Assessment dynamic ratio for traffic loading on highway bridges

Caprani, Colin C.; González, Arturo; Rattigan, Paraic; O’Brien, Eugene J.

doi:10.1080/15732471003667645

Cited by 23 publications

(14 citation statements)

References 19 publications

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“…This information is nowadays commonly available from Weigh-In-Motion (WIM) stations, often paired with inductive loop detectors. Importantly, recent studies have shown that the dynamic increment for extreme loading events may not be as high as previously thought [2,3]. This has the potential of lowering the above-mentioned threshold between the two governing traffic states [4].…”

Section: Research On Highway Bridge Traffic Loadingmentioning

confidence: 90%

Highway Bridge Traffic Loading

Lipari¹

2016

Structural Bridge Engineering

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In this chapter, traditional approaches and recent advances in highway bridge traffic loading are described, which are of great significance for structural safety assessment of bridges. Indeed, it is widely accepted that consideration of site-specific traffic features can enable significant savings in maintenance operations. While short spans are governed by free-flowing traffic plus an allowance for the dynamic effects, long spans are governed by congested conditions. For the former, a promising research trend is the investigation of the dynamic vehicle-bridge interaction, which is shown to lead to dynamic effects much lower than previously thought. For the latter, advances in traffic flow modelling enable the simulation of realistic congestion patterns based on widely available free-flowing traffic data, thus partially overcoming a long-standing shortage of congestion data. Here, emphasis is given to the promising application of traffic microsimulation to long-span bridge loading, combined with a probabilistic approach based on the extreme value theory, to compute site-specific characteristic loading values.

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Section: Research On Highway Bridge Traffic Loadingmentioning

confidence: 90%

Highway Bridge Traffic Loading

Lipari¹

2016

Structural Bridge Engineering

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“…In this study, the authors have modeled the bridge by frame elements, and they have modeled the joints and support by axial springs and torsion spring, respectively. To estimate the dynamic impact factor in road bridges, González et al and Caprani et al have used finite element models, probability, and statistical simulations 39,40 …”

Section: Literature Reviewmentioning

confidence: 99%

“…To estimate the dynamic impact factor in road bridges, González et al and Caprani et al have used finite element models, probability, and statistical simulations. 39,40 Eventually, the effects of span length, the first frequency, and train speed on impact factor, the displacement of mid-span of 11 railway bridges, have been measured by Ataei and Miri. 41 Then, the results from different directives (including AREMA, Eurocode, and ORE) were compared.…”

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confidence: 99%

Probabilistic estimation of dynamic impact factor for masonry arch bridges using health monitoring data and new finite element method

Nouri

Mohammadzadeh

2020

Struct Control Health Monit

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The presence of uncertainties in the behavior of rolling stock, infrastructure, and the interaction between them undoubtedly generates a probabilistic nature for the structure impact factor. Such an effect becomes more severe in the case of the aged structures, specifically masonry arch bridges. This research initially provides a probabilistic estimation for the impact factor based on the ratio of the dynamic to static loads by using Weigh-In-Motion data. Then, the research is followed by proposing a new finite elements approach for a more precise estimation of the structure response pattern and a more rigorous evaluation of the masonry arch bridges. It uses optimization techniques for updating the model and results in the probabilistic estimation of the impact factor by using field measurements. Through these two methods and by using data from continuous monitoring of an 80-year-old masonry arch bridge with a length of 100 m and a span of 66 m, the impact factor is surveyed. The outputs prove to be 50% more accurate compared with the already established methods. The rate of variations of the impact factor within the first proposed method is 1.6 times more compared with the second approach. This emphasizes the uncertainties in the available approaches and the probabilistic nature of the impact factor. Therefore, the assumption of the absolute certainty for the impact factor becomes questionable. This survey reveals that for the best cases, the available certainty-based methods can only cover 17% of the probabilistic domain of the impact factor.

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“…For instance, OBrien et al (2010) presented an assessment dynamic ratio (ADR) to investigate the influence of vehicle-bridge interaction (VBI) on medium-span bridges. Subsequently, Caprani et al (2012) utilized the ADR in conjunction with a multivariate GEV theory to investigate the dynamic allowance of a highway bridge under long-term traffic loading. Even though these developments are mostly concentrated on short-to medium-span bridges, a solid foundation has been provided for the extended application to long-span bridges.…”

Section: Introductionmentioning

confidence: 99%

Lifetime Deflections of Long-Span Bridges under Dynamic and Growing Traffic Loads

Beer

Noori

et al. 2017

J. Bridge Eng.

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Steady traffic growth may pose a safety hazard to in-service bridges, especially long-span bridges subjected to the simultaneous presence of multiple heavy-duty trucks. This study presents a methodology for evaluating the statistical extrapolation of traffic load effects on long-span bridges. As part of the contributions advancing the state of the art, this study addresses several challenging issues, including traffic growth, the resulting dynamic impact and actual traffic patterns. The nonstationarity of the traffic load effects due to traffic growth is considered in a series system compounded by several interval traffic models. The dynamic impacts of traffic loads are simulated by a traffic-bridge coupled vibration system, and its statistical characteristics are captured using the Rice level-crossing model. The actual traffic pattern is simulated by stochastic traffic flows based on the statistics of the weigh-in-motion measurements of a highway bridge. Two numerical examples demonstrate the ability of the interval traffic growth model to capture the nonstationarity of the growing traffic loads. In

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Assessment dynamic ratio for traffic loading on highway bridges

Cited by 23 publications

References 19 publications

Highway Bridge Traffic Loading

Highway Bridge Traffic Loading

Probabilistic estimation of dynamic impact factor for masonry arch bridges using health monitoring data and new finite element method

Lifetime Deflections of Long-Span Bridges under Dynamic and Growing Traffic Loads

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