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Pavement Smoothness Prediction Based on Fuzzy and Gray Theories
Author(s) -
Wang Kelvin C.P.,
Li Qiang
Publication year - 2011
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2009.00639.x
Subject(s) - international roughness index , gray (unit) , smoothness , fuzzy logic , regression analysis , predictive modelling , computer science , regression , engineering , data mining , mathematics , statistics , artificial intelligence , machine learning , surface finish , mechanical engineering , medicine , mathematical analysis , radiology
Pavement smoothness has been recognized as one of the measures of pavement performance. In the Mechanistic‐Empirical Pavement Design Guide (MEPDG), pavement smoothness indicated by the International Roughness Index (IRI) was predicted based on various distresses using traditional regression analysis approaches. Recognizing the limitations of linear regression method, a Gray Theory‐based technique was previously proposed by the authors for the development of pavement smoothness prediction models. In this article, instead of using the conventional least squares method to determine the coefficients for gray prediction models, fuzzy regression method is proposed to solve this gray problem. With pavement IRI and distresses data exported from the Long Term Pavement Performance (LTPP) database, Fuzzy and Gray Model (FGM)‐based smoothness predictions are established using influencing factors similar to those in MEPDG. Based on the comparisons among results originated from MEPDG models, conventional GM models, FGM models, and actual LTPP data, it is shown that the Gray Theory‐based prediction methods with fuzzy regression for estimating model coefficients provide promising results and are useful for modeling pavement performance .