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Dowling, Jason
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Dowling, Jason
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Dowling, Jason
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Now showing 1 - 9 of 9
- PublicationFirst Order Moving Force Identification Applied to Bridge Weigh-In-Motion(2013-12)
; ; ; Bridge Weigh-In-Motion systems are based on the measurement of the deformation of a bridge and the use of these measurements to estimate the attributes of passing traffic loads. Despite many advantages, Bridge Weigh-In-Motion algorithms have often failed to predict axle weights accurately due to noise and vehicle and bridge dynamics. The algorithm in this paper uses Moving Force Identification theory and it applies first order Tikhonov regularization in conjunction with dynamic programming to predict the unknown traffic forces from simulated bridge strain measurements. An accurate finite element mathematical model that resembles the response of the bridge is needed to predict the applied forces. For this purpose, a calibration method based on the Cross-Entropy Optimization algorithm is used to adjust the mass and stiffness matrices of the finite element model. Once the model has been calibrated, the algorithm requires accurate velocity and axle spacing of the vehicle forces and the continuous strain record that they induce on the bridge. Sensitivity analyses are carried out to demonstrate the effect of errors in each of these required inputs. It is shown that the approach proposed herein has the potential to estimate static weights and the time history of the forcing function of each axle accurately.243 - PublicationDirect measurement of dynamics in road bridges using a bridge weigh-in-motion system(Technika. Vilnius Gediminas Technical University, 2013-12)
; ; ; A method is presented of measuring a bridge’s characteristic allowance for dynamic interaction in the form of Assessment Dynamic Ratio. Using a Bridge Weigh-in-Motion system, measurements were taken at a bridge in Slovenia over 58 days. From the total observed traffic population, 5-axle trucks were extracted and studied. The Bridge Weigh-in-Motion system inferred the static weights of the trucks, giving each measured event’s dynamic increment of load. Theoretical simulations were carried out using a 3-dimensional vehicle model coupled with a bridge plate model, simulating a traffic population similar to the population measured at the site. These theoretical simulations varied those properties of the 5-axle fleet that influence the dynamic response; simulating multiple sets of total (dynamic + static) responses for a single measured static strain response. Extrapolating the results of these theoretical simulations to a 50-year Assessment Dynamic Ratio gives similar results to those obtained by extrapolating the data measured using the Bridge Weigh-in-Motion system. A study of the effect of Bridge Weigh-in-Motion system errors on the predictions of Assessment Dynamic Ratio is conducted, identifying a trend in the Bridge Weigh-in-Motion calculations of maximum static response. The result of this bias is in turn quantified in the context of predicting characteristic maximum total load effect.452Scopus© Citations 10 - PublicationCharacteristic dynamic traffic load effects in bridges(Elsevier, 2009-07)
; ; ; ; When formulating an approach to assess bridge traffic loading with allowance for Vehicle-Bridge Interaction (VBI), a trade-off is necessary between the limited accuracy and computational demands of numerical models and the limited time periods for which experimental data is available. Numerical modelling can simulate sufficient numbers of loading scenarios to determine characteristic total load effects, including an allowance for VBI. However, simulating VBI for years of traffic is computationally expensive, often excessively so. Furthermore, there are a great many uncertainties associated with numerical models such as the road surface profile and the model parameter values (e.g., spring stiffnesses) for the heavy vehicle fleet. On site measurement of total load effect, including the influence of VBI, overcomes many of these uncertainties as measurements are the result of actual loading scenarios as they occur on the bridge. However, it is often impractical to monitor bridges for extended periods of time which raises questions about the accuracy of calculated characteristic load effects. Soft Load Testing, as opposed to Proof Load or Diagnostic Load Testing, is the direct measurement of load effects on bridges subject to random traffic. This paper considers the influence of measurement periods on the accuracy of soft load testing predictions of characteristic load effects, including VBI, for bridges with two lanes of opposing traffic. It concludes that, even for relatively short time periods, the estimates are reasonably accurate and tend to be conservative. Provided the data is representative, Soft Load Testing is shown to be a useful tool for calculating characteristic total load effect.1310Scopus© Citations 56 - PublicationCritical speed for the dynamics of truck events on bridges with a smooth road surface(Elsevier, 2010-05-24)
; ; ; ; ; Simple numerical models of point loads are used to represent single and multiple vehicle events on two-lane bridges with a good road profile. While such models are insufficiently complex to calculate dynamic amplification accurately, they are presented here to provide an understanding of the influence of speed and distance between vehicles on the bridge dynamic response. Critical combinations of speed as a function of main bridge natural frequency and meeting point of two vehicles travelling in opposite directions are identified. It is proposed that such simple models can be used to estimate the pattern of critical speeds versus dynamic amplification for heavy trucks on a bridge with a relatively smooth surface. The crossing of a three-dimensional spring-dashpot truck is simulated over a bridge plate model to test this hypothesis for a range of road roughness. Further validation is carried out using the site-specific mean pattern associated to field measurements due to the passage of a truck population. The latter is found to be closely resembled by the theoretical pattern derived from simple point load models.1573Scopus© Citations 22 - PublicationAdaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problemMoving Force Identification (MFI) theory can be used to create an algorithm for a Bridge Weigh-in-Motion (WIM) system that can produce complete force histories of the loads that have traversed a bridge structure. MFI is based on general inverse theory, however, and calibration of such a system requires a complete Finite Element (FE) model of the bridge to be available for implementation in the field. This is something that is often infeasible in practice as FE models created using theoretical values for material properties bear a poor relation to reality. The Cross Entropy optimisation method has been adapted here to address this calibration problem. The general system FE global mass and stiffness matrices of the bridge FE model are found by best fit optimisation to match field measurements. In this fashion a fully automated calibration procedure is developed for an MFI algorithm. This system is tested theoretically using three different FE plate models, coupled with a 3-dimensional vehicle model, allowing for Vehicle–Bridge Interaction (VBI).
980Scopus© Citations 42 - PublicationA filtered measured influence line approach to bridge weigh-in-motionIn Bridge Weigh-in-Motion (B-WIM), an instrumented bridge is used as a scales to weigh passing trucks and their axles. The most common algorithm upon which modern B-WIM systems are based remains that developed by Moses (1979). The performance of this method is well documented; it is very good at estimating Gross Vehicle Weight, but less accurate for individual axles, particularly closely spaced axles on longer bridges. Many alternatives to Moses's original algorithm have been tested and some show the potential to improve accuracy but commercially available B-WIM systems are still based substantially on the original approach. This paper proposes a method of altering the B-WIM algorithm to improve the accuracy of the predictions. The measured dynamic signal, to which the algorithm is applied, is first filtered to remove high frequency components of the dynamic increment of load. The influence line used by the algorithm is also calculated differently. As previously described by OBrien et al. (2006) it is determined using a pre-weighed calibration truck and an algorithm to automatically convert the corresponding measured signal into a 'measured' influence line. However, for this work, the measured signal is first filtered to remove much of the high frequency dynamic components which results in a significant improvement in the overall accuracy of the system. Moses's equations are applied as in most other B-WIM systems but, in this case, using a filtered measured influence line and a filtered signal for the unknown truck. In this way, Moses's least squares fitting method is now comparing only the low frequency components of the measured and theoretical responses and produces a much more accurate fit. The new approach is tested in numerical models and it is shown to result in a substantial improvement in accuracy.
623 - PublicationFinite Element Model Updating Using Cross-EntrophyThis paper presents the potential of the cross-entropy method to surmise the properties of a simply supported beam using as input the response of the structure to a moving load. The beam model is discretised into a number of elementary beams with assumed initial statistical distributions of stiffness. Then, an optimisation procedure based on cross-entropy is employed to minimise differences between simulated measurements and the results of the theoretical finite element beam model. The procedure consists of generating a large sample of stiffness distributions for each elementary beam, and selecting those fitting the measured response best. Then, the parameters of the statistical distribution of stiffness assumed for each elementary beam (mean and standard deviation) are updated using the stiffness values of those combinations of elementary beams giving a best solution. It is an iterative procedure where the mean value of each distribution tends towards the true stiffness in successive iterations. The level of accuracy is limited by the quantity and quality of the available measurements. Therefore, the standard deviation of the final stiffness for each beam element (once further iterations do not lead to a reduction of the error) provides an estimation of the reliability of the prediction. Here, the method is demonstrated for the characterisation of the stiffness distribution of a beam from the simulated response to a moving load. First, deflections are calculated using a finite element beam model with assumed initial stiffness properties. There will be a record of simulated responses per measurement point that cross-entropy will try to imitate by adjusting and improving estimations of stiffness in successive iterations. The results show cross-entropy can be used as a valuable tool to estimate structural parameters and it has huge scope for applications in model calibration, bridge weigh-in-motion and monitoring.
245 - PublicationExperimental determination of dynamic allowance for traffic loading in bridges(Transportation Research Board, 2010)
; ; ; Bridge codes adopt values for dynamic allowance in traffic load models that are necessarily conservative to cover for an entire range of bridges with different mechanical characteristics, boundary conditions, and the large number of uncertainties associated to the vehicle-bridge interaction problem. A further level of conservatism occurs due to the independent manner in which the governing static load and the corresponding allowance for dynamics are specified. In particular, certain bridges are not susceptible to high levels of vehicle-bridge interaction when loaded by a critically heavy vehicle or a critical combination of vehicles. Recent advances in Bridge Weigh-In-Motion technology allow not only to collect information on the weights, spacings and speeds of the traffic loads traversing a bridge, but also to separate the maximum static strain from the total measured strain using a filtering procedure. In this paper, maximum static and total load effects are collected and analysed for three different sites as part of the European project ARCHES (6th RTD framework programme). Bridge measurements are used to discuss the dynamics of the most frequent truck classes and the entire traffic sample. The measurements reveal a decrease in percentage increment in dynamics and a reduction on the variability of the dynamic increment as the static load effect increases. This phenomenon can be of particular relevance in the assessment of the dynamics of extreme loading cases.348 - PublicationTesting of a Bridge Weigh-in-Motion Algorithm Utilising Multiple Longitudinal Sensor LocationsA 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.
1931Scopus© Citations 24