Arturo González

The use of vehicle acceleration measurements to estimate road roughness

Road roughness is a broad term that incorporates everything from potholes and cracks to the random deviations that exist in a profile. To build a roughness index, road irregularities need to be measured first. Existing methods of gauging the roughness are based either on visual inspections or using one of a limited number of instrumented vehicles that can take physical measurements of the road irregularities. This paper proposes the collection of data from accelerometers fixed in a specific vehicle type and the use of this data to estimate the road condition. Although the estimate is approximate, accelerometers are being increasingly used by car manufacturers to improve suspension performance and the proposed method is relatively inexpensive to implement and provide road managers with constantly updated measurements of roughness. This approach is possible due to the relationship between the power spectral densities of road surface and vehicle accelerations via a transfer function. This paper shows how road profiles can be accurately classified using axle and body accelerations from a range of simulated vehicle–road dynamic scenarios.

Characterization of non-linear bearings using the Hilbert–Huang transform

Changes in the performance of bearings can significantly vary the distribution of internal forces and moments in a structure as a result of environmental or operational loads. The response of a bearing has been traditionally idealized using a linear model but a non-linear representation produces a more accurate picture at the expense of modelling complexity and computational time. In this article, a lead rubber bearing is idealized using the hysteretic Bouc–Wen model. The Hilbert–Huang transform is then employed to characterize the features of the non-linear system from the instantaneous frequencies of the bearing response to a time-varying force. Instantaneous frequencies are also shown to be a useful tool in detecting sudden damage to the bearings simulated by a reduction in the effective stiffness of the force-deformation loop.

The use of a dynamic truck-trailer drive-by system to monitor bridge damping

Bridge structures are continuously subject to degradation due to the environment, ageing and excess loading. Periodic monitoring of bridges is therefore a key part of any maintenance strategy as it can give early warning if a bridge becomes unsafe. This article investigates an alternative method for the monitoring of bridge dynamic behaviour: a truck–trailer vehicle system, with accelerometers fitted to the axles of the trailer. The method aims to detect changes in the damping of a bridge, which may indicate the existence of damage. A simplified vehicle–bridge interaction model is used in theoretical simulations to assess the effectiveness of the method in detecting those changes. The influence of road profile roughness on the vehicle vibration is overcome by recording accelerations from both axles of a trailer and then analysing the spectra of the difference in the accelerations between the two axles. The effectiveness of the approach in detecting damage simulated as a loss in stiffness is also investigated. In addition, the sensitivity of the approach to the vehicle speed, road roughness class, bridge span length, changes in the equal axle properties and noise is investigated.

Vehicle-Bridge Dynamic Interaction Using Finite Element Modelling

First investigations on the dynamic response of bridges due to moving loads were motivated by the collapse of the Chester railway bridge in the UK in the middle of the 19th century. This failure made evident the need to gain some insight on how bridges and vehicles interact, and derived into the first models of moving loads by Willis (1849) and Stokes (1849). These models consisted of a concentrated moving mass where the inertial forces of the underlying structure were ignored. The latter were introduced for simple problems of moving loads on beams in the first half of the 20th century (Jeffcott, 1929; Inglis, 1934; Timoshenko & Young, 1955). Although Vehicle-Bridge Interaction (VBI) problems were initially addressed by railway engineers, they rapidly attracted interest in highway engineering with the development of the road network and the need to accommodate an increasing demand for heavier and faster vehicle loads on bridges. In the 1920’s, field tests carried out by an ASCE committee (1931) laid the basis for recommendations on dynamic allowance for traffic loading in bridge codes, and further testing continued in the 50’s as part of the Ontario test programme (Wright & Green, 1963). However, site measurements are insufficient to cover all possible variations of those parameters affecting the bridge response, and VBI modelling offers a mean to extend the analysis to a wide range of scenarios (namely, the effect of road roughness or expansion joints, the effect of vehicle characteristics such as suspension, tyres, speed, axle spacing, weights, braking, or the effect of bridge structural form, dimensions and dynamic properties). A significant step forward took place in the 50’s and 60’s with the advent of computer technology. It is of particular relevance the work by Frýba (1972), who provides an extensive literature review on VBI and solutions to differential equations of motion of 1-D continuous beam bridge models when subjected to a constant or periodic force, mass and sprung vehicle models. At that time, VBI methods were focused on planar beam and vehicle models made of a limited number of degrees of freedom (DOFs). From the decade of the 70’s, the increase in computer power has facilitated the use of numerical methods based on the Finite Element Method (FEM) and more realistic spatial models with a large number of DOFs. This chapter reports on the most widely used finite element techniques for modelling road vehicles and bridges, and for implementing the interaction between both. 26

Modelling the vehicle in vehicle-infrastructure dynamic interaction studies

Abstract This article presents the equations of motion for a general articulated road vehicle, with variable number of wheels for the tractor and trailer. The equations are applicable to vehicle—infrastructure dynamic interaction problems for two- and three-dimensional systems, allowing for the definition of a wide variety of vehicle configurations with the same formulae.

Dynamic Axle Force and Road Profile Identification Using a Moving Vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.

Experimental validation of a drive-by stiffness identification method for bridge monitoring

An experimental investigation is carried out to verify the feasibility of using an instrumented vehicle to detect and monitor bridge dynamic parameters. The low-cost method consists of the use of a moving vehicle fitted with accelerometers on its axles. In the laboratory experiment, the vehicle–bridge interaction model consists of a scaled two-axle vehicle model crossing a simply supported steel beam. The bridge model also includes a scaled road surface profile. The effects of varying the vehicle model configuration and speed are investigated. A finite element beam model is calibrated using the experimental results, and a novel algorithm for the identification of global bridge stiffness is validated. Using measured vehicle accelerations as input to the algorithm, the beam stiffness is identified with a reasonable degree of accuracy.

Empirical mode decomposition of the acceleration response of a prismatic beam subject to a moving load to identify multiple damage locations

Empirical Mode Decomposition (EMD) is a technique that converts the measured signal into a number of basic functions known as intrinsic mode functions. The EMD-based damage detection algorithm relies on the principle that a sudden loss of stiffness in a structural member will cause a discontinuity in the measured response that can be detected through a distinctive spike in the filtered intrinsic mode function. Recent studies have shown that applying EMD to the acceleration response, due to the crossing of a constant load over a beam finite element model, can be used to detect a single damaged location. In this paper, the technique is further tested using the response of a discretized finite element beam with multiple damaged sections modeled as localized losses of stiffness. The ability of the algorithm to detect more than one damaged section is analysed for a variety of scenarios including a range of bridge lengths, speeds of the moving load and noise levels. The use of a moving average filter on the acceleration response, prior to applying EMD, is shown to improve the sensitivity to damage. The influence of the number of measurement points and their distance to the damaged sections on the accuracy of the predicted damage is also discussed.

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

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.

Evaluation of an Artificial Neural Network Technique Applied to Multiple-Sensor Weigh-in-Motion Systems

Weigh-in-motion (WIM) accuracy in measuring static axle loads is affected by vehicle dynamics and noise. Neural networks can identify underlying relationships, such as the spatial repeatability in axle dynamics, and can efficiently remove noise. Furthermore, they can adapt to changing circumstances (e.g., traffic characteristics, road profile, or sensor failure), unlike conventional WIM calibration algorithms. The paper performance of a multilayer feed-forward artificial neural network algorithm applied to a multiple-sensor WIM is analyzed. Numerical simulations of the axle forces applied on a smooth road profile are used to train, validate, and test the artificial neural network algorithm. This dynamic axle load variation is predicted with the vehicle simulation model VESYM. The mechanical parameters of the truck models and their speeds are randomly varied over a range established from real traffic measurements. Once the theoretical WIM data are obtained at the sensor locations, the measurements are artificially corrupted with noise up to the typical level of WIM accuracy. Details are given on the process of the neural network design, the size of the training sample, and the length of the training period. The artificial neural networks approach resulted in higher accuracy than the traditional average-based calibration method, especially at high noise levels. As a result, it shows promise for estimating static axle loads from multiple WIM measurements.