Road Roughness Measurement using PSD

The article presents how road roughness is measured by using Power Spectral Density and a brief explanation of the ISO Classification of roads based on Road Roughness.

Road Roughness Measurement using PSD

The article presents how road roughness is measured by using Power Spectral Density and a brief explanation of the ISO Classification of roads based on Road Roughness.

What Is Road Roughness?

Road roughness is the measure of longitudinal road surface irregularities from the true reference plane. It plays a vital role in ride comfort and vehicle performance in terms of maintenance cost, speed and durability. Poor roads will increase fuel consumption and travel time as there will be more wear on the vehicle's spare parts. Fatigue stress will act on the vehicle subsystem.

Methods of Road Roughness Measurement:

  • Based on the road surface profile Eg: Dipstick Road Profiler
  • Based on suspension travel Eg: Fifth Wheel Bump Integrator

In most countries, the suspension travel method is used, since the profilometry method is a slow process. Suspension travel methods can be done in a faster phase by using devices like the fifth wheel bump integrator. Thus the data collected by any one of these methods is compared with the ISO standard road roughness chart and the road quality is identified. Normally for road inspection, the measurement is done yearly once to test the condition of the road.

International Roughness Index:

IRI is calculated using a quarter-car vehicle model travelling at 80km/hr, and its response is obtained as a slope of the graph between the distance travelled by the car and the suspension travel. The roughness index is the slope of the graph with units (in/mi, m/km, etc.).

Power Spectral Density Analysis:

The above-mentioned IRI method holds good for passenger cars when it is considered for heavy-load vehicles such as trucks data does not hold good. Thus Power spectral density comes into play, PSD is the approach where the road profiles are considered as waves(sin and cos) and the summation of these waves can be done by using Fourier transform. Fourier transform says that any wave can be represented by using a combination of sine and cosine waves, it helps to change the function from the time domain to the frequency domain. The theory of signal originated from electrical signals, now it has wide application in acoustics, biomedical engineering, image analysis and most recently road profile analysis. In the road roughness analysis, the data obtained by the inspection is plotted as a graph.

The graph is plotted by taking,

X-axis: wavenumber---inverse of wavelength (cycles/metre)

Y- axis : PSD---square of amplitude divided by frequency(metre^2/(cycles/metre))

The obtained plot is compared with standard ISO road roughness criteria and road quality is tested. The PSD profiles for each road are unique and all roads will have a drop in amplitude with respect to wavenumber, this is because the amplitudes for long wavelengths are higher than the amplitudes for short wavelengths. Amplitude indicates the roughness and the magnitude of distribution between the higher and lower wavenumber indicates the frequency of the bump. Thus the Power Spectral Density approach is the most accurate way to classify the road.

ISO Classification of Road-Based on Road Roughness:

International Organisation for Standardization gives the quality criteria as given below by plotting the graph of spatial frequency Vs Power Spectral Density. ISO classifies the roads into eight different classes, by indicating the bands of lines as shown in the figure. These bands of lines are obtained by classifying them based on the fixed PSD range. Then by overlaying the obtained PSD data with the ISO standard chart the road quality is determined.

Conclusion:

The above-plotted graph shows how the road classification is done by overlaying the PSD data with the ISO standard chart. Thus the methods of road inspection and the power spectral density approach to classifying the roads according to ISO are explained in detail in this article.

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