Artificial Neural Network Model for Prediction of Depth Temperature of Asphalt Layers Using LTPP Data – Case Study: Ohio, USA

Document Type : Original Research

Authors
M. Sedighian-Fard
N. Solatifar
Department of Civil Engineering, Urmia University
Abstract
One of the critical environmental factors that affect the deformation of flexible pavements is the depth temperature of asphalt layers. This is due to the viscoelastic behavior of the asphalt mixtures. The stiffness of the asphalt layers has a significant effect on the structural capacity of flexible pavements. This property is a function of the asphalt layer temperature and changes daily and seasonally. As the temperature increases, the stiffness of the asphalt layer decreases, which increases the stress in the base and subbase layers of the pavement. Therefore, the pavement response to the applied loads is affected by the depth temperature. Hence, the depth temperature of asphalt layers is one of the most important and main factors in the analysis, design, and rehabilitation process of flexible pavements. Some predictive models have been developed to determine the depth temperature of asphalt layers in pavement maintenance and rehabilitation activities. These models, as an alternative to field and laboratory measurements of this factor, are low-cost, rapid, and simple methods to determine the depth temperature of asphalt layers. It should be noted that these models are based on the limited field and laboratory data, therefore, there is a need for developing new models for prediction of the depth temperature of asphalt layers in different traffic and climatic conditions. The objective of this study is to develop a model for predicting the depth temperature of asphalt layers based on climatic data. In recent years, Artificial Neural Networks (ANNs) have shown good performance as a useful tool for modeling physical events. The modeling method used in this study is a Back-Propagation Neural Network (BPNN) model that predicts the average hourly depth temperature of asphalt layers based on several variables, including the time of the day, desired depth from the pavement surface, average hourly air temperature, average speed and direction of the wind, minimum air humidity and total solar radiation. Data was extracted from the Long-Term Pavement Performance (LTPP) database. After extracting and preparing raw data, all the needed data were acquired from different data tables and linked to each other in a database. As a case study, data points collected from pavements in Ohio, USA, has been used for modeling. Also, to ascertain the presence or absence of multicollinearity between independent variables, the Pearson correlation test has been conducted. For this reason, the maximum speed and direction of the wind and maximum air humidity parameters were removed from the data set. According to the results of the Pearson correlation test, the average hourly air temperature has the most powerful impact on the average hourly temperature of the asphalt layer depth (correlation=95.2%). After training and testing the neural network, the performance of the developed model has been evaluated, and results were compared with a non-linear quadratic regression model. The results show that the developed model is more accurate than the regression-based model. In addition, the ability of the developed model in predicting the depth temperature of asphalt layers based on existing climatic data with a very good prediction accuracy (R2=0.96) and very low bias and error has been shown. Furthermore, the performance of the developed model has some restrictions for the prediction of depth temperature of asphalt layers. Other factors such as material characteristics can be scrutinized and applied to enhance the performance and applicability of the model.

Keywords

Subjects

[1] Hen-Cheng, Dan., Lin-Hua, He. and Xu, B. (2016). “Experimental Investigation on Skid Resistance of Asphalt Pavement under Various Slippery Conditions”, Int. J. Pavement Eng. doi: http://dx.doi.org/10.1080/10298436.2015.1095901
[2] Zhang, H. (2012). “Study on Grades of Freeway Meteorological Disasters by Risk Matrix”, Appl. Mech. Mater., Vol. 178, pp. 2788–2792.
[3] Shao, L., Park, S. W. and Kim, Y. R. (1997). “Simplified Procedure for Prediction of Asphalt Pavement Subsurface Temperatures Based on Heat Transfer Theories”, Transportation Research Record, Vol. 1568, pp. 114–123. doi: https://doi.org/10.3141/1568-14
[4] Kim, Y. R, and Lee, Y. C. (1995). “Interrelationships Among Stiffnesses of Asphalt-Aggregate Mixtures”, Journal of the Association of Asphalt Paving Technologists, Vol. 64, pp. 575–609.
[5] Park, H. M., Kim, Y. R. and Park, S. (2002). “Temperature Correction of Multiload-Level Falling-Weight Deflectometer Deflections”, Transportation Research Record, Vol. 1806, pp. 3–8. doi: https://doi.org/10.3141/1806-01
[6] Petersen, C. and Mahura, A. (2012). “Influence of the Pavement Type on the Road Surface Temperature”, Danish Meteorological Institute, Copenhagen, Denmark.
[7] Irwin, R. S. and Boston, I. (2005). “Rigid and Flexible Pavement Design”, Transportation Research Record, Journal of the Transportation Research Board, No. 1919, Washington, D.C., USA.
[8] Arifuzzaman, M. (2017). “Advanced ANN Prediction of Moisture Damage in CNT Modified Asphalt Binder”, Soft Computing in Civil Engineering, Vol. 1, No. 1, pp. 1-11. doi: https://doi.org/10.22115/scce.2017.46317
[9] Naderpour, H., Nagai, K., Fakharian, P. and Haji, M. (2019). “Innovative Models for Prediction of Compressive Strength of FRP-Confined Circular Reinforced Concrete Columns Using Soft Computing Methods”, Composite Structures, Vol. 215, pp. 69-84. doi: https://doi.org/10.1016/j.compstruct.2019.02.048
[10] Solatifar, N. and Lavasani, S. M. (2020). “Development of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data”, Journal of Rehabilitation in Civil Engineering, Vol. 8, No. 1, pp. 121-132. doi: https://dx.doi.org/10.22075/jrce.2019.17120.1328
[11] Karlaftis, M. G. and Vlahogianni, E. I. (2011). “Statistical Methods Versus Neural Networks in Transportation Research: Differences, Similarities and Some Insights”, Transportation Research Part C, Vol. 19, No. 3, pp. 387-399
[12] Golshani, N., Shabanpour, R., Mohammadifard, S., Derriblr, S. and Mohammadian, A. (2017). “Comparison of Artificial Neural Networks and Statistical Copula-Based Joint Models”, Transportation Research Board, No. 17, 96th Annual Meeting, Washington, D.C., USA.
[13] Xu, B., Han-Cheng, Dan. and Li, L. (2017). “Temperature Prediction Model of Asphalt Pavement in Cold Regions Based on an Improved BP Neural Network”, Applied Thermal Engineering, Vol. 120, pp. 568-580. doi: https://dx.doi.org/10.1016/j.applthermaleng.2017.04.0
[14] Matic, B., Matic, D., Sremac, S., Radovic, N. and Vidikant, P. (2014). “A Model for the Pavement Temperature Prediction at Specified Depth Using Neural Networks”, Metalurgija, Vol. 53, No. 4.
[15] Godoy, J., Haber, R., Muñoz, J. J., Matía, F. and García, Á. (2018). “Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications”, Sensors (Basel), Vol. 18, No. 7. doi: https://doi.org/10.3390/s18072092.
[16] Stubstad, R. N., Baltzer, S., Lukanen, E. O. and 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’, Minneapolis, Minnesota, USA.
[17] Lukanen, E. O., Chunhua, H. and Skok, E. L. (1998). “Probabilistic Method of Asphalt Binder Selection Based on Pavement Temperature”, Transportation Research Record, Transportation Research Board, Vol. 1609, Issue. 1, pp. 12-20. doi: https://doi.org/10.3141/1609-02
[18] Tabatabaie, S. A., Ziari, H. and Khalili, M. (2008). “Modeling Temperature and Resilient Modulus of Asphalt Pavements for Tropic Zones of Iran”, Asian Journal of Scientific Research, Vol. 1, pp. 579-588. doi: 10.3923/ajsr.2008.579.588
[19] Hassan, H. F., Al-Nuaimi, A. S., Taha, R. and Jafar, T. M. A. (2005). “Development of Asphalt Pavement Temperature Models for Oman”, Journal of Engineering Research, Vol. 2, No. 1. pp. 32-42.
[20] Diefenderfer, B. K., Al-Qadi, I. L. and Diefenderfer, S. D. (2006). “Model to Predict Pavement Temperature Profile: Development and Validation”, Journal of Transportation Engineering, Vol. 132 Issue. 2. doi: https://doi.org/10.1061/(ASCE)0733-947X(2006)132:2(162)
[21] Solatifar, N., Abbasghorbani, M., Kavussi, A. and Sivilevičius, H. (2018). “Prediction of Depth Temperature of Asphalt Layers in Hot Climate Areas”, Journal of Civil Engineering and Management, Vol. 24, No. 7, pp. 516-525. doi: https://doi.org/10.3846/jcem.2018.6162
[22] Sedighian-Fard, M. and Solatifar, N. (2020). “Analysis of Regression-Based Models for Prediction of Depth Temperature of Asphalt Layers – A Review”, Amirkabir Journal of Civil Engineering, doi: 10.22060/CEEJ.2020.18131.6793
[23] Gedafa, D. S., Hossain, M. and Romanoschi, S. A. (2014). “Perpetual Pavement Temperature Prediction Model”, Journal of Road Materials and Pavement Design, Vol. 15, No. 1, pp. 55–65, doi: http://dx.doi.org/10.1080/14680629.2013.852610
[24] Li. Y., Liu, L. and Sun, L. (2018). “Temperature Predictions for Asphalt Pavement with Thick Asphalt Layer”, Construction and Building Materials, Vol. 160, pp. 802-809. doi: https://doi.org/10.1016/j.conbuildmat.2017.12.145
[25] Asefzadeh, A., Hashemian, L. and Bayat, A. (2017). “Development of Statistical Temperature Prediction Models for a Test Road in Edmonton, Alberta, Canada”, International Journal of Pavement Research and Technology, Vol. 10, Issue. 5, pp. 369-382. doi: https://doi.org/10.1016/j.ijprt.2017.05.004
[26] Rafiq, M. Y., Bugmann, G. and Easterbrook, D. J. (2001). Neural Network Design for Engineering Applications”, Computers & Structures, Vol. 79, No. 17, pp. 1541-1552.
[27] Zhang, G., Patuwo, B. E. and Hu, M. Y. (1998). “Forecasting with Artificial Neural Networks: The State of the Art”, International Journal of Forecasting, Vol. 14, No. 1, pp. 35-62.
[28] Alharbi, F. (2018). “Predicting Pavement Performance Utilizing Artificial Neural Network (ANN) Models”, Ph.D. Thesis, Iowa State University, USA.
[29] Solatifar, N. and Abbasghorbani, M. (2019). “Calibration of Regression Models Based on Viscoelastic Principles for Prediction of Dynamic Modulus of In-Service Asphalt Layers”, Journal of Transportation Engineering, In press.
Modares Civil Engineering journal
Volume 21, Issue 3
May and June 2021
Pages 91-105