American Society of Civil Engineers


Critical Analysis of Different Hilbert-Huang Algorithms for Pavement Profile Evaluation


by Y. O. Adu-Gyamfi, (Graduate student, Dept. of Civil and Environmental Engineering, Univ. of Delaware, Newark, DE 19716.), N. O. Attoh-Okine, P.E., (corresponding author), M.ASCE, (Professor, Dept. of Civil and Environmental Engineering, Univ. of Delaware, Newark, DE 19716), and A. Y. Ayenu-Prah, Ph.D., (Former Graduate Student, Dept. of Civil and Environmental Engineering, Univ. of Delaware, Newark, DE 19716.)

Journal of Computing in Civil Engineering, Vol. 24, No. 6, November/December 2010, pp. 514-524, (doi:  http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000056)

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Document type: Journal Paper
Abstract: Pavement profile analysis is a major component in pavement infrastructure management decision making for maintenance and rehabilitation. This paper takes an in-depth look at pavement profile characterization and evaluation, taking into account the inherent nature of road profile data, i.e., nonstationary and non-Gaussian. Although there have been several studies aimed at the analysis and characterization of pavement profile, the bulk have been limited to applying relatively conventional signal processing techniques, such as the Fourier analysis. Using this approach, only the average condition of the local conditions can be represented. Most transient and changing signals will not be handled well due to the averaging effect of the technique. The Hilbert-Huang transform operates at the scale of every oscillation, an extremely important property for obtaining localized profile information. In this paper, the different algorithms of the Hilbert-Huang transform: empirical mode decomposition (EMD), ensemble EMD, and complex EMD (CEMD) have been discussed and implemented to extract useful information from road profile data. The robustness of the algorithms is compared based on its ability to produce physically meaningful intrinsic mode functions (IMFs) which truly characterize the underlying process. The results show that although all the methodologies yielded similar residual trends, the CEMD produced physically meaningful and trusted IMFs whose information at the various levels of decomposition could be used to extract profile information such as the extent of deterioration and localized roughness information.


ASCE Subject Headings:
Pavements
Decomposition
Algorithms
Decision making
Maintenance
Rehabilitation

Author Keywords:
Hilbert-Huang transform
Pavement profiles
Ensemble empirical mode decomposition
Empirical mode decomposition
Complex empirical mode decomposition
Intrinsic mode functions