Cumulative Distribution-Based Method for Pavement Performance Modeling
TU Chen-hao1, YE Wen-ya1, ZHANG Rui2, QIAN Xu-dong3, YANG Qun2
1. School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo, Zhejiang 315021, China; 2. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; 3. Ningbo Road and Bridge Engineering Construction Co., Ltd., Ningbo, Jiangsu 315100, China
Cumulative Distribution-Based Method for Pavement Performance Modeling
TU Chen-hao1, YE Wen-ya1, ZHANG Rui2, QIAN Xu-dong3, YANG Qun2
1. School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo, Zhejiang 315021, China; 2. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; 3. Ningbo Road and Bridge Engineering Construction Co., Ltd., Ningbo, Jiangsu 315100, China
摘要Pavement performance prediction is the basis for maintenance decisions. Predicting future pavement conditions accurately and efficiently helps determine the optimal maintenance time, select appropriate measures, and allocate rehabilitation funds effectively. However, limited to the instability and variability in pavement condition data collection, deterministic models are not always reliable for all pavement situations. On the other hand, probabilistic-based models are influenced by environmental factors that are challenging to quantify. Recognizing the limitations of the above two methods, this paper proposes a cumulative distribution-based technique for developing pavement performance prediction models. First, after comparing performance metrics such as pile-by-pile single-point, probability density, and cumulative distribution, it is evident that the cumulative distribution is the most reliable method for describing pavement conditions. A continuous distribution function is created from a limited set of discrete observed field pavement condition data using the sampling theorem. With cumulative distribution-based deterioration curves changing over time, it is possible to predict future pavement deterioration rates. A case study is presented at last. Analyses of the predicted curve and observed pavement performance indicate that the cumulative distribution-based technique is effective in modeling pavement performance and can provide reliable predictive results.
Abstract：Pavement performance prediction is the basis for maintenance decisions. Predicting future pavement conditions accurately and efficiently helps determine the optimal maintenance time, select appropriate measures, and allocate rehabilitation funds effectively. However, limited to the instability and variability in pavement condition data collection, deterministic models are not always reliable for all pavement situations. On the other hand, probabilistic-based models are influenced by environmental factors that are challenging to quantify. Recognizing the limitations of the above two methods, this paper proposes a cumulative distribution-based technique for developing pavement performance prediction models. First, after comparing performance metrics such as pile-by-pile single-point, probability density, and cumulative distribution, it is evident that the cumulative distribution is the most reliable method for describing pavement conditions. A continuous distribution function is created from a limited set of discrete observed field pavement condition data using the sampling theorem. With cumulative distribution-based deterioration curves changing over time, it is possible to predict future pavement deterioration rates. A case study is presented at last. Analyses of the predicted curve and observed pavement performance indicate that the cumulative distribution-based technique is effective in modeling pavement performance and can provide reliable predictive results.
基金资助:This work was supported by 2022 Ningbo Transportation Science and Technology Plan Project (Grant No.202216).
通讯作者:
TU Chen-hao, YANG Qun
引用本文:
TU Chen-hao, YE Wen-ya, ZHANG Rui, QIAN Xu-dong, YANG Qun. Cumulative Distribution-Based Method for Pavement Performance Modeling[J]. Journal of Highway and Transportation Research and Development, 2024, 18(2): 14-25.
TU Chen-hao, YE Wen-ya, ZHANG Rui, QIAN Xu-dong, YANG Qun. Cumulative Distribution-Based Method for Pavement Performance Modeling. Journal of Highway and Transportation Research and Development, 2024, 18(2): 14-25.
[1] HONG F, PROZZI J A. Estimation of Pavement Performance Deterioration Using Bayesian Approach[J]. Journal of Infrastructure Systems, 2006, 12(2):77-86. [2] DONEV V, HOFFMANN M. Condition Prediction and Estimation of Service Life in the Presence of Data Censoring and Dependent Competing Risks[J]. International Journal of Pavement Engineering, 2019, 20(3):313-331. [3] REMEK L, MIKOLAJ J, RUTTKAY L. Pavement Performance Prediction as a Key to Sustainable Road Infrastructure Construction[C]//15th International Multidisciplinary Scientific Geoconference (SGEM). Albena, Bulgaria:Curran Associates, Inc., 2015. [4] ABAZA K A, ASHUR, S A. Optimum Microscopic Pavement Management Model Using Constrained Integer Linear Programming[J]. International Journal of Pavement Engineering, 2009, 10(3):149-160. [5] OUYANG Y, MADANAT S. Optimal Scheduling of Rehabilitation Activities for Multiple Pavement Facilities:Exact and Approximate Solutions[J]. Transportation Research Part a-Policy and Practice, 2004, 38(5):347-365. [6] LYTTON R L. Concepts of Pavement Performance Prediction and Modelling[M]. 2nd ed. Toronto, Canada:North American Conference on Managing Pavements, 1987. [7] HAAS R, HUDSON W R, ZANIEWSKI J P. Modern Pavement Management. Malabar[M]. India:Krieger Publishing Company, 1994. [8] AASHTO. Pavement Management Guide[S]. 2nd ed. Washington, DC:AASHTO, 2012. [9] UDDIN. Pavement Management Systems[M]//FWA T F. The Handbook of Highway Engineering. Boca Raton:Taylor&Francis, 2005. [10] JUSTO-SILVA R, FERREIRA A, FLINTSCH G. Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models[J]. Sustainability, 2021, 13(9):5248.. [11] ABAZA K A. Back-calculation of Transition Probabilities for Markovian-based Pavement Performance Prediction Models[J]. International Journal of Pavement Engineering, 2016, 17(3):253-264. [12] ABAZA K A. Empirical Markovian-based Models For Rehabilitated Pavement Performance Used in a Life Cycle Analysis Approach[J]. Structure and Infrastructure Engineering, 2017, 13(5):625-636. [13] PÉREZ-ACEBO H, MINDRA N, RAILEAN A, et al. Rigid Pavement Performance Models by Means of Markov Chains with Half-year Step Time[J]. International Journal of Pavement Engineering, 2019, 20(7):830-843. [14] PÉREZ-ACEBO H, GONZALO-ORDEN H, FINDLEY D J, et al. Modeling the International Roughness Index Performance on Semi-rigid Pavements in Single Carriageway Roads[J]. Construction and Building Materials, 2021, 272:121665. [15] PÉREZ-ACEBO H, MONTES-REDONDO M, APPELT A, et al. A Simplified Skid Resistance Predicting Model for a Freeway Network to be Used in a Pavement Management System[J]. International Journal of Pavement Engineering, 2023, 24(2):2020266. [16] AASHTO Guide. Guide for Design of Pavement Structures[M]. Washington, DC:American Association of State Highway and Transportation Officials, 1993. [17] SUN Li-jun, LIU Xue-ping. General Deterioration Equation for Pavement Performance[J]. Journal of Tongji University, 1995, 23(5):512-518.(in Chinese) [18] WANG K C P, LI Q. Pavement Smoothness Prediction Based on Fuzzy and Gray Theories[J]. Computer-Aided Civil and Infrastructure Engineering, 2011, 26(1):69-76. [19] LI N, HAAS R, XIE W C. Development of a New Asphalt Pavement Performance Prediction Model[J]. Canadian Journal of Civil Engineering, 1997, 24(4):547-559. [20] OSORIO-LIRD A, CHAMORRO A, VIDELA C, et al. Application of Markov Chains and Monte Carlo Simulations for Developing Pavement Performance Models for Urban Network Management[J]. Structure and Infrastructure Engineering, 2018, 14(9):1169-1181. [21] ATTOH-OKINE N O. Grouping Pavement Condition Variables for Performance Modeling Using Self‐Organizing Maps[J]. Computer-Aided Civil and Infrastructure Engineering, 2001, 16(2):112-125. [22] MARCELINO P, de LURDES ANTUNES M, FORTUNATO E, et al. Machine Learning Approach for Pavement Performance Prediction[J]. International Journal of Pavement Engineering, 2021, 22(3):341-354. [23] ROSE S, MATHEW B S, ISAAC K P, et al. Risk Based Probabilistic Pavement Deterioration Prediction Models for Low Volume Roads[J]. International Journal of Pavement Engineering, 2018, 19(1):88-97. [24] HONG F, PROZZI J A. Roughness Model Accounting for Heterogeneity Based on In-service Pavement Performance Data[J]. Journal of Transportation Engineering, 2010, 136(3):205-213. [25] YAMANY M S, SAEED T U, VOLOVSKI M, et al. Characterizing the Performance of Interstate Flexible Pavements Using Artificial Neural Networks and Random Parameters Regression[J]. Journal of Infrastructure Systems, 2020, 26(2):04020010. [26] LI Jie, CHEN Jian-bing. The Probability Density Evolution Method for Analysis of Dynamic Nonlinear Response of Stochastic Structures[J]. Acta Mechanica Sinica, 2003, 35(6):722-728.(in Chinese) [27] CHEN J B, LI J. The Extreme Value Distribution and Dynamic Reliability Analysis of Nonlinear Structures with Uncertain Parameters[J]. Structural Safety, 2007, 29(2):77-93. [28] ABED A, THOM N, NEVES L. Probabilistic Prediction of Asphalt Pavement Performance[J]. Road Materials and Pavement Design, 2019, 20(sup1):S247-S264. [29] YAMANY M S, ABRAHAM D M. Hybrid Approach to Incorporate Preventive Maintenance Effectiveness into Probabilistic Pavement Performance Models[J]. Journal of Transportation Engineering, Part B:Pavements, 2021, 147(1):04020077. [30] JTG 5210-2018, Highway Performance Assessment Standards[S].(in Chinese) [31] ZHA Z, WEN B, YUAN X, et al. Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing:Nonlocal Sparse and Low-rank Modeling[J]. IEEE Signal Processing Magazine, 2023, 40(1):32-44.
2024, Vol. 18 
