Traffic Load Modelling for Urban Highway Bridges using Weigh-in-Motion Data

2018 ◽  
pp. 1681-1688
Author(s):  
T.L. Huang ◽  
J.J. Liao ◽  
J. Zhong ◽  
J.W. Zhong ◽  
H.P. Chen
Keyword(s):  
Highway Bridges ◽  
Traffic Load ◽  
Motion Data ◽  
Weigh In Motion

scholarly journals Vehicle-Assisted Techniques for Health Monitoring of Bridges

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3460 ◽  
Author(s):  
Hoofar Shokravi ◽  
Hooman Shokravi ◽  
Norhisham Bakhary ◽  
Mahshid Heidarrezaei ◽  
Seyed Saeid Rahimian Koloor ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Autonomous Vehicle ◽  
Highway Bridges ◽  
Traffic Load ◽  
Monitoring Methods ◽  
Conventional Vehicle ◽  
Weigh In Motion ◽  
Moving Vehicles ◽  
Existing Bridges ◽  
Future Work

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.

scholarly journals Traffic load modelling and factors influencing the accuracy of predicted extremes

2005 ◽  
Vol 32 (1) ◽  
pp. 270-278 ◽  
Author(s):  
Alan O'Connor ◽  
Eugene J O'Brien
Keyword(s):  
Highway Bridges ◽  
Traffic Load ◽  
Remaining Life ◽  
Paper Briefly ◽  
Weigh In Motion ◽  
Extreme Load ◽  
Load Effects ◽  
Recorded Data ◽  
Characteristic Values ◽  
Evolution With Time

Design and assessment of highway bridges requires accurate prediction of the extreme load effects expected during the proposed or remaining life of the structure. Traditionally these effects are calculated using conservative codified deterministic loading models. While this conservatism is relatively insignificant in design, it may be critical in assessment. Advances in weigh-in-motion (WIM) technology, i.e., the process of weighing trucks travelling at full highway speeds, have increased the availability of accurate and unbiased site-specific traffic records. Assessments performed using WIM data are generally accepted as less conservative than those performed using generalized codified loading models. This paper briefly describes traffic simulation using WIM statistics. The implications of the accuracy of the recorded data and the duration of recording and of the sensitivity of the extreme to the method of prediction are investigated. Traffic evolution with time is also explored. The conclusions are of interest to engineers performing assessment of existing bridges.Key words: bridge, load effects, characteristic values, simulation, traffic flow, Monte Carlo, weigh-in-motion.

Impact of Specialized Hauling Vehicles and Emergency Vehicles on Bridge Load Rating

2021 ◽  
pp. 036119812110145
Author(s):  
Peng Lou ◽  
Chan Yang ◽  
Hani Nassif
Keyword(s):  
Highway Bridges ◽  
Extensive Literature ◽  
Load Rating ◽  
Motion Data ◽  
Weigh In Motion ◽  
Load Factors ◽  
Emergency Vehicles ◽  
Load Effects ◽  
Load Ratings ◽  
Bridge Load Rating

The Federal Highway Administration (FHWA) mandated states to adopt specialized hauling vehicles (SHVs) and emergency vehicles (EVs) in 2013 and 2016, respectively, in the load rating of bridges. Both the AASHTO single-unit trucks (SUs) and EVs are specially configured so that they may result in high load effects and can adversely affect bridge load rating factors. This paper investigates the impacts of rating these vehicles on the states’ bridge load ratings. An extensive literature review of the states’ load rating policies is performed. To determine whether any state can possibly be exempted from the new load ratings for SUs and EVs for Interstate highway bridges, the load effects of various state legal vehicles are analyzed and compared with those of SUs and EVs. The results of the study indicate the inevitability of executing the new load rating analysis for SUs and EVs for the vast majority of states. Weigh-in-motion data are processed to screen the potential EV traffic fleeting on the highway, and the calibrated live load factors are proposed for EVs accordingly. The load effects are found to be smaller than those FHWA originally assigned, improving the rating factors. Lastly, this paper proposes a screening tool to help state agencies to convert the known rating factors to the rating factors of SUs and EVs so that the load rating work can be prioritized for the bridges that are vulnerable to SUs and EVs.

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 Standardised bridge weigh-in-motion data and its applications in bridge engineering

2021 ◽  
Author(s):  
Jami Qvisen ◽  
Weiwei Lin ◽  
Heikki Lilja ◽  
Timo Tirkkonen ◽  
Mikko Peltomaa ◽  
...  
Keyword(s):  
Bridge Engineering ◽  
Traffic Load ◽  
Bridge Design ◽  
Design Assessment ◽  
Motion Data ◽  
Load Models ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion ◽  
Orthotropic Bridge Deck

<p>Applying actual traffic data in bridge analyses will provide more accurate results compared to the results obtained according to the Eurocode traffic load models. Bridge Weigh-in-Motion (B-WIM) measurements are an excellent tool to produce such data. Using B-WIM data as a part of the bridge design or assessment processes has a large potential, but the lack of widely adopted standardised data format hinders broader utilisation of it. This study proposes a new standardised format to present the measured B-WIM data so that in the future, developed software can directly utilise any available B-WIM data. This would make calculations with multiple different traffic compositions and types straightforward and enable the basis for further utilisation of B-WIM data in bridge design/assessment. To demonstrate the benefits, a fatigue case study of an orthotropic bridge deck was conducted, and the results were compared to ones obtained according to Eurocode FLM 4.</p>

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