The effect of truck permitting policy on US bridge loading

O’Brien, Eugene J.; Enright, Bernard; Leahy, Cathal

doi:10.1201/b16387-546

Cited by 4 publications

(3 citation statements)

References 10 publications

(12 reference statements)

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“…This approach could only be used if it is reasonable to exclude cars from data, for any suitable lower truck axle load or gross vehicle weight (GVW) limit will, surely, exclude all cars. Filters just for permit vehicles and cleaning based on three-step procedure, where truck weight changes are monitored, were proposed by OBrien et al (2013) and Mai et al (2013) respectively. Both methods require large data samples.…”

Section: Introductionmentioning

confidence: 99%

Comparision of constant-span and influence line methods for long-span bridge load calculations

Paeglītis

Freimanis

2016

The Baltic Journal of Road and Bridge Engineering

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Traffic load models available in building standards are most often developed for short or medium span bridges, however, it is necessary to develop traffic load models just for long span bridges, because the most unfavourable traffic situations are different. Weigh-in-Motion system data from highway A1 and A3 were used in this study. Measurement errors from data were cleaned using two groups of filters. The first group was based on vehicle validity codes recorded by both systems, if any circumstances might have influenced the measurements, the second group cleaned data using general filters for all vehicles and specific filters for trucks and cars. Additionally, vehicles were adjusted for influence of temperature. Data cleaning increased the average gross vehicle, so it could be considered as a conservative choice. Six traffic scenarios, each with different percentage of cars in the traffic, were made to assess the difference in loads from different traffic compositions. Traffic loads for long-span bridges were calculated using two approaches: the first assuming constant span length, the second, using influence lines from a bridge currently in design stage. Gumbel distribution were fitted to the calculate loads and they were extrapolated to probability of exceedance of 5% in 50 year period. Results show that influence line approach yield larger loads than those from constant-span. Both approaches result in loads larger than ones in Eurocode 1 Load Model 1, however, increase might have been caused by an increase in vehicle weight.

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Section: Introductionmentioning

confidence: 99%

Comparision of constant-span and influence line methods for long-span bridge load calculations

Paeglītis

Freimanis

2016

The Baltic Journal of Road and Bridge Engineering

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“…As an alternative to direct extrapolation from measured data, Monte Carlo simulation can be used [6,7]. This involves fitting suitable distributions to the various measured parameters-axle weights, axle spacings, intervehicle gaps, traffic flow rates, etc.…”

Section: Introductionmentioning

confidence: 99%

“…These previously mentioned studies [6][7][8] generally assume that the characteristics of the traffic (i.e. truck weights, number of axles, flow, etc) are stationary and that future traffic will have the same characteristics as the currently measured traffic.…”

Section: Introductionmentioning

confidence: 99%

Traffic Load Effect Forecasting for Bridges

2015

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Traffic flows, as well as truck weights, increase with time. This must be taken into account in order to accurately assess traffic loading on bridges. The Eurocode Load Model 1 is used for the design of new bridges but a scaled down version of the model can be used for the assessment of existing bridges. This scaling is usually done by applying α-factors to the load model. The effect of traffic growth on these α-factors is assessed in this paper. Weigh-in-motion data from the Netherlands is used as the basis for traffic models which simulate year-on-year growth of both traffic flow and truck weights. A time-varying generalised extreme value distribution is then used to calculate the characteristic load effects and determine the α-factors. The effect of different traffic growth rates on these α-factors is then examined. It is found that an increase in truck weights has the most influence on the α-factors but that increased flow also has a significant effect.

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