تحلیل مدل‌های رگرسیونی پیش‌بینی دمای عمق لایه‌های آسفالتی-مطالعه مروری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی عمران، دانشگاه ارومیه، ارومیه، ایران

2 گروه مهندسی عمران، دانشگاه ارومیه

چکیده

دمای عمق لایه‌های آسفالتی به دلیل رفتار ویسکوالاستیک مخلوط‌های آسفالتی، در ارزیابی سازه‌ای روسازی‌های انعطاف‌پذیر از اهمیت زیادی برخوردار است. دمای عمق لایه آسفالتی می‌تواند به طور مستقیم در محل اندازه‌گیری شود و یا توسط مدل‌هایی پیش‌بینی گردد. در این مقاله تحلیل جامعی در خصوص دوازده مدل رگرسیونی مهم و پرکاربرد پیش‌بینی دمای عمق لایه‌های آسفالتی صورت گرفته و با ارائه سوابق پژوهشی، به بررسی متغیرهای ورودی مدل، تحلیل حساسیت مدل نسبت به این متغیرها، ارزیابی عملکرد آن‌ها از لحاظ دقت و قدرت پیش‌بینی و نیز مقایسه برتری آن‌ها نسبت به یکدیگر پرداخته شده است. از آن جایی که اغلب این مدل‌ها در مناطق جغرافیایی و شرایط آب و هوایی خاصی توسعه داده شده‌اند، مدل‌های توسعه یافته با کالیبراسیون مدل‌های اصلی برای استفاده در شرایط محلی متفاوت نیز مورد بررسی قرار گرفته است. نتایج پژوهش‌ها نشان می‌دهد که مدل‌های رگرسیونی از عملکرد و دقت خوب و قابل قبولی برخوردار می‌باشند. در میان مدل‌های مورد بررسی، مدل BELLS با توجه به گستره داده‌های مورد استفاده در توسعه مدل، عملکرد و دقت مناسب و نیز در نظر گرفتن اثر پارامتر‌های مختلف به عنوان یکی از بهتر‌ین مدل‌های رگرسیونی پیش‌بینی دمای عمق لایه‌های آسفالتی شناخته می‌شود. همچنین مدل صولتی‌فر و همکاران به عنوان مدل اصلاح شده BELLS برای روسازی‌های تازه ساخت با دقت بالا و برای مناطق با وضعیت آب و هوایی گرم توسعه یافته است. در مجموع بررسی نتایج پژوهش‌ها نشان می‌دهد، مدل‌های توسعه یافته قابلیت کاربرد در پیش‌بینی دمای عمق لایه‌های آسفالتی را با اعمال تصحیحاتی برای روسا‌زی‌ها و شرایط محلی متفاوت دارند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Analysis of Regression-Based Models for Prediction of Depth Temperature of Asphalt Layers – A Review

نویسندگان [English]

  • Mohammad Sedighian-Fard 1
  • Nader Solatifar 2
1 Department of Civil Engineering, Urmia University, Urmia, Iran
2 Department of Civil Engineering, Urmia University, Urmia, Iran
چکیده [English]

Due to the viscoelastic behavior of asphalt mixtures, the depth temperature of asphalt layers is very important in the structural evaluation of flexible pavements. Depth temperature could be measured directly in the field or maybe predicted using prediction models. This paper presents a comprehensive analysis of different twelve regression-based models for the prediction of depth temperature of asphalt layers. With reference to the literature, required input parameters, sensitivity analysis, evaluation of prediction performance, as well as a comparison of the goodness of these models were discussed. Furthermore, calibrated models for different local conditions were presented. This is due to the fact that the original models were usually developed in specific geographical regions and climatic conditions. Results show that the regression-based models have a good performance and high accuracy in predicting the depth temperature of asphalt layers. Among the investigated models, according to the variety of data (or parameters) used in the model development, performance, considering the effect of various parameters, the BELLS model was introduced as one of the best regression-based models for the prediction of depth temperature of asphalt layers. The model developed by Solatifar et al. as a new version of the BELLS model showed very good accuracy for newly constructed pavements. In addition, with applying some modifications, it could be possible to use these models in different pavements and local conditions.

کلیدواژه‌ها [English]

  • Asphalt Pavement
  • Depth Temperature of Asphalt Layers
  • Temperature Predictive Models
  • Regression-Based Model
  • BELLS Model

مراجع

  1. 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.
  2. 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
  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, No .1568, pp. 114–123. doi: https://doi.org/10.3141/1568-14
  4. Marshall, C., Meier, R. and Welch, M. (2001). “Seasonal Temperature Effects on Flexible Pavements in Tennessee”, Transportation Research Record, No. 1764, Issue. 1, pp. 89-96. doi: https://doi.org/10.3141/1764-10
  5. Diefenderfer, B. K., Al-Qadi, I. L. and Reubush, S. D. (2002). “Prediction of Daily Temperature Profile in Flexible Pavements”, 81st Annual Meeting ‘Transportation Research Board’, Washington, D.C., USA.
  6. 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)
  7. Petersen, C. and Mahura, A. (2012). “Influence of the Pavement Type on the Road Surface Temperature”, Danish Meteorological Institute, Copenhagen, Denmark.
  8. Herb, W. M., Stefan, M. and Heinz, G. (2006). “Simulation and Characterization of Asphalt Pavement Temperatures”, Minnesota Department of Transportation (MNDOT), University of Minnesota.
  9. Fajing, P., Lei W., Jinke, J. and Wang, J. (2015). “A Research Review of Flexible Pavement Temperature Profile”, International Forum on ‘Energy, Environment Science and Materials’ (IFEESM), Shenzhen, China.
  10. 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.
  11. Kennedy, T., Huber, G., Harrigan, E., Cominsky, R., Hughes, C., Quintus, H. V. and Moulthrop, J. (1994). “Superior Performing Asphalt Pavements (Superpave)”, The Product of the SHRP Asphalt Research Program, National Research Council, Washington, D.C., USA.
  12. Mohseni, A. and Symons, M. (1998). “Effect of Improved LTPP AC Pavement Temperature Models on Superpave Performance Grades”, Proceedings of 77th Annual Meeting, Transportation Research Board, Washington, D.C., USA.
  13. (2017). “The Long-Term Pavement Performance Program”, Publication No. FHWA-HRT-15-049, Turner-Fairbank Highway Research Center, 6300 Georgetow Pike, McLean, VA 22101.
  14. 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
  15. Shao, J. and Lister, P. J. (1996). “An Automated Now casting Model of Road Surface Temperature and State for Winter Road Maintenance”, Journal of Applied Meteorology and Climatology (JAMC), Vol. 35, pp. 1352–1361. doi: https://doi.org/10.1175/1520-0450(1996)035%3C1352:AANMOR%3E2.0.CO;2
  16. Rayer, P. J. (1987). “The Meteorological Office Forecast Road Surface Temperature Model”, Meteor. Magazine, Vol. 116, pp. 180–191.
  17. Chapman, L., Thornes, J. E. and Bradley, A. V. (2001). “Modeling of Road Surface Temperatures from a Geographical Parameter Database, Part. 1, Stat. Meteor. Appl., Vol. 8, pp. 409–419. doi: https://doi.org/10.1017/S1350482701004030
  18. Akihiro, F., Akira, S. and Teruyuki, F. (2012). “A New Approach to Modeling Vehicle-Induced Heat and Its Thermal Effects on Road Surface Temperature”, Journal of Applied Meteorology and Climatology, Vol. 51. doi: https://doi.org/10.1175/JAMC-D-11-0156.1
  19. Xu, B., Dan, Han-Cheng. 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.2017.04.024
  20. Minhoto, J. C., Pais, J. C. and Pereira, A. A. (2005). “Asphalt Pavement Temperature Prediction”, Transportation Research Record: Journal of the Transportation Research Board (TRR Journal), Vol. 1, No. 1919. pp. 96-110. doi: https://doi.org/10.3141/1919-11
  21. Ramadhan, R. H. and Wahhab, A. H. (1997). “Temperature Variation of Flexible and Rigid Pavements in Eastern Saudi Arabia”, Journal of Building and Environment, Vol. 32, No. 4, pp. 367-373, Printed in Great Britain. doi: https://doi.org/10.1016/S0360-1323(96)00072-8
  22. 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.
  23. Velasquez, R., Marasteanu, M., Clyne, T. R. and Worel, B. (2008). “Improved Model to Predict Flexible Pavement Temperature Profile”, 3th International Conference on Accelerated Pavement Testing (APT). Impacts and Benefits from APT Programs, Madrid, Spain.
  24. 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: 3923/ajsr.2008.579.588
  25. 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
  26. Islam, M. R., Ahsan, S. and Tarefder, R. A. (2015). “Modeling Temperature Profile of Hot-Mix Asphalt in Flexible Pavement”, Journal of Pavement Research and Technology (IJPRT), Vol. 8, Issue. 8, No. 1, pp. 47-52.
  27. Ariawan, A., Sugeng Subagio, B. and Hario Setiadji, B. (2015). “Development of Asphalt Pavement Temperature Model for Tropical Climate Conditions in West Bali Region”, 5th International Conference of Euro Asia Civil Engineering Forum (EACEF-5), Procedia Engineering, Vol. 125, pp. 474-480. Doi: https://doi.org/10.1016/j.proeng.2015.11.126
  28. Albayati, A. H. K. and Alani, A. M. M. (2015). “Temperature Prediction Model for Asphalt Concrete Pavement”, 14th Annual International Conference on Pavement Engineering and Infrastructure, Liverpool, UK.
  29. 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
  30. 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
  31. 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.
  32. 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
  33. Mohseni, A. (1998). “LTPP Seasonal Asphalt Concrete Pavement Temperature Models”, Report FHWA-RD- 97-103, Federal Highway Administration, Washington, D.C., USA.
  34. Jia, L. and Sun, Y. (2008). “Asphalt Pavement Statistical Temperature Prediction Models Developed from Measured Data in China”, Plan, Build, and Manage Transportation Infrastructure in China, pp. 723–732.
  35. Sun, L, and Qin, J. (2006) “Prediction Model on Temperature Field in Asphalt Pavement”, J. Tongji Uni. (Nat. Sci.), Vol. 34, No. 4, pp. 480–483.
  36. Baltzer, S. and Jansen J. M. (1994). “Temperature Correction of Asphalt-Moduli for FWD-Measurements”, Proc., 4th International Conference on the ‘Bearing Capacity of Roads and Airfields’, Vol. 1, Minneapolis, Minn.
  37. (1994). “LTPP Seasonal Monitoring Program: Instrumentation Installation and Data Collection Guidelines”, Report No. FHWA-RD-94-110. McLean, VA.
  38. AASHTO T317. (2009). “Prediction of Asphalt-Bound Pavement Layer Temperatures”, American Association of State Highway and Transportation Officials, Washington, D.C., USA.
  39. (2016). “LTPP Guide to Asphalt Temperature Prediction and Correction”, Publication Number: FHWA-RD-98-085.
  40. Lukanen, E. O., Stubstad, R. and Briggs, R. C. (2000). “Temperature Predictions and Adjustment Factors for Asphalt Pavements”, Report FHWA-RD-98- 085, Department of Transportation, USA.
  41. 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 Persian)
  42. Park, D. Y., Buch, N. and Chatti, K. (2001). “Effective Layer Temperature Prediction Model and Temperature Correction Via Falling Weight Deflectometer Deflections”, Transport. Res. Rec. J. Transport. Res. Board, No. 1764, pp. 97–111.
  43. Solatifar, N. (2018). “Analysis of Conventional Dynamic Modulus Predictive Models of Asphalt Mixtures”, Amirkabir Journal of Civil Engineering. (in Persian)
نشریه مهندسی عمران امیرکبیر
دوره 53، شماره 9
آذر 1400
صفحه 3985-4006

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