A Consistency Evaluation Method of Pavement Performance Based on K-Means Clustering and Cumulative Distribution
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Shrestha, 2022, Implementing Traffic Speed Deflection Measurements for Network Level Pavement Management in Virginia, J. Transp. Eng. Part B, 148, 04022021, 10.1061/JPEODX.0000371
Shtayat, 2022, An Overview of Pavement Degradation Prediction Models, J. Adv. Transp., 2022, 1, 10.1155/2022/7783588
Sidess, 2020, A model for predicting the deterioration of the pavement condition index, Int. J. Pavement Eng., 22, 1625, 10.1080/10298436.2020.1714044
Lin, 2022, Pavement anomaly detection based on transformer and self-supervised learning, Autom. Constr., 143, 104544, 10.1016/j.autcon.2022.104544
Jiang, 2022, Evaluation of inverted pavement by structural condition indicators from falling weight deflectometer, Constr. Build. Mater., 319, 125991, 10.1016/j.conbuildmat.2021.125991
Gong, 2019, Estimating Asphalt Concrete Modulus of Existing Flexible Pavements for Mechanistic-Empirical Rehabilitation Analyses, J. Mater. Civ. Eng., 31, 04019252, 10.1061/(ASCE)MT.1943-5533.0002892
Chen, 2016, Sigmoidal Models for Predicting Pavement Performance Conditions, J. Perform. Constr. Facil., 30, 04015078, 10.1061/(ASCE)CF.1943-5509.0000833
Haider, 2010, Effect of Frequency of Pavement Condition Data Collection on Performance Prediction, Transp. Res. Rec. J. Transp. Res. Board, 2153, 67, 10.3141/2153-08
Abaza, 2009, Optimum microscopic pavement management model using constrained integer linear programming, Int. J. Pavement Eng., 10, 149, 10.1080/10298430802068907
Ouyang, 2004, Optimal scheduling of rehabilitation activities for multiple pavement facilities: Exact and approximate solutions, Transp. Res. Part A, 38, 347
Liu, 2021, Large-scale pavement roughness measurements with vehicle crowdsourced data using semi-supervised learning, Transp. Res. Part C: Emerg. Technol., 125, 103048, 10.1016/j.trc.2021.103048
Du, 2010, Asphalt Pavement Performance Prediction Model Based on Gray System Theory, J. Tongji Univ. Nat. Sci., 38, 1161
Guo, 2021, A weighted multi-output neural network model for the prediction of rigid pavement deterioration, Int. J. Pavement Eng., 23, 2631, 10.1080/10298436.2020.1867854
Kim, 2006, Development of performance prediction models in flexible pavement using regression analysis method, KSCE J. Civ. Eng., 10, 91, 10.1007/BF02823926
Wang, 2018, An Unsupervised Cluster Method for Pavement Grouping Based on Multidimensional Performance Data, J. Transp. Eng. Part B, 144, 04018005, 10.1061/JPEODX.0000030
Park, 2008, Development of Prediction Method for Highway Pavement Condition, Int. J. Highw. Eng., 10, 199
Lijuan, 2010, Gray and Fuzzy Clustering Method-Based on Network Level Pavement Performance Assessment, J. Tongji Univ. Nat. Sci., 38, 252
Kaya, 2020, Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems, Transp. Res. Rec. J. Transp. Res. Board, 2674, 448, 10.1177/0361198120915889
Abed, 2019, Probabilistic prediction of asphalt pavement performance, Road Mater. Pavement Des., 20, S247, 10.1080/14680629.2019.1593229
Kalita, 2014, Variability characterisation of input parameters in pavement performance evaluation, Road Mater. Pavement Des., 16, 172, 10.1080/14680629.2014.988171
Wojtkiewicz, 2010, Probabilistic Numerical Simulation of Pavement Performance using MEPDG, Road Mater. Pavement Des., 11, 291, 10.1080/14680629.2010.9690277
Dilip, 2014, Influence of Spatial Variability on Pavement Responses Using Latin Hypercube Sampling on Two-Dimensional Random Fields, J. Mater. Civ. Eng., 26, 04014083, 10.1061/(ASCE)MT.1943-5533.0000994
Evdorides, 2013, A methodology to model the variability in pavement performance, Proc. Inst. Civ. Eng. —Transp., 166, 233
Lepech, 2020, Incorporating pavement deterioration uncertainty into pavement management optimization, Int. J. Pavement Eng., 23, 2062
Jia, 2018, Influence of Measurement Variability of International Roughness Index on Uncertainty of Network-Level Pavement Evaluation, J. Transp. Eng. Part B, 144, 04018007, 10.1061/JPEODX.0000034
Rose, 2016, Risk based probabilistic pavement deterioration prediction models for low volume roads, Int. J. Pavement Eng., 19, 88, 10.1080/10298436.2016.1162308
Indebetouw, 1988, Interpolation theorem for quasi-periodic sampling, JOSA A, 5, 1030, 10.1364/JOSAA.5.001030
Wang, 2000, A sampling theorem associated with quasi-Fourier transform, IEEE Trans. Signal Process., 48, 895, 10.1109/78.824688
Zhang, 2005, Sampling Theorems for Bandpass Signals with Fractional Fourier Transform, Acta Electron. Sin., 33, 1196
Agrawal, 2001, On computing the distribution function of the sum of independent random variables, Comput. Oper. Res., 28, 473, 10.1016/S0305-0548(99)00133-1
Dokmanic, 2009, Convolution on the $n$-Sphere With Application to PDF Modeling, IEEE Trans. Signal Process., 58, 1157, 10.1109/TSP.2009.2033329
Finkelshtein, 2018, Kesten’s bound for subexponential densities on the real line and its multi-dimensional analogues, Adv. Appl. Probab., 50, 373, 10.1017/apr.2018.18
Katsikadelis, 2019, Numerical solution of integrodifferential equations with convolution integrals, Ingenieur-Archiv., 89, 2019
Maslakov, 2021, New Approach to the Iterative Method for Numerical Solution of a Convolution Type Equation Determined for a Certain Class of Problems, Comput. Math. Math. Phys., 61, 1260, 10.1134/S0965542521080054
Zhang, 2019, Runge–Kutta convolution quadrature methods with convergence and stability analysis for nonlinear singular fractional integro-differential equations, Commun. Nonlinear Sci. Numer. Simul., 84, 105132, 10.1016/j.cnsns.2019.105132
McDaniel, L.S., Glen, A.G., and Leemis, L.M. (2016). Linear Approximations of Probability Density Functions. Computational Probability Applications, Springer.