Road roughness evaluation using in-pavement strain sensors

Abstract

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roughness only for some secondary roads but much less frequently, such as once every five years, resulting in outdated roughness information.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.

Online Classification of Road Roughness Conditions with Vehicle Unsprung Mass Acceleration by Sliding Time Window

“…In this paper above, some studies on how to calculate the ADV values and how to choose the sampling frequency and time-window are conducted. But according to Equation (11), it can be found that the mean square values of unsprung mass acceleration of vehicle suspension system are not only related to road roughness, sampling frequency, and timewindow in reality, but also has a direct relationship with the amplitude-frequency characteristics of vehicle suspension itself. µ -3σ at G q (n 0 ) = 22.6 × 10 -6 µ -3σ at G q (n 0 ) = 25.4 × 10 -6 µ -3σ at G q (n 0 ) = 32.0 × 10 -6 µ -3σ at G q (n 0 ) = 64.0 × 10 -6 µ 0 + 3σ 0 = µ -3σ * * * * * With wide use of semiactive suspension, in a vehicle suspension system, only the sprung mass, spring stiffness, and damping coefficient can be changed considerably.…”

“…Existing methods for estimating road roughness can be divided into three categories [6]: direct measurements [7][8][9][10][11], noncontact measurements [12][13][14][15], and system response-based estimation [16][17][18][19][20][21].…”

“…Recently, Japanese researchers Nueraihemaitijiang Abulizi et al had proposed a road roughness estimator based on compact road profiler and ArcGIS [7], which services for the road management instead of systems on the vehicle; Russians researchers Lushnikov and Lushnikov used a profilometer that can measure the longitudinal microprofile of the road surface to assess the road surface roughness coefficients accurately [8]; Balaram and Mostert presented the results of road roughness measurements on a gravel road in KwaZulu-Natal collected with a Roughometer III [9], while Kumar et al chose to use mobile laser scanning data to estimate the road roughness [10]. All the methods above need special sensors attached on the vehicles, but Zhang et al developed a real-time roughness evaluation approach based on the durable in-pavement strain sensors that build in pavement panels [11]. Among these direct and noncontact measurements, they require either specific vehicles or sensors, which cause poor transplant ability and restrict their practical applications.…”

A road quality classification technique based on vehicle system responses with experimental validation

“…The large dynamic axle loading from roughness accelerates pavement deterioration, so pavement engineers have to maintenance the road in relatively good level all the time [1]. However, analysis and measurement of road roughness is a difficult problem that pavement engineers have been facing for many years, especially the road which heavy vehicles such as truck often make lots of stop-and-go trips [1]. Detection and maintenance of the condition of a road profile is important for many reasons, such as safety and economic savings.…”

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

“…Currently applied road estimation methods can be roughly divided into three categories [5], i.e. direct measurement [5,6], non-contact measurement [7,8] and system response-based estimation [9,10]. Among these, due to lower costs and reduced measurement contamination (e.g.…”