Modeling of Asphalt Pavement Surface Temperature for Prevention of Icing on the Surface

Yıl 2024, , 1 - 21, 01.03.2024

Hüseyin Akbulut , Lale Atılgan Gevrek , Murat Ay

https://doi.org/10.18400/tjce.1211542

Cited By: 1

Öz

Hydronic heating systems are emerging as one of the best methods, which are environmentally friendly, clean, and sustainable modern ice prevention methods, an alternative to traditional ice precautions in the pavements. In this present study, temperatures were measured on asphalt samples prepared using the hydronic heating system when the air temperature in situ fell below 0 °C. T(minute), the temperature of influent (°C), air temperature (°C), temperature of effluent (°C) and pavement mean temperature (°C) were measured for four different asphalt samples. The results of the measurements were then modeled separately for four samples (345×4=1380 data) by using multiple linear regression (MLR), multi-layer perceptron (MLP), and radial basis neural network (RBNN). The results were discussed as tables and graphs. The performances of the models were evaluated using the root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2). According to the results, the RBNN models of four inputs had the best performance for each sample. The RBNN (4,0.6,9) model, which refers to 4-inputs, spread coefficient of 0.6 and hidden nodes of 9, of sample-3 with RMSE=0.76 °C and MAE=0.63 °C and R2=0.91 had the best performance among all models. In addition, it is thought that the models having low errors in this concept can be evaluated for early warning systems for the ice condition of the roads.

Anahtar Kelimeler

Ice prevention, modeling of pavement temperature, traffic safety

Destekleyen Kurum

Afyon Kocatepe University

Proje Numarası

Afyon Kocatepe University, 18.FENBİL.41.

Teşekkür

We would like to thank Afyon Kocatepe University, Scientific Research Projects Coordination Unit for supporting this study with the project number 18.FENBİL.41.

Kaynakça

• Fay, L., Volkening, K., Gallaway, C., Shi, X., Performance and Impacts of Current Deicing and Anti-Icing Products: User Perspective versus Experimental Data. Presented at 87th Annual Meeting of the Transportation Research Board, Washington DC., 08-1382, 2008.
• Houssain, SMK., Optimum De-Icing and Anti-Icing for Snow and Ice Control of Parking Lots and Sidewalks. PhD thesis in Civil Engineering. The University of Waterloo, 186, Canada, 2014.
• Adl-Zarrabi, B., Mirzanamadi, R., Jhonsson, J., Hydronic Pavement Heating for Sustainable Ice-free Roads. Transportation Research Procedia, 14, 704-713, 2016.
• Akbulut, H., Woodside, AR., Traffic Safety and Unprotected Road Users in Low and Middle Income Countries. Journal of Innovations in Civil Engineering and Technology, 1(1), 1-9, 2019.
• Blomqvist, S. Amiri, S. Rohdin, P. Ödlund, L., Analyzing the Performance and Control of a Hydronic Pavement System in a District Heating Network. Energies, 12(11), 2078, 2019.
• Feng, J., Yin, G., Thermal Analyses and Responses of Bridge Deck Hydronic Snow Melting System. Advances in Civil Engineering. 8172494, 14p, 2019.
• ASHRAE, Handbook of HVAC Applications, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc, Atlanta, GA, 2003.
• Ho, I-H., Li, S., Abudureyimu, S., Alternative Hydronic Pavement Heating System Using Deep Direct Use of Geothermal Hot Water. Cold Regions Science and Technology, 160, 194-208, 2019.
• Liu X., Spitler J.D., A Simulation Tool for the Hydronic Bridge Snow Melting System, 12th International Road Weather Conference, 2003.
• Anand, P., Nahvi, A., Ceylan, H., Pyrialakou, V.D., Gkritza, K., Gopalakrishnan, K., Kim, S., Taylor, PC., Energy and Financial Viability of Hydronic Heated Pavement Systems, National Technical Information Services (NTIS), Springfield, Virginia 22161, No. DOT/FAA/TC17/47, 2007.
• Lund, JW., Pavement Snow Melting. Geo-Heat Center Quarterly Bulletin, 21(2), 12-19, 2000.
• Fujimoto, A., Tokunaga, RA., Kiriishi, M., Kawabata, Y., Takahashi Ishida, T., Fukuhara, T., A road surface freezing model using heat, water and salt balance and its validation by field experiments. Cold Reg. Sci. Technol., 106-107, 01-10, 2014.
• Akbulut, H., Gürer, C., Gevrek, L., Highway Pavement Surface Icing and Traffic Safety. International Journal of Scientific Engineering Research, 9(8), 6-9, 2018.
• Xu, H., Wang, D., Tan, Y., Zhou, J., Oeser, M., Investigation of design alternatives for hydronic snow melting pavement systems in China. Journal of Cleaner Production, 170, 1413-1422, 2018.
• Gevrek Atılgan, L., Modeling of Anti-Icing Systems with Geothermal Energy in Highway Pavements. Afyon Kocatepe University, Graduate School of Natural and Applied Sciences, PhD Thesis, Turkey, 2021.
• Gustafson, K., Icing Conditions on Different Pavement Structures. Transportation Research Record 860. Nr 84, ISSN 0347-6049, 1983.
• Xiao, J., Kulakowski, BT., El-Gindy, M., Prediction of Risk of Wet-Pavement Accidents: Fuzzy Logic Model. Transportation Research Record, 1717(1), 2000.
• Eugster, WJ., Road and Bridge Heating Using Geothermal Energy Overview and Examples. Proceedings European Geothermal Congress, Unterhaching, Germany, 30 May-1 June, 2007.
• Chiasson, AD., Spitler, JD., Rees, SJ., Smith, MD., A model for simulating the performance of a pavement heating system as a supplemental heat rejecter with closed-loop ground-source heat pump systems. Journal of Solar Energy Engineering, 122(4), 183-191, 2000.
• Minhoto, MJC., Pais, JC., Pereira, PAA., Picado-Santos, LG., Predicting asphalt pavement temperature with a three-dimensional finite element method. Transp. Res. Rec., 1919(1), 96-110, 2005.
• Liu, X., Rees SJ., Spitler, JD., Modeling snow melting on heated pavement surfaces. Part I: Model development. App Therm Eng, 27, 1115-1124, 2007a.
• Liu, X., Rees SJ., Spitler, JD., Modeling snow melting on heated pavement surfaces, Part II: Experimental validation. App Therm Eng, 27, 1125-1131, 2007b.
• Wang, H., Chen, Z., Study of critical free-area ratio during the snow-melting process on pavement using low-temperature heating fluids. Energy Conversion and Management, 50(1), 157-165, 2009.
• Xu, H., Tan, Y., Modeling and Operation Strategy of Pavement Snow Melting Systems Utilizing Low-Temperature Heating Fluids. Energy, 80, 666-676, 2015.
• Mirzanamadi, R., Hagentoft, CE., Johansson, P., Johnsson, J., Anti-icing of Road Surfaces Using Hydronic Heating Pavement with Low Temperature. Cold Regions Science and Technology, 145, 106-118, 2018.
• Jhonsson, J., Adl-Zarrabi, B., Modeling the thermal performance of low temperature hydronic heated pavements. Cold Regions Science and Technology, 161, 81-90, 2019.
• Chen, J., Wang, H., Xie, P., Pavement temperature prediction: theoretical models and critical affecting factors. Applied Thermal Engineering, 158, 113755, 2019.
• Zeiada, W., Hamad, K., Omar, M., Underwood, BS., Khalil, MA., Karzad, AS., Investigation and modelling of asphalt pavement performance in cold regions. International Journal of Pavement Engineering, 20(8), 986-997, 2019.
• Mirzanamadi, R., Hagentoft, CE., Johansson, P., Coupling a Hydronic Heating Pavement to a Horizontal Ground Heat Exchanger for harvesting solar energy and heating road surfaces. Renewable Energy,147(Part 1), 447-463, 2020.
• Zhao, W., Su, W., Li, L., Zhang, Y., Li, B., Optimization design of the road unit in a hydronic snow melting system with porous snow. Journal of Thermal Analysis and Calorimetry, 141, 1509-1517, 2020.
• Marcelino, P., Antunes, MDL., Fortunato, E., Gomes, MC., Machine learning approach for pavement performance prediction. International Journal of Pavement Engineering, 22(3), 341354, 2021.
• Rigabadi, A., Herozi, MRZ., Rezagholilou, A., An attempt for development of pavements temperature prediction models based on remote sensing data and artificial neural network. International J of Pavement Engineering, 23(9), 2912-2921, 2021.
• Tabrizi, SE., Xiao, K., Thè, JVG., Saad, M., Farghaly, H., Yang, SX., Gharabaghi, B., Hourly road pavement surface temperature forecasting using deep learning models. Journal of Hydrology, 603, Part A, 126877, 2021.
• Ay, M., Kisi, Ö., Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques. Journal of Hydrology, 511, 279-289, 2014.
• Haykin, S., Neural networks: A comprehensive foundation. 2nd Ed., Prentice-Hall, Upper Saddle River, NJ., 1998.
• Marquardt, D., An algorithm for least squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Maths., 11(2), 431-441, 1963.
• Broomhead, D., Lowe, D., Multivariable functional interpolation and adaptive networks. Complex Syst., 2(6), 321-355, 1988.
• Poggio, T., Girosi, F., Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247(4945), 978-982, 1990.
• Wang, H., Zhao, J., Chen, Z., Experimental Investigation of Ice and Snow Melting Process on Pavement Utilizing Geothermal Tail Water. Energy Conversion and Management, 49, 1538-1546, 2008.