Effect of Vehicle Velocity on the Dynamic Amplification of a Vehicle Crossing a Simply Supported Bridge

et al. 2009

Engineering Structures

Self Cite

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 effect in bridges subject to random traffic. This paper considers the influence of measurement period 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.

Section: Description Of the Modelmentioning

confidence: 99%

“…Many authors [1,10,11,15] report values of dynamic amplification factor, defined as the ratio of total load effect to the corresponding static load effect. Both the static and the total load effect in such a calculation correspond to the same loading scenario.…”

Section: Assessment Dynamic Ratio (Adr)mentioning

confidence: 99%

Characteristic dynamic traffic load effects in bridges

O’Brien

Rattigan

et al. 2009

Engineering Structures

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“…For the case of 'n' axle static weights, represented by W 1 ,W 2 , ...W n with axle spacings of a 1 , ... a n-1 , and front axle located at a distance d from the left hand support, the theoretical strain for a particular point in time would be given by Equation (2).…”

Section: Extension Of the Original Bridge Wim Algorithm To An Instantmentioning

confidence: 99%

“…The influence line ordinate at the location (d-a 1 ), is shown as I(d-a 1 ) in Equation (2). Then, the axle weights are inferred by minimising the error function with respect to the unknown axle weights.…”

Section: Extension Of the Original Bridge Wim Algorithm To An Instantmentioning

confidence: 99%

See 1 more Smart Citation

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

Journal of Testing and Evaluation

Dowling

O’Brien

et al. 2012

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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.

Damage detection in bridges based on patterns of dynamic amplification

Struct Control Health Monit

Mohammed

2019

frequencies with temperature, noting a daily change of 4.7%, 6.6%, and 5.0%, for the first three modal frequencies of a bridge deck in New Mexico that varied temperature by about 22°C, and changes of 14-18% for the first four modal frequencies of the Z24 bridge in Switzerland during 10 months. For a reinforced concrete beam and slab, Liu et al 4 suggest that modal frequencies will decrease 0.12-0.33% per degree Celsius with respect to the modal frequencies at 0°C, being the variation of concrete elasticity modulus with temperature the main cause of these changes. There are two approaches towards separating effects due to operational conditions from effects due to damage: (a) input-output methods, where operational variables such as temperature, humidity, and/or traffic loading are measured together with modal properties to establish their relationship, that is, via the polynomial chaos expansion method proposed by Spiridonakos et al 5 and (b) output-only methods, where operational variables are treated as embedded variables that do not need to be measured, as their influence will be discarded via methods such as neural networks, factor analysis, 6 principal component analysis, 7,8 or kernel principal component analysis. 9 Rytter 10 classifies vibration-based damage identification approaches into four levels of increasing degree of complexity that establish: Whether damage occurred or not (Level 1), the position of damage (Level 2), its quantification (Level 3), and the remaining lifespan of the structure (Level 4). Fan and Qiao 11 carry out a thorough review of the available vibration-based damage detection methods. Although there is a group of model-based SHM methods that can reach Level 4 depending on the amount of available data and the complexity of the finite element (FE) model to be updated, they are time-consuming compared with data-driven SHM methods, which are typically Levels 1 or 2 and able to provide results in real time. There is a third group of methods that combines model-based and data-driven SHM, that is, the damage-locating vector and statistical fault isolation methods, as discussed by Limongelli et al, 12 who compare data-driven with model-based SHM using the measurements of the post-tensioned concrete two-cell box Z24 bridge girder.Cebon 13 argues that heavy vehicles induce vibrations in the bridge that can cause damage to the structure and consequently; they affect its lifespan and safety. A pavement-based weigh-in-motion (WIM) system placed prior to the bridge (i.e., bending plate or piezoelectric sensors embedded in the pavement) can be employed to identify and prevent overloading. Load monitoring is the basis for the Level 1 damage detection technique proposed by Cantero and González 14 that measures truck loads via two means: (a) a pavement-based WIM system and (b) a bridge weigh-inmotion (B-WIM) system (i.e., from strains measured on the bridge soffit based on the concept of influence lines). The principle of their technique is that the estimation of truck loads by both WIM and B-WIM sy...