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.

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.

Probabilistic evaluation of maximum dynamic traffic load effects on cable-supported bridges under actual heavy traffic loads

2020 ◽  
pp. 1748006X2093849
Author(s):  
Yang Liu ◽  
Qinyong Wang ◽  
Naiwei Lu
Keyword(s):  
Heavy Traffic ◽  
Suspension Bridge ◽  
Traffic Monitoring ◽  
Traffic Load ◽  
Dynamic Traffic ◽  
Type Distribution ◽  
Cable Stayed Bridge ◽  
Weigh In Motion ◽  
Load Effects ◽  
Motion Measurements

The traffic load has grown significantly in recent years, which might be a threat for the service safety of existing bridges. Thus, it is an urgent task to assess the actual traffic load effects on bridges, considering actual heavy traffic load instead of design traffic load. This study presents a framework for extrapolating maximum dynamic traffic load effects on large bridges using site-specific traffic monitoring data. The framework involves vehicle–bridge interaction analysis and probabilistic modelling of extreme values. The weigh-in-motion measurements of a busy highway in China were collected for stochastic traffic load modelling. Case studies of two long-span cable-supported bridge based on the weigh-in-motion measurements were conducted to demonstrate the effectiveness of the proposed framework. It is demonstrated that Rice’s level-crossing approach can capture both dynamic and probabilistic characteristics of the traffic load effects. The root-mean-square displacement of the cable-stayed bridge follows a C-type distribution, and the one for the suspension bridge follows an M-type distribution, which is associated with the first-order mode shapes of the two types of bridges. The amplification factors for the cable-stayed bridge and the suspension bridge are 5.9% and 3.6%, respectively. The numerical analysis indicates that the dynamic effect for extrapolation is weaker with the increase in bridge span length, but the effect of traffic volume growth will be more significant.

Research on the general method for extrapolating traffic load effects for highway bridges

Stahlbau ◽  
2014 ◽  
Vol 83 (3) ◽  
pp. 186-198 ◽  
Author(s):  
Weizhen Chen ◽  
Zhenlin Xie ◽  
Bochong Yan
Keyword(s):  
Highway Bridges ◽  
Traffic Load ◽  
Load Effects ◽  
General Method

Traffic loads for the assessment of existing bridges

2021 ◽  
Author(s):  
Vazul Boros ◽  
Roman Lenner ◽  
Alan O'Connor ◽  
Andre Orcesi ◽  
Franziska Schmidt ◽  
...  
Keyword(s):  
Road Traffic ◽  
Traffic Load ◽  
Traffic Loading ◽  
Traffic Loads ◽  
Universal Solution ◽  
Load Effects ◽  
Characteristic Values ◽  
Existing Bridges ◽  
Partial Factor

<p>IABSE TG 1.3 aims to identify appropriate approaches for applications of the partial factor format in assessment of existing bridges. A sub-group was formed to investigate and provide recommendations on updating road traffic loads. Commonly, these are assessed by complex numerical simulations. While this study does not provide a universal solution, it demonstrates by a case study a simple and reasonably conservative way of using simulations to update traffic load effects, meanwhile continuously highlighting the objectives, potential alternatives or pitfalls of simulations. The results indicate that, for the short, single span bridge under consideration, the characteristic values given in Eurocodes provide conservative estimates. The probabilistic model for traffic loading obtained by bridge- and route-specific simulations will yield substantially more favourable reliability levels in comparison to the general model in fib Bulletin 80.</p>

Development of a traffic load model for bridge structures within an urban subway system

2021 ◽  
Author(s):  
Thibault Tepho ◽  
Marcel Nowak ◽  
Oliver Fischer ◽  
Philipp Tamm ◽  
Markus Schöning
Keyword(s):  
Scaling Factor ◽  
Traffic Load ◽  
Load Model ◽  
Extreme Load ◽  
Bridge Structures ◽  
Subway System ◽  
Engineering Standards ◽  
Load Effects ◽  
Factor Α

<p>Subway systems are a key component of today’s urban infrastructure. However, current engineering standards such as the Eurocode only give few indications regarding the modelling of traffic loads for subway trains. For this reason, a traffic load model is developed based on load model LM71 from Eurocode 1 - Part 2, the standard load model for rail bridges. The aim is to scale this load model so that it is able to cover the extreme load effects resulting from actual subway trains within the subway network. Within the scope of these investigations, different structural systems and load effects are studied, as well as different strategies for modifying LM71 and its scaling factor α. Additionally, the results obtained for simplified beam systems are validated on real structures to account for effects like two-dimensional bearing capacity for the determination of the scaling factor α. These investigations are performed for the subway system of the city of Munich.</p>

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.

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