materials in Civil Engineering, 33(2), 04020438. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003511
Stubstad, R. N., Baltzer, S. E., Lukanen, O., & Ertman-Larsen, H. J. (1994). Prediction of AC mat temperatures for routine load-deflection measurements. In 4th International Conference on the ‘Bearing Capacity of Roads and Airfields’ (Vol. 1,
pp. 401–412), Minneapolis, Minnesota, USA.
Tabatabaie, S. A., Ziari, H., & Khalili, M. (2008). Modeling temperature and resilient modulus of asphalt pavements for tropic zones of Iran. Asian Journal of Scientific Research, 1(6), 579–588. https://doi.org/10.3923/ajsr.2008.579.588
Velasquez, R., Marasteanu, M., Clyne, T. R., & Worel, B. (2008). Improved model to predict flexible pavement temperature profile. In 3rd International Conference on Accelerated Pavement Testing (APT). ‘Impacts and Benefits from APT Programs’, Madrid, Spain.
Wang, D. (2012). Analytical approach to predict temperature profile in a multilayered pavement system based on measured surface temperature data. Journal of Transportation Engineering, 138(5), 674–679. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000362
Wang, T. H., Su, L. J., & Zhai, J. Y. (2014). A case study on diurnal and seasonal variation in pavement temperature. International Journal of Pavement Engineering, 15(5), 402–408. http://doi.org/10.1080/10298436.2012.752825
Zhang, H. (2012). Study on grades of freeway meteorological disasters by risk matrix. Applied Mechanics and Materials, 178–181, 2788–2792. https://doi.org/10.4028/www.scientific.net/AMM.178-181.2788