scholarly journals Lane changing control to reduce traffic load effect on long-span bridges

2012 ◽  
pp. 2842-2849
Author(s):  
C Caprani ◽  
B Enright ◽  
C Carey
Keyword(s):  
Traffic Load ◽  
Lane Changing ◽  
Load Effect ◽  
Long Span Bridges ◽  
Long Span

Vehicle Load Effect of Long-Span Bridges

2015 ◽  
Vol 2481 (1) ◽  
pp. 132-139 ◽  
Author(s):  
Jun Wu ◽  
Fei Yang ◽  
Wanshui Han ◽  
Liujie Wu ◽  
Qiang Xiao ◽  
...  
Keyword(s):  
Load Effect ◽  
Long Span Bridges ◽  
Long Span ◽  
Vehicle Load

An efficient approach for traffic load modelling of long span bridges

2019 ◽  
Vol 15 (5) ◽  
pp. 569-581 ◽  
Author(s):  
Junyong Zhou ◽  
Xin Ruan ◽  
Xuefei Shi ◽  
Colin C. Caprani
Keyword(s):  
Traffic Load ◽  
Long Span Bridges ◽  
Efficient Approach ◽  
Long Span

scholarly journals Simplified vehicle–bridge interaction for medium to long-span bridges subject to random traffic load

2020 ◽  
Vol 10 (4) ◽  
pp. 693-707
Author(s):  
Soheil Sadeghi Eshkevari ◽  
Thomas J. Matarazzo ◽  
Shamim N. Pakzad
Keyword(s):  
Traffic Load ◽  
Long Span Bridges ◽  
Long Span

scholarly journals Estimation of Extreme Load Effects on Long-Span Bridges Using Traffic Image Data

2018 ◽  
Vol 13 (4) ◽  
pp. 429-446 ◽  
Author(s):  
Elena Alexandra Micu ◽  
Eugene John Obrien ◽  
Abdollah Malekjafarian ◽  
Michael Quilligan
Keyword(s):  
Extreme Values ◽  
Image Data ◽  
Traffic Load ◽  
Long Span Bridges ◽  
Motion Data ◽  
Congested Traffic ◽  
Long Span ◽  
Weigh In Motion ◽  
Extreme Load ◽  
One Year

This paper proposes an algorithm for the estimation of extreme intensity of traffic load on long-span bridges. Most Weigh-in-Motion technologies do not operate in congested conditions which are the governing cases for these bridges. In the absence of Weigh-in-Motion data on the bridge itself, a correlation between vehicle weights and their lengths is established here using a (free- flowing) Weigh-in-Motion database. Photographic images of congested traffic are modelled here for three bridges using weights estimated from lengths and one year of Weigh-in-Motion data. The actual weights are taken from the Weigh-in- Motion data, and the results are compared to test the method. The gaps between vehicles are firstly set to a constant value and later to Beta-distributed values according to vehicle type. The intensity of traffic load for all pictures is calculated and compared to the loads obtained from the recorded weights. A return period of 75-year is chosen to evaluate the extreme values of intensity. The probability that intensity of load is being exceeded is obtained using normal probability paper for both recorded and simulated weights. This study demonstrates the feasibility of the proposed concept of using lengths to estimate the extreme traffic load events with acceptable accuracy.

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

2016 ◽  
Vol 11 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Ainars Paeglitis ◽  
Andris Freimanis
Keyword(s):  
Measurement Errors ◽  
Gumbel Distribution ◽  
Traffic Load ◽  
Design Stage ◽  
Long Span Bridges ◽  
Load Models ◽  
Long Span ◽  
Motion System ◽  
Influence Line ◽  
Long Span Bridge

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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