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原文传递 Classification of Features of Pavement Profiles Using Empirical Mode Decomposition

题名：	Classification of Features of Pavement Profiles Using Empirical Mode Decomposition
作者：	Walker, D.; Franta, D. P.
关键词：	Pavements##Granular materials##Virginia##Highway design##Texture surface##Soils##Aggregates##Saturation##Moisture content##Density##Resilient modulus tests##Compression tests##Implementation##Pavement slabs##Pavement surface analysis##Flexible pavement##
摘要：	The LTPP database contains surface profile data for pavement sections throughout the United States that is used to compute IRI. Byrum previously used Wisconsin LTPP section profiles to analyze curvature in concrete pavement slabs.(3) However, the difficulty with analyzing raw field profiles is the level of noise and frequency of inconsistencies within these data sets. To remedy this problem, an automated pavement analysis method was developed to smooth the real-field profiles and allow for more accurate and consistent analysis of pavement sections or slabs. This method is based on the EMD process contained within the HHT. Past analysis of road surface profiles using the HHT is limited. Adu-Gyamfi et al. used the empirical mode decomposition for pavement surface analysis, and Attoh-Okine et al. also used it to analyze two flexible pavement profiles.(4,5) The application in this paper focuses on grouping intrinsic mode functions (IMF) to analyze built-in curl features of rigid pavement profiles. Extraction of noise in real-life and artificial profile data can be performed by applying a sifting process to filter and identify the IMFs that are contained in raw surface profiles. The idea behind the Hilbert-Huang-based sifting process is to identify the intrinsic functions contained within the data and to subsequently remove and categorize them in order to analyze specific portions of the original profile. Figure 1 shows the basic decomposition of any profile.(6) Where y(x) is the original profile, cj(x) represents IMFs within the data set, and rn(x) is the residue after the first n IMFs have been removed. In the case of pavement profiles, some of the IMFs are due to “noise/surface texture,” “curling,” and/or “base trends” within the pavement. Removing the IMFs due to “noise/texture surface ” from the distorted, non-linear profiles, will reveal the smoother functions of the original data set that can be attributed to “curl” and/or “base trends.” Quotations are used around “noise/surface texture,” “curl,” and “base trends” to emphasize that these terms are used only to group functions of similar characteristics contained in surface profiles. Surface profiles contain IMFs attributed to many different variables.
总页数：	16
报告类型：	科技报告