Content uploaded by Igor Rychlik
Author content
All content in this area was uploaded by Igor Rychlik on Nov 09, 2014
Content may be subject to copyright.
A preview of the PDF is not available
Modelling of road profiles using roughness indicators
Abstract and Figures
The vertical road input is the most important load for durability assessments of vehicles. We focus on stochastic modelling of the road profile with the aim to find a simple but still useful model. The proposed non-stationary Laplace model with ISO spectrum has only two parameters, and can be efficiently estimated from a sequence of roughness indicators, such as IRI or ISO roughness coefficient. Thus, a road profile can be stochastically reconstructed from roughness indicators. Further, explicit approximations for the fatigue damage due to Laplace roads are developed. The usefulness of the proposed Laplace-ISO model is validated for eight measured road profiles. Reference to this paper should be made as follows: Johannesson, P. and Rychlik, I. (2014) 'Modelling of road profiles using roughness indicators', Int.
Figures - uploaded by Igor Rychlik
Author content
All figure content in this area was uploaded by Igor Rychlik
Content may be subject to copyright.
... For each combination of roughness class and vehicle speed, a straight-line simulation of 200 metres was run to calculate forces with the simulation model as road segments up to this length can be regarded with a constant variance regarding the vertical profile according to Johannesson and Rychlik (2014). The forces were then used to determine the pseudo-damage for equal time windows of one second. ...
... Other methods to create synthetic road profiles based on a target spectrum are reviewed in (Haigermoser et al., 2015). More complex models that require additional parameters or measurements from real roads can be found in (Bogsjö, 2007;Bogsjö et al., 2012;Johannesson and Rychlik, 2014;Figure 3: PSD of synthetic road with roughness class C. Múčka , 2012). The more complex models include two-and three-line fits for the PSD or statistical models where the non-stationary and non-homogenous behaviour of real roads is considered. ...
This work deals with the virtualisation of motorcycle customer
behaviour. First, a method was found to identify design-relevant customers based
on collected onboard signals. For vertical excitations, the roughness of the road
and ridden velocities were examined with multi-body simulations (MBS). A
measurement of a real motorcycle on a test track was used for validation. Using a similar dataset,
an approach is presented with which virtual tracks are created. A measured speed
profile and scatter for the vertical excitation were considered to obtain precise
loads via MBS. Besides the virtualisation of the road, a novel algorithm was
developed to generate speed profiles based on rider characteristics. Different
synthetic rider types from beginner to expert were defined and compared. The
developed methods enable the identification of design-relevant customers and the
simulation of load profiles resulting from real customer usage.
... and α scales the amount of regularisation we wish to impose. This should ideally mimic the weighting function achieved by ADA in Equation (20). FIR-DT further modifies the triangular weighting matrix through the use of a first difference matrix A, given as ...
... The former represents the broad changes in the landscape which have negligible effects on vehicle dynamics. In contrast, the upper limit on the frequency represents small variations which are filtered out by the tyre [20]. When generating the profiles only the amplitude information is given by the ISO 8608 standard; therefore, in order to generate time signals, a uniformly random signal is generated for the phase signal with spatial frequencies sampled at discrete intervals. ...
Response reconstruction is used to obtain accurate replication of vehicle structural responses of field recorded measurements in a laboratory environment, a crucial step in the process of Accelerated Destructive Testing (ADA). Response Reconstruction is cast as an inverse problem whereby an input signal is inferred to generate the desired outputs of a system. By casting the problem as an inverse problem we veer away from the familiarity of symmetry in physical systems since multiple inputs may generate the same output. We differ in our approach from standard force reconstruction problems in that the optimisation goal is the recreated output of the system. This alleviates the need for highly accurate inputs. We focus on offline non-causal linear regression methods to obtain input signals. A new windowing method called AntiDiagonal Averaging (ADA) is proposed to improve the regression techniques’ performance. ADA introduces overlaps within the predicted time signal windows and averages them. The newly proposed method is tested on a numerical quarter car model and shown to accurately reproduce the system’s outputs, which outperform related Finite Impulse Response (FIR) methods. In the nonlinear configuration of the numerical quarter car, ADA achieved a recreated output Mean Fit Function Error (MFFE) score of 0.40% compared to the next best performing FIR method, which generated a score of 4.89%. Similar performance was shown for the linear case.
... In the field of vehicle design, surface profile classification according to [9] has been adopted to generate artificial road profiles that simulate different conditions to optimize the mechanical components or to verify the suspension parameters of vehicles [40][41][42]. Other scholars have realized artificial road profiles with given IRI values [43][44][45]. ...
Stone pavements are the historical, architectural, and cultural heritage of lots of cities in Italy and the world. Road managers should be able to make decisions on the global conditions to define the most suitable strategies and maintenance interventions for every type of pavement. There are no standard monitoring methods or criteria for evaluating stone pavement performance. These pavements have more uneven surfaces than traditional pavements, but this characteristic could be accepted if type of vehicles and relative travel conditions are considered. Therefore, it is useful to define criteria for assessing roughness considering the comfort experienced by users in different vehicles. In this research, both traditional and innovative methodologies for assessing irregularities have been investigated using true stone surface profiles. In this regard, traditional performance indicators such as the International Roughness Index (IRI) defined by the ASTM E1926, the ISO 8608 classification, and the frequency-weighted vertical acceleration (awz) provided by ISO 2631-1 for comfort assessment have been considered. In the case of comfort assessment, three dynamic vehicle models (bike, automobile, and bus) have been adopted. Finally, this two-part paper also proposes an innovative straightedge analysis for stone pavements (SASP) to evaluate the effect on traffic of both pavement profile roughness and localized irregularities. In this way, the authors aim to provide an effective tool to monitor stone pavements.
... This estimation method has been elaborated by Johannesson and Rychlik (2014). A statistical analysis of 71-km road data showed that the normal range of the local roughness variance is between 0.24 and 1.18 (Johannesson et al. 2016(Johannesson et al. , 2017. ...
A passenger vehicle's fuel-powered engine compensates for its dissipated energy, which is caused by road roughness. This paper presents an integrated vehicle-pavement approach and investigates the excess fuel consumption of a seven-degree-of-freedom (DOF) full-car model on rough pavements. In the approach, a nonstationary Laplace process is used to artificially generate road roughness profiles with consideration of local roughness variance and parallel roughness correlation. The proposed mechanistic approach quantifies the impact of road roughness and vehicle dynamic characteristics on excess fuel consumption. The study concluded that overlooking local roughness variance, an indicator of road roughness nonstationarity, may underestimate excess fuel consumption by 30%. Compared with a two-dimensional (2D) half-car model, a three-dimensional (3D) full-car model reduces computation error of excess fuel consumption by approximately 11%. For practical implementation, the extended roughness-speed-impact (ERSI) model (R 2 ¼ 98%) is developed to estimate roughness-induced energy dissipation of five two-axle vehicles.
... The crowdsourcing aspects were discussing aggregation and visualization techniques [10], data synergy from multiple sourcing data [11] or proposed different online systems for data aggregation [12]. As for the road quality scoring, usually the IRI (International Roughness Index) is used [13], or its electronic counterpart, eIRI [14,15]. The IRI is not perfectly understandable for the average road user, so alternative quality indicators have been also proposed [16]. ...
In this paper, the authors are showing a calculation of the road quality index called Simple Road Quality Index (SRQI) using the weight provided by the amateur drivers to best possibly rate their comfort on driving on that road. The index is calculated from acceleration data acquired by the smartphone application and is aggregated in a crowdsourcing system for the classification of road quality using the fuzzy membership function. The paper shows that the proposed index correctly shows road quality changes over time and may be used as a way to mark roads to be avoided or needs to be repaired. The numerical experiment was based on the same street in Lublin, Poland, in 2015-2021 and is correctly showing that the quality of analyzed roads deteriorated over time, especially in the winter season.
Road administrators as an implementer of road asset management (RAM) are obliged to control everything from asset planning, inspection maintenance, rehabilitation, etc. In Indonesia, the Ministry of Public Work and Housing dealing with road infrastructures has issued a policy to prioritize Optimization, Maintenance, Operation, and Rehabilitation, abbreviated as OMOR, rather than constructing new infrastructures during the pandemic COVID-19. This is in line with the concept of RAM including maintenance and rehabilitation activities of the road surface in pavement management. This study considers the trip-based model and user satisfaction approaches to optimize pavement maintenance and rehabilitation activities in the OMOR processes. The trip-based model is a result of the network traffic speed throughout the trip duration. The primary objectives of this research are as follows: (1) investigating the influence of pavement maintenance and rehabilitation on road networks based on the trip-based model, (2) surveying user satisfaction in the road network in terms of pavement condition, and (3) optimizing RAM processes for the OMOR. Approaches and methodologies are analysis of the pavement roughness quantified with the International Roughness Index (IRI) using a driving simulator in terms of questionnaire-based user satisfaction. The finding illustrates what exact IRI is comfortable through various speeds in trip-based viewpoints. It contributes to RAM by developing a new maintenance criterion, especially for local roads in terms of IRI.
Keywords: road asset management, pavement management, trip-based model, user satisfaction
div class="section abstract"> Road roughness is the most important source of vertical loads for road vehicles. To capture this during durability engineering, various mathematical models for describing road profiles have been developed. The Laplace process has turned out to be a suitable model, which can describe road profiles in a more flexible way than e.g., Gaussian processes. The Laplace model essentially contains two parameters called C and ν (to be explained below), which need to be adapted to represent a road with certain roughness properties. Usually, local road authorities provide such properties along a road on sections of constant length, say, 100 m. Often the ISO 8608 roughness coefficient or the IRI (International Roughness Index) are used. In such cases, there are well known explicit formulas for finding the parameters C and ν of the Laplace process, which best fits the road under certain assumptions. Besides local road authorities there are also other sources of roughness data, for instance commercial providers of digital maps. Sometimes, the information is given for road sections of varying length such that these formulas do not apply anymore. Therefore, this paper suggests a method to fit the Laplace parameters in the more general setting of non-equidistant roughness data. Neither the roughness indicators nor the Laplace model are modified, just the process of parameter fitting is adapted. The task is reduced to solving a maximum-likelihood problem. In addition, an explicit formula approximating the maximum-likelihood solution for the Laplace parameters C and ν is derived. This extends the explicit formulas known for data on an equidistant grid. The methods and results are validated based on roughness data for roads of a total length of 1270 km. Road surface properties are also highly relevant for vehicle vibration, ride comfort, or handling. However, this is not considered further in this paper.
</div
In diesem Beitrag präsentieren wir das sog. IdenT-System, welches im Rahmen des gleichnamigen Forschungsprojekts für LKW-Trailer entwickelt wird. Wir betrachten eine spezifische Teilkomponente, nämlich die Online-Identifikation von Straßenprofilen und -rauigkeiten basierend auf Trailer-Messungen, einfachen Modellen und maßgeschneiderten mathematischen Verfahren. Die Rauigkeit des befahrenen Straßensegments wird mithilfe des sog. IRIs (International Roughness Index) aus dem Profil abgeleitet und dient u.a. der Detektion besonders beanspruchender und kritischer Straßensegmente. Für solche wird im IdenT-System u.a. ein Signal ausgelöst, das den auf der Cloud-Plattform befindlichen Offline-Zwilling startet, der für solche Straßenabschnitte höher aufgelöste Informationen generiert. Die basierend auf den identifizierten Straßeneigenschaften gewonnenen Informationen dienen der Überwachung des Trailerzustands und können zukünftig einen Beitrag dazu leisten, Sattelzüge sicherer zu betreiben.
Road roughness-induced vibrations are the main source of fatigue damage accumulation in vehicles. Hence, road surface profile modeling and simulation are of high importance when it comes to vehicle fatigue damage assessment.
This research focuses on uncovering the statistical distributions that describe the characteristics of road loads that affect fatigue damage accumulation. Particle filtering is deployed to estimate the log-volatility of the road surface profile by assuming a random walk behavior for the hidden log-volatility. The skew-normal distribution with three parameters is fitted to the estimated log-volatility. The inferred parameters are used for synthesizing artificial road profiles used in a vehicle simulation to calculate fatigue damage indices (FDI). The new road profile model outperforms all the previous models in predicting the real road FDI.
WAFO (Wave Analysis for Fatigue and Oceanography) is a toolbox of Matlab routines for statistical analysis and simulation of random waves and random loads. An important part are routines for calculation of theoretical distributions of wave characteristics from observed or theoretical power spectra of the sea. A new philosophy concerning documentation is presented. The tool-box contains scripts for generation of plots in recent publications and scripts for comparison with results in the literature. The theoretical density of rainflow cycles can be computed from parameters of random loads.
About 25,800 longitudinal profiles of in-service roads in the USA (Long-Term Pavement Performance (LTPP) Program) were evaluated to obtain indicators of the longitudinal unevenness (roughness) of the road surface defined by standards ISO 8608 : 1995 and EN 13036-5 : 2006. Besides the commonly used International Roughness Index (IRI), also parameters of the Power Spectral Density (PSD) were evaluated taking into account not only the whole interval of effectively acting wavelengths, but also its two and three partial sub-bands. Differences between left and right tracks and between Asphalt Concrete (AC) and Portland Cement Concrete (CC) wearing courses were considered. For all the mentioned indicators (21), their Probability Density Functions (PDF) and numerical statistics were estimated.
With the general lack of equivalence between the many methods and measures by which road roughness is characterized, standardized indices offer the means to achieve a time-stable data base that can be utilized by all. The International Road Roughness Experiment (IRRE) was organized in Brasilia, Brazil, to find a suitable index and to quantify the relationships between different equipment and roughness indices in use. Roughness measurements were made on 49 test sites by diverse types of equipment in common use. The data were analyzed to determine the equivalence between the roughness measures that could be obtained with each type of equipment and whether one common measure was applicable to all. The results from the IRRE showed that a standard roughness index is practical and measurable by most of the equipment in use today. As a result of the IRRE, a standard index was selected that is based on the quarter-car analysis method.
This concluding part of the paper is reprinted, with permission, from the Proceedings of the 38th Annual Technical Meeting of the Institute of Environmental Sciences, Nashville, Tennessee, May 1992, and presents the final part of the discussions on the vibration and shock environments perceived by equipment when transported by wheeled vehicles. Methodologies are presented for deriving environment descriptions and test severities from measured road transportation dynamic response data. Measured responses from a data gathering trial are used to illustrate a number of useful signal processing techniques and illustrate the compilation of environment descriptions that quantify the severity and character of measured data. The environment description is used as the basis for the compilation of a test severity definition in terms of a random vibration spectrum and duration.
This paper proposes that the non-Gaussian (leptokurtic) nature of pavement surface elevation data is a direct result of the inherent level-type non-stationarity of the process manifested as variations in magnitude or roughness. The hypothesis that random pavement profiles are essentially composed of a sequence of zero-mean random Gaussian processes of varying standard deviations is put forward and tested. This paper introduces a numerical approach for decomposing non-stationary random vibration signals into constituent Gaussian elements by extracting Gaussian component of varying root mean square (RMS) levels from the distribution estimates using a curve fitting algorithm. The validity of the method was tested using a representative set of pavement profiles. The decomposition method presented is significant in that it affords great simplicity for the synthesis of non-stationary pavement profiles which can be achieved without much difficulty when the process is represented by a sequence of Gaussian events.