پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 423 |
| تعداد مقالات | 5,542 |
| تعداد مشاهده مقاله | 6,470,762 |
| تعداد دریافت فایل اصل مقاله | 5,541,654 |
تحلیل عدم قطعیت فرآیند اضمحلال روسازی آسفالتی مبتنی بر شاخص ناهمواری با استفاده از داده‌های LTPP | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 1، دوره 53، شماره 4، تیر 1400، صفحه 1259-1274 اصل مقاله (1.68 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2019.16072.6116 | ||
| نویسنده | ||
| نادر صولتی فر* | ||
| دانشگاه ارومیه، ارومیه، ایران | ||
| چکیده | ||
| مدل‌های اضمحلال از اجزای مهم هر سیستم مدیریت روسازی (PMS) می‌باشند که امکان پیش‌بینی وضعیت روسازی را در زمان بهره‌برداری آن فراهم می‌کنند. اضمحلال روسازی فرآیندی بسیار پیچیده بوده و دارای عدم قطعیت زیادی است. مدل‌های احتمالاتی پیش‌بینی اضمحلال در مقایسه با مدل‌های قطعی امکان در نظر گرفتن این عدم قطعیت را دارا هستند. یکی از مهم‌ترین مدل‌های احتمالاتی پیش‌بینی اضمحلال، مدل منحنی روند است که مبتنی بر شاخص ناهمواری به عنوان یکی از مهم‌ترین شاخص‌های عملکردی روسازی می‌باشد. در این پژوهش از داده‌های ناهمواری روسازی قطعات GPS-1 و GPS-2 از پایگاه داده LTPP، که در واقع روسازی‌های آسفالتی در حال بهره‌برداری به ترتیب با اساس دانه‌ای و اساس تثبیت‌شده هستند، جهت تحلیل عدم قطعیت فرآیند اضمحلال روسازی استفاده شده است. برای این منظور از دو آزمون برازندگی مجذور کای (χ2) و کولموگروف – اسمیرنف (K-S) جهت تعیین توزیع احتمال وضعیت آینده روسازی نسبت به وضعیت فعلی آن در سال‌های مختلف استفاده شده و توزیع لاگ‌نرمال بیشترین مقدار همبستگی با توزیع واقعی داده‌ها را در بلندمدت ارائه کرده است. با داشتن این توزیع، مدل پیش‌بینی احتمالاتی اضمحلال روسازی مبتنی بر شاخص ناهمواری با استفاده از مدل منحنی روند توسعه یافته است. با بهره‌گیری از مدل توسعه داده شده، سیستم مدیریت روسازی می‌تواند وضعیت آینده روسازی را با ملاحظه عدم قطعیت فرآیند اضمحلال پیش‌بینی نماید و با تخصیص بودجه بهینه، وضعیت شبکه روسازی را در سطح ریسک معینی نگهداری کند و از تحمیل هزینه‌های گزاف ناشی از در نظر نگرفتن ریسک‌های احتمالی متاثر از عدم قطعیت وضعیت اضمحلال روسازی جلوگیری نماید. | ||
| کلیدواژه‌ها | ||
| سیستم مدیریت روسازی (PMS)؛ اضمحلال روسازی؛ شاخص ناهمواری بین‌المللی (IRI)؛ عدم قطعیت؛ LTPP | ||
| موضوعات | ||
| انواع خرابی روسازی انعطاف پذیر؛ تعمیر و تقویت روسازی انعطاف پذیر؛ روشهای تحلیلی | ||
| عنوان مقاله [English] | ||
| Analysis of Uncertainties in Deterioration Process of Asphalt Pavements based on Roughness Index Using LTPP Data | ||
| نویسندگان [English] | ||
| Nader Solatifar | ||
| Urmia University | ||
| چکیده [English] | ||
| Pavement deterioration models are the most important components of any Pavement Management System (PMS). These models could predict pavement condition at any time in its service life. Pavement deterioration is a very complicated and uncertain process. Probabilistic deterioration models in comparison with deterministic ones could take into account these uncertainties. One of the most important probabilistic pavement deterioration models, is the trend curve model that is based on pavement roughness. In this research, roughness data of GPS-1 and GPS-2 pavement sections, which are in-service asphalt pavements respectively with granular base and stabilized base layers, have been extracted from Long-Term Pavement Performance (LTPP) database. These data then were used for analyzing pavement deterioration uncertainties. For this purpose, Chi-square (χ2) and Kolmogorov-Smirnov (K-S) statistical tests were used to determine probability distribution of pavement future condition over its current condition ratio in different years. Results showed that lognormal distribution is more fitted with actual data in long-term pavement life. Having this distribution, pavement deterioration model was developed based on roughness index using trend curve model. Utilizing proposed model, the pavement management system could predict pavement future condition taking into account uncertainties of deterioration process and with optimal budget assignment, could maintain the network condition at a specified risk level. This could prevent of any future risk regarding the pavement deterioration uncertainties. | ||
| کلیدواژه‌ها [English] | ||
| Pavement Management System (PMS), Pavement Deterioration, International Roughness Index (IRI), Uncertainty, LTPP | ||
| مراجع | ||
|
[1] Haas, R., Hudson, W.R. and Falls, L.C. (2015). “Pavement Asset Management”, Scrivener Publishing with John Wiley & Sons. [2] Carnahan, J.V., Davis, W.J., Shahin, M.Y., Keane, P.L., and Wu, M.I. (1987). “Optimal Maintenance Decisions for Pavement Management”, Journal of Transportation Engineering, Vol. 113, No. 5, pp. 554-572. [3] Kim, Y.R. (2009). “Modeling of Asphalt Concrete”, ASCE press, McGraw-Hill. [4] Bekheet, W., Helali, K., Halim, A. and Springer, J. (2005). “A Comprehensive Approach for the Development of Performance Models for Network-Level PMS Using LTPP Data”, Proceedings of 84th Annual Meeting of TRB, Washington, D.C. [5] Alberti, S. and Fiori, F. (2019). “Integrating Risk Assessment into Pavement Management Systems”, Journal of Infrastructure Systems, Vol. 25, No. 1, pp. 1-21. doi: https://doi.org/10.1061/(ASCE)IS.1943-555X.0000472 [6] Seyedshohadaie, S.R., Damnjanovic, I.D. and Butenko, S. (2010). “Risk-Based Maintenance and Rehabilitation Decisions for Transportation Infrastructure Networks”, Transportation Research, Part A, Vol. 44, No. 4, pp. 236-248. [7] Shahin, M.Y. (2005). “Pavement Management for Airports, Roads, and Parking Lots”, Chapman & Hall, N.Y. [8] Haas, R., Hudson, W.R. and Zaniewski, J.P. (1994). “Modern Pavement Management”, Krieger, Malabor, Fla. [9] Durango, P. and Madanat, S. (2002). “Optimal Maintenance and Repair Policies in Infrastructure Management Under Uncertain Facility Deterioration Rates: An Adaptive Control Approach”, Transportation Research, Part A, Vol. 36, No. 9, pp. 763–778. [10] Silva, F., Dam, T.V., Bulleit, W. and Ylitalo, R. (2000). “Proposed Pavement Performance Models for Local Government Agencies in Michigan” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1699, pp. 81–86. [11] Porras-Alvarado, J.D., Zhang, Z. and Salazar, L.G.L. (2014). “Probabilistic Approach to Modeling Pavement Performance Using IRI Data”, Proceedings of 93rd Annual Meeting of TRB, Washington, D.C. [12] AASHTO. (2010). “AASHTO Pavement Management Guide”, American Association of State Highway and Transportation Officials, Washington, D.C. [13] Tsunokawa, K. and Schofer, J. (1994). “Trend Curve Optimal Control Model for Highway Pavement Maintenance: Case Study and Evaluation”, Transportation Research, Part A, Vol. 28, No. 2, pp. 151–166. [14] Garber, N.J. and Hoel, L.A. (1996). “Traffic and Highway Engineering”, 2nd Edition, Thomson Publishing Inc, PWS. [15] Martin, T. (1996). “A Review of Existing Pavement Performance Relationships”, ARRB Transport, Reasearch: 60, Vermont South, Victoria, Australia. [16] Zaabar, I. and Chatti, K. (2010). “Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U. S. Conditions”, Transportation Research Record: Journal of the Transportation Research Board, No. 2155, pp. 105-116. [17] Ozbay, K. and Laub, R. (2001). “Models for Pavement Deterioration Using LTPP”, Report no. FHWA-NJ-1999-030, Federal Highway Administration, Washington, D.C. [18] ARA. (2004). “Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures”, NCHRP 1-37A, National Cooperative Highway Research Program, Transportation Research Board, National Research Council, Washington, D.C. [19] Prozzi, J. and Madanat, S. (2004). “Development of Pavement Performance Models by Combining Experimental and Field Data”, Journal of Infrastructure Systems, Vol. 10, No. 1, pp. 9-22. [20] Kargah-Ostadi, N., Stoffels, S. and Tabatabaee, N. (2010). “Network-Level Pavement Roughness Prediction Model for Rehabilitation Recommendations”, Proceedings of 89th Annual Meeting of TRB, Washington, D.C. [21] Khattak, M.J., Nur, M.A., Bhuyan, M.R-U-K. and Gaspard, K. (2013). “International Roughness Index Models for HMA Overlay Treatment of Flexible and Composite Pavements for Louisiana”, Proceedings of 92nd Annual Meeting of TRB, Washington, D.C. [22] Mohamed Jaafar, Z.F., Uddin, W. and Najjar, Y. (2016). “Asphalt Pavement Roughness Modeling Using the Artificial Neural Network and Linear Regression Approaches for LTPP Southern U.S. States”, Proceedings of 95th Annual Meeting of TRB, Washington, D.C. [23] Madanat, S. (1993). “Optimal Infrastructure Management Decisions Under Uncertainty”, Transportation Research, Part C, Vol. 1, No. 1, pp. 77-88. [24] Smilowitz, K. and Madanat, S. (2000). “Optimal Inspection and Maintenance Policies for Infrastructure Networks”, Computer Aided Civil Infrastructure Engineering, Vol. 15, No. 1, pp. 5–13. [25] Jia, X., Huang, B., Zhu, D., Dong, Q. and Woods, M. (2018). “Influence of Measurement Variability of International Roughness Index on Uncertainty of Network-Level Pavement Evaluation”, Journal of Transportation Engineering, Part B: Pavements, Vol. 144, No. 2, doi: https://doi.org/10.1061/JPEODX.0000034 [26] Noshadravan, A., Wildnauer, M., Gregory, J. and Kirchain, R. (2013). “Comparative Pavement Life Cycle Assessment with Parameter Uncertainty”, Transportation Research, Part D, Vol. 25, Dec 2013, pp. 131-138. doi: https://doi.org/10.1016/j.trd.2013.10.002 [27] FHWA. (2009). “Long-Term Pavement Performance Information Management System: Pavement Performance Database User Reference Guide”, Publication No. FHWA-RD-03-088, Federal Highway Administration, Washington, D.C. [28] Solatifar, N., Behnia, C. and Aflaki, S. (2011). “A Review to Experiences of Different Countries in Implementing Long-Term Pavement Performance (LTPP) Program”, Proceedings of 6th National Congress on Civil Engineering, Semnan, Iran (in Persian). [29] FHWA. (2009). “Long-Term Pavement Performance (LTPP) Standard Data Release 23.0.”, Federal Highway Administration, <http://www.ltpp-products.com> (May. 19, 2011). [30] Solatifar, N., Aflaki, S. and Behnia, C. (2013). “A Comprehensive Review of Deterministic and Stochastic Pavement Deterioration Prediction Models”, National Conference on Transportation Infrastructures, Tehran, Iran (in Persian). [31] Li, Y. and Madanat, S. (2002). “A Steady-State Solution for the Optimal Pavement Resurfacing Problem”, Transportation Research, Part A, Vol. 36, No. 5, pp. 347–365. [32] Ouyang, Y. and Madanat, S. (2004). “Optimal Scheduling of Rehabilitation Activities for Multiple Pavement Facilities: Exact and Approximate Solutions”, Transportation Research, Part A, Vol. 38, No. 5, pp. 347–365. [33] May, A.D. (1990). “Traffic Flow Fundamentals”, Prentice Hall, New Jersey. [34] Kanji, G.K. (2006). “100 Statistical Tests”, 3rd edition, Sage publications, London. [35] Selezneva, O., Darter, M., Zollinger, D. and Shoukry, S. (2003). “Characterization of Transverse Cracking Spatial Variability Using LTPP Data for CRCP Design”, Proceedings of 82th Annual Meeting of TRB, Washington, D.C. | ||
|
آمار تعداد مشاهده مقاله: 1,369 تعداد دریافت فایل اصل مقاله: 1,318 |
||