Simulation of Road Roughness Based on Using IFFT Method

Jiang, Chunlan; Cheng, Lin; Sun, Fuchun; Hong-jie, Chang

doi:10.1109/wcse.2012.46

Cited by 7 publications

(3 citation statements)

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“…The random nature of the road geometry has been studied for many years, with a wide majority of studies being conducted on the road surface roughness, aiming at modelling the road surfaces stochastically, to be used in vehicle ride comfort as well as Noise-Vibration-Harshness studies [19][20][21][22][23][24][25][26][27], where some of the earliest works in this regard could be attributed to [28][29][30].…”

Section: Literature Survey-road Applicationmentioning

confidence: 99%

A Mathematical Approach Towards Random Road Profile Generation Based on Chaotic Signals of Chua's Circuit

Milani

Momtaz

Amini

et al. 2022

Contemporary Mathematics

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In response to application demands in vehicle dynamics and control, traffic engineering, urban planning, and logistics, the generation of an adequate artificial road profile in terms of the diversity of geometric scenarios has been addressed in the current manuscript. The underlying mathematical principles for generating a geometrically comprehensive, yet logically meaningful, 3D road profile have been taken from high and unbiased sweeping factors of random number sequences over their domain of interest. And to generate such random number sequences, the mathematically manipulated output signal of a well-established chaotic system has been utilized, namely that of the Chua's circuit. Having defined the target road profile mathematically with all its geometrical complexities, a suitable scheme derived from the mentioned chaotic signal has been used to generate the required random number sequences as defining parameters of the road profile. The scheme has been otherwise tested and proven to show the demanded level of randomness in literature. Several attempts have been made to create a diverse range of road profiles, considering the constraints imposed by vehicle dynamics. To generate the road geometries, the limitations imposed by the vehicle's motion, such as the limitations on corresponding curvatures, slopes, and banking angles are negotiated, in terms of vehicle dynamics and available tire-road friction forces, by evaluating how close a vehicle will be to its tire force capacity limits as it travels on sections of the generated road.

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Section: Literature Survey-road Applicationmentioning

confidence: 99%

A Mathematical Approach Towards Random Road Profile Generation Based on Chaotic Signals of Chua's Circuit

Milani

Momtaz

Amini

et al. 2022

Contemporary Mathematics

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“…The inverse fast Fourier transform (IFFT) of the power spectrum density (PSD) of a road profile ( i; ) provides a suitable model to represent profiles of different roughness levels (Wu 2000, Jiang, Cheng et al 2012. The ISO (1995) (Andren 2006) represents the elevation profile as a PSD Ss (K) in the form ( 5)…”

Section: Roughness Generationmentioning

confidence: 99%

Road profile reconstruction using connected vehicle responses and wavelet analysis

Zhang

Sun

Bridgelall

et al. 2018

Journal of Terramechanics

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Practitioners analyze the elevation profile of a roadway to detect localized defects and to produce the international roughness index. The prevailing method of measuring road profiles uses a specially instrumented vehicle and trained technicians, which usually leads to a high cost and an insufficient measurement frequency. The recent availability of probe data from connected vehicles provides a method that is cost-effective, continuous, and covers the entire roadway network. However, no method currently exists that can reproduce the elevation profile from multi-resolution features of the vehicle inertial response signal. This research uses the wavelet decomposition of the vehicle inertial 1 responses and a nonlinear autoregressive artificial neural network with exogenous inputs to reconstruct the elevation profile. The vehicle inertial responses are a function of both the vehicle suspension characteristics and its speed. Therefore, the authors normalized the vehicle response models by the traveling speed and then numerically solved their inertial response equations to simulate the vehicle dynamic responses. The results demonstrate that applying the artificial neural network to the wavelet decomposed inertial response signals provides an effective estimation of the road profile.

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“…To simulate road profiles ( ) of different roughness levels for sensitivity analysis, the inverse fast Fourier transform (IFFT) of the PSD is a suitable approach to apply [32,33]. For this analysis, the secondorder forward difference provides a higher-accuracy numerical approximation of the second derivative of the profile (    ) [34].…”

Section: Sensitivity Studymentioning

confidence: 99%

Road roughness evaluation using in-pavement strain sensors

Zhang¹,

Deng²,

Huang³

et al. 2015

Smart Mater. Struct.

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The international roughness index (IRI) is a characterization of road roughness or ride quality that transportation agencies most often report. The prevalent method of acquiring IRI data requires instrumented vehicles and technicians with specialized training to interpret the results. The extensive labor and high cost requirements associated with the existing approaches limit data collection to at most once per year for portions of the national highway system. Agencies characterize roughness only for some secondary roads but much less frequently, such as once every five years, resulting in outdated roughness information. This research developed a real-time roughness evaluation approach that links the output of durable in-pavement strain sensors to prevailing indices that summarize road roughness. Field experiments validated the high consistency of the approach by showing that it is within 3.3% of relative IRI estimates. After their installation and calibration during road construction, the ruggedized strain sensors will report road roughness continuously. Thus, the solution will provide agencies a real-time roughness monitoring solution over the remaining service life of road assets.

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Simulation of Road Roughness Based on Using IFFT Method

Cited by 7 publications

References 0 publications

A Mathematical Approach Towards Random Road Profile Generation Based on Chaotic Signals of Chua's Circuit

A Mathematical Approach Towards Random Road Profile Generation Based on Chaotic Signals of Chua's Circuit

Road profile reconstruction using connected vehicle responses and wavelet analysis

Road roughness evaluation using in-pavement strain sensors

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