Interaction Between Heavy Vehicles and Roads

Cebon, David

doi:10.4271/930001

Cited by 137 publications

(103 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heavy goods vehicles and buses are found to produce the most perceptible vibrations. The vehicle models used to describe the dynamic behaviour of a vehicle are composed of discrete masses, springs, friction elements and dampers [2][3][4][5][6]. When a linear vehicle model is used, vehicle Frequency Response Functions (FRF) facilitate the calculation of the axle loads [2,3].…”

Section: Introductionmentioning

confidence: 99%

The vibrations induced by surface irregularities in road pavements – a Matlab® approach

Agostinacchio

Ciampa

Olita

2013

Eur. Transp. Res. Rev.

205

View full text Add to dashboard Cite

Purpose The paper tackles the theme of evaluating dynamic load increases that the vehicle transfers to the road pavement, due to the generation of vibration produced by surface irregularities. Method The study starts from the generation, according to the ISO 8608 Standard, of different road roughness profiles characterized by different damage levels. In particular, the first four classes provided by ISO 8608 were considered. Subsequently, the force exchanged between the pavement and three typologies of vehicles (car, bus and truck) has been assessed by implementing, in Matlab®, the QCM (Quarter Car Model) characterized by a quarter vehicle mass and variable speed from 20 to 100 km/h. The analysis allows determining the amount of dynamic overload that causes the vibrational stress.Results/Conclusions The paper shows how this dynamic overload may be predetermined as a function of the pavements surface degradation. This is a useful reference for the purposes of designing and maintaining road pavements.

show abstract

Section: Introductionmentioning

confidence: 99%

The vibrations induced by surface irregularities in road pavements – a Matlab® approach

Agostinacchio

Ciampa

Olita

2013

Eur. Transp. Res. Rev.

205

View full text Add to dashboard Cite

show abstract

“…The three dimensional model has a total of 15 degrees of freedom: one vertical hop for each wheel assembly; bounce, pitch and roll of the tractor body mass; bounce and pitch of the trailer body mass (Figure 1). This vehicle model is similar to many used previously for vehicle-bridge interaction modelling [Gillespie et al 1979, El-Madany 1988, Wang and Huang 1992, Cebon 1993]. The Newmark-β direct integration scheme [Tedesco et al 1999] is used to solve the vehicle system equations and calculate the vehicle response over the road profile.…”

Section: Vehicle-bridge Interaction Modelmentioning

confidence: 99%

Testing of a Bridge Weigh-in-Motion Algorithm Utilising Multiple Longitudinal Sensor Locations

González

Dowling

O’Brien

et al. 2012

Journal of Testing and Evaluation

View full text Add to dashboard Cite

A new bridge weigh-in-motion (WIM) algorithm is developed which makes use of strain sensors at multiple longitudinal locations of a bridge to calculate axle weights. The optimisation procedure at the core of the proposed algorithm seeks to minimise the difference between static theory and measurement, a procedure common in the majority of bridge WIM algorithms. In contrast to the single unique value calculated for each axle weight in common Bridge WIM algorithms, the new algorithm provides a time history for each axle based on a set of equations formulated for each sensor at each scan. Studying the determinant of this system of equations, those portions of the time history of calculated axle weights for which the equations are poorly conditioned are removed from the final reckoning of results. The accuracy of the algorithm is related to the ability to remove dynamics and the use of a precise influence line. These issues are addressed through the use of a robust moving average filter and a calibration procedure based on using trucks from ambient traffic. The influence of additional longitudinal sensor locations on the determinant of the system of equations is discussed. Sensitivity analyses are carried out to analyse the effect of a misread axle spacing or velocity on the predictions, and as a result, the algorithm reveals an ability to identify potentially erroneous predictions. The improvement in accuracy of the calculated axle weights with respect to common approaches is shown, first using numerical simulations based on a vehicle-bridge interaction finite-element model, and second using experimental data from a beam-and-slab bridge in Slovenia.

show abstract

“…It is therefore assumed here that such a pattern can be captured with a simple uniaxial truck fleet model. A two-degree-of-freedom quarter car model [19][20][21][22][23][24] (Figure 2) is used in this paper.…”

Section: Vehicle Modelmentioning

confidence: 99%

“…Nonetheless, Cebon [23] noted that the majority of single axle suspensions in current use can be broadly represented by this model.…”

Section: Vehicle Modelmentioning

confidence: 99%

Truck fleet model for design and assessment of flexible pavements

Belay¹,

O’Brien

Kroese

2008

Journal of Sound and Vibration

View full text Add to dashboard Cite

Interaction Between Heavy Vehicles and Roads

Cited by 137 publications

References 42 publications

The vibrations induced by surface irregularities in road pavements – a Matlab® approach

The vibrations induced by surface irregularities in road pavements – a Matlab® approach

Testing of a Bridge Weigh-in-Motion Algorithm Utilising Multiple Longitudinal Sensor Locations

Truck fleet model for design and assessment of flexible pavements

Contact Info

Product

Resources

About