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Recognizing Patterns in Seasonal Variation of Pavement Roughness Using Minimum Message Length Inference
Author(s) -
Byrne M.,
Albrecht D.,
Sanjayan J.G.,
Kodikara J.
Publication year - 2009
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.1467-8667.2008.00575.x
Subject(s) - variation (astronomy) , seasonality , inference , surface finish , statistics , computer science , mathematics , artificial intelligence , materials science , physics , astrophysics , composite material
Pavement roughness is a common measure of pavement condition regularly measured by road authorities. An approach to recognize patterns of seasonal variation in rural sealed granular pavement roughness by minimum message length (MML) inference is demonstrated in this article. MML solves two fundamental questions: First, is the seasonal variation a systematic pattern or merely the result of random scatter? Second, given evidence of seasonal variation to what level of complexity should the seasonal trend be modeled? The MML technique developed does not require user input rather will identify in a quantitative and consistent manner any patterns evident in the data. The patterns identified with MML can be used to remove seasonal variation effects. The analysis utilized 104,188 roughness values obtained from a particular region in Australia over 15 years. MML inference recognized patterns of seasonal variation and demonstrated that these are not merely due to random scatter. The optimum model selected by MML inference has four separate segments of variation. These segments correspond to changes in climatic conditions that support the inference.