Wavelet Analysis and Interpretation of Road Roughness

Liu, Wei; Fwa, T F; Zhe, Zhao

doi:10.1061/(asce)0733-947x(2005)131:2(120)

Cited by 70 publications

(35 citation statements)

References 4 publications

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“…It was concluded that the HHT is a powerful tool for analysing frequency content of pavement profile data and feature extraction. The wavelet analysis of road roughness was carried out by Wei et al [2]; it was demonstrated that wavelet analysis can provide further roughness detail that is absent in the conventional indices such as the international roughness index (IRI). Such details could be used to determine the causes of deterioration of pavement roughness, provide useful information for maintenance operations, and aid in network pavement management.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of Hilbert–Huang transform, Fourier transform and wavelet transform in pavement profile analysis

Ayenu-Prah

Attoh-Okine

2009

Vehicle System Dynamics

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This study employs the Hilbert-Huang transform (HHT), the wavelet transform and the Fourier transform to analyse the road surface profiles of three pavement profiles. The wavelet and Fourier transforms have been the traditional spectral analysis methods, but they are predicated on a priori selection of basis functions that are either of infinite length or have fixed finite widths. The central idea of HHT is the empirical mode decomposition, which decomposes a signal into basis functions called the intrinsic mode functions (IMFs). The Hilbert transform can then be applied to the IMFs to generate an energytime-frequency spectrum called the Hilbert spectrum. The strength of HHT is the ability to process non-stationary and non-linear data. Unlike the Fourier transform, which transforms information from the time domain into the frequency domain, the HHT does not lose temporal information after transformation, i.e. energy-frequency information is maintained in the time domain. This paper attempts to reveal the frequency and energy content of the road profile data with the three methods mentioned as a means to establishing the most suitable way of characterising the pavement profiles in terms of ride quality. In performing the analyses, the nature and behaviour of the road profiles as indicated by the literature are taken into account, that road profiles are non-stationary and are inherently non-Gaussian. It is demonstrated that HHT offers more flexibility in terms of detailed profile analysis and description, which can be beneficial to pavement profile analysts in understanding the effects on vehicle vibrations and ride quality.

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Section: Introductionmentioning

confidence: 99%

Comparative study of Hilbert–Huang transform, Fourier transform and wavelet transform in pavement profile analysis

Ayenu-Prah

Attoh-Okine

2009

Vehicle System Dynamics

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“…Several other studies employed the db3 wavelet function to analyse concrete surface texture, pavement roughness, dynamic load excitation and aggregate segregation in asphalt concrete mixtures (e.g. Wei and Fwa 2004, Wei et al 2005, Abbas et al 2007, Papagiannakis et al 2007a, 2007b, Zelelew and Papagiannakis 2011. The db3 wavelet function is also orthogonal, maximally flat and compactly supported, which are desirable characteristics for the iterative decomposition used in discrete wavelet transforms (Daubechies 1992).…”

Section: Wavelet Analysismentioning

confidence: 99%

“…It has been used in other pavement engineering applications, such as analysing pavement roughness affecting ride quality and interaction between pavement roughness and heavy vehicle (e.g. Wei and Fwa 2004, Wei et al 2005, Papagiannakis et al 2007a, 2007b. Recently, it was applied in quantifying the directional aggregate segregation in laboratory compacted asphalt mixtures (Zelelew and Papagiannakis 2011).…”

Section: Introductionmentioning

confidence: 99%

Pavement macro-texture analysis using wavelets

Zelelew

Papagiannakis

Izeppi

2013

International Journal of Pavement Engineering

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This paper utilises a wavelet approach to interpret the macro-texture data collected on asphalt and concrete pavement surfaces with a wide range of macro-texture properties. The experimental data were obtained using a circular track meter (CTMeter) device on pavements built at the Virginia's Smart Road test facility. The size of the data-set allowed nine levels of wavelet decomposition with wavelengths ranging from 1.7 to 435 mm. The extent of macro-texture variation was summarised using the normalised wavelet energy metric defined as the sum of the squares of the detailed wavelet coefficients for the sub-bands that correspond to the macro-texture range of wavelengths divided by the length of the test section expressed in mm 2 /m. This metric was found highly correlated with the empirical mean profile depth measurements. Hence, the wavelet approach can be used to objectively analyse CTMeter measurements of pavement texture.

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“…Furthermore, it is orthogonal, maximally flat and compactly supported, which are desirable characteristics for the iterative decomposition used in discrete wavelet transforms (Daubechies 1988). It should be noted that the work by Wei et al (2005) on pavement roughens utilized a db3 wavelet as well.…”

Section: Wavelet Analysismentioning

confidence: 99%

A wavelet interpretation of vehicle-pavement interaction

Papagiannakis

Zelelew

Muhunthan

2007

International Journal of Pavement Engineering

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This paper utilizes a wavelet approach to interpret the interaction between truck dynamic axle loads and pavement roughness profile. The experimental data used was obtained from an instrumented 5-axle semi-trailer truck equipped with an air and a rubber suspension in the drive and trailer axles, respectively. Wavelet decomposes the original signal into a number of sub-band levels depending on its characteristics. The size of the dataset used allowed 11 levels of wavelet decomposition with test speed dependent frequencies ranging up to 4 cy/m. For dynamic load, the extent of variation in each of these wavelength sub-bands, was summarized through an energy metric effectively computed as the sum of the squares of the wavelet coefficients in each sub-band. Total energy was computed as the sum of the energy of all sub-bands and was normalized by dividing by the length of the test section. Relative energy was computed as the percent of total energy in each sub-band. The results were summarized through 3D plots of relative energy versus load frequency versus profile frequency. The profile frequencies mostly affecting dynamic loads depend on speed and range from 0.65 to 3.76 cy/m.

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Wavelet Analysis and Interpretation of Road Roughness

Cited by 70 publications

References 4 publications

Comparative study of Hilbert–Huang transform, Fourier transform and wavelet transform in pavement profile analysis

Comparative study of Hilbert–Huang transform, Fourier transform and wavelet transform in pavement profile analysis

Pavement macro-texture analysis using wavelets

A wavelet interpretation of vehicle-pavement interaction

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