Indicators of longitudinal unevenness of roads in the USA

Kropáč, Oldřich; Múčka, Peter

doi:10.1504/ijvd.2008.020306

Cited by 43 publications

(19 citation statements)

References 6 publications

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“…The results confirmed the statements made in this paper (Kropá and Mú ka, 2008). The results confirmed the statements made in this paper (Kropá and Mú ka, 2008).…”

Section: Processing Of the Whole Ltpp Databasesupporting

confidence: 92%

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Properties of random component of longitudinal road profile influenced by local obstacles

Múčka

Kropáč²

2009

IJVSMT

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This paper describes the statistical properties of the pure random component of longitudinal road profile. The individual obstacles were removed from the road profile by median filtering approach. A total of 25,830 test sections records from the long-term pavement performance (LTPP) programme were analysed. The differences between asphalt concrete (AC) and cement concrete (CC) wearing courses were outlined. The comparison was shown on statistics (mean, median, standard deviation values) and probability density functions (PDFs) of contemporary used road indices (international roughness index (IRI), road elevation variance, power spectrum density (PSD) parameters in one-, two-and three-wavelength bands, etc.). The simulation study revealed the marked influence of obstacles on the road indices. The spectral properties of the pure random part were quantified.

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“…The results confirmed the statements made in this paper (Kropá and Mú ka, 2008). The results confirmed the statements made in this paper (Kropá and Mú ka, 2008).…”

Section: Processing Of the Whole Ltpp Databasesupporting

confidence: 92%

“…This fact was revealed and mentioned in some previous works (Andrén, 2006;Braun et al, 1991;Kropá and Mú ka, 2008). This fact was revealed and mentioned in some previous works (Andrén, 2006;Braun et al, 1991;Kropá and Mú ka, 2008).…”

Section: C3supporting

confidence: 58%

Properties of random component of longitudinal road profile influenced by local obstacles

Múčka

Kropáč²

2009

IJVSMT

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“…Andren, for instance, analysed the road elevation profiles of the entire Swedish road network and found that the spectral exponent, w , may vary within the range of 1.5–3.0. Other authors have made similar claims of the ineffectiveness of the ISO model, including Kropac and Mucka, who analysed over 25 800 longitudinal elevation profiles from the USA and Canada. Despite this, it has been repeatedly shown that, for long stretches of road, the spectral model adopted by the ISO under standard 8608 is often adequate (for example, in ).…”

Section: Transport Vibration Test Schedules Based On Road Power Spectmentioning

confidence: 81%

Issues with Combining Road Elevation Spectral Models and Vehicle Vibration Response to Estimate Vehicle Dynamic Characteristics

Ainalis

Rouillard

Sek

2014

Packag. Technol. Sci.

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The research presented in this paper focuses on issues associated with the development of an experimental technique to estimate the dynamic characteristics of wheeled vehicles (namely, the frequency response function) using only in‐service response data. To validate the approach and eliminate complexities associated with multi‐wheel vehicles, a single‐wheeled prototype vehicle was designed and commissioned. The vertical vibration acceleration of the prototype vehicle's sprung mass was measured during normal operation. The power spectral density function was computed and used to estimate the frequency response function of the vehicle. A number of experiments using various configurations of the single‐wheeled prototype vehicle were undertaken, along with a series of vibration table experiments to provide a comparison with the estimated frequency response functions. The results show that the best estimate of the frequency response function using the vehicle response data provides reasonable agreement with the measured laboratory experiments when the value of the slope of the spectral function is not set to the value suggested by the International Organisation for Standardisation. Another technique was further developed to estimate the value of the pavement spectral slope using only in‐service response data; however, this technique does not yield consistent and accurate estimates. Interestingly, the main resonance of the vehicle is in agreement between the vibration table and response data around the sprung mass of all three vehicle configurations when inspected using linear scales (regardless of the variation in the spectral shape of the excitation), although the additional modes (including the unsprung mass) differs for all vehicles. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Hence, often more complex models are studied, see, for instance, Refs. [3, 6, 20–22]. The analysis methods in Refs.…”

Section: Fatigue Accumulation In a Vehiclementioning

confidence: 99%

“…The analysis methods in Refs. [6 and 22] are similar in the sense that roads are analysed in the frequency domain, and that the statistical properties vary along the roads. With such methods, computer‐simulated roads with variable properties can be obtained.…”

Section: Fatigue Accumulation In a Vehiclementioning

confidence: 99%

Vehicle fatigue damage caused by road irregularities

Bogsjö

Rychlik

2009

Fatigue Fract Eng Mat Struct

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A B S T R A C T Road roughness causes fatigue-inducing loads in travelling vehicles. Road sections with a high degree of roughness are of special interest because these have a significant impact on vehicle's fatigue life. This study is focused on the statistical description and analysis of vehicle damage caused by irregularities. Standard statistical analysis tools are not straightforwardly applicable because of the non-stationary property of the irregularities. However, it is found that the road irregularities' influence on vehicles can be accurately described using a 'local' narrow-band approximation of the fatigue damage intensity.

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Indicators of longitudinal unevenness of roads in the USA

Cited by 43 publications

References 6 publications

Properties of random component of longitudinal road profile influenced by local obstacles

Properties of random component of longitudinal road profile influenced by local obstacles

Issues with Combining Road Elevation Spectral Models and Vehicle Vibration Response to Estimate Vehicle Dynamic Characteristics

Vehicle fatigue damage caused by road irregularities

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