Train–track–bridge modelling and review of parameters

Cantero, Daniel; Arvidsson, Therese; O’Brien, Eugene J.; Karoumi, Raid

doi:10.1080/15732479.2015.1076854

Cited by 86 publications

(64 citation statements)

References 61 publications

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“…There is an extensive literature about how to model the dynamic interaction between a vehicle and a bridge using FE models . In the case of highway bridges and typical trucks, uncoupled and coupled approaches are equivalent in most cases, and an uncoupled vehicle–bridge interaction (VBI) model is usually preferred for computational efficiency.…”

Section: Simulation Of the Response Of A Bridge To A Moving Vehiclementioning

confidence: 99%

Damage detection in bridges based on patterns of dynamic amplification

González

Mohammed

2019

Struct Control Health Monit

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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...

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Section: Simulation Of the Response Of A Bridge To A Moving Vehiclementioning

confidence: 99%

Damage detection in bridges based on patterns of dynamic amplification

González

Mohammed

2019

Struct Control Health Monit

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“…Track irregularities in vertical direction due to settlement and tilting of sleepers and horizontal misalignment of rails and sleepers should be very small to ensure riding safety and passenger comfort at high speeds. For instance, in [10] track irregularities are modeled as irregularity functions, composed of a stochastic superposition of harmonic functions with random phase angle and amplitude.…”

Section: Uncertainty Modelingmentioning

confidence: 99%

On computational interdisciplinary modeling for reliability assessment of high‐speed train – bridge interaction

Adam

Hirzinger

Oberguggenberger

et al. 2018

Proc Appl Math and Mech

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Bridge and crossing high-speed train represent an uncertain interacting dynamic system of high complexity with, in general, time-dependent system matrices. Thus, full reliability assessment of this system can only be conducted efficiently in an interdisciplinary approach, bringing together the disciplines of structural mechanics, engineering mathematics, and high performance computing. This contribution gives a brief summary on computational modeling issues of uncertain bridge-train interaction, its efficient stochastic simulation, and an outline of pros and cons of different strategies is provided.This is an open access article under the terms of the Creative Commons Attribution-Non-Commercial-NoDerivs Licence 4.0, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…The element coefficients can be adjusted to fit many different train types, as shown in [3]. Also adopted here, is a 2D model of a ballasted railway track with discrete sleepers.…”

Section: Background: a Simplified Prediction Modelmentioning

confidence: 99%

“…Such a realization is incorporated into the track model above by changing the vertical coordinates of the beam nodes accordingly. A comprehensive list of different PSDs available for track irregularities is presented in [3].…”

Section: Background: a Simplified Prediction Modelmentioning

confidence: 99%