North American and British Long‐Span Bridge Loads

Buckland, Peter G.

doi:10.1061/(asce)0733-9445(1991)117:10(2972)

Cited by 15 publications

(3 citation statements)

References 4 publications

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“…The current load model by the American Association of State Highway and Transportation Officials (AASHTO) [34] may be considered to apply to "ordinary bridges" with spans up to 152 m [35,36], although, in the calibration of its current traffic load model, the maximum span considered is 60 m [37]. The AASHTO load model is lighter than that prescribed in the Eurocode [8].…”

Section: Codes Of Practice and Previous Research On Long-span Bridge mentioning

confidence: 99%

Micro-simulation of single-lane traffic to identify critical loading conditions for long-span bridges

O’Brien

Lipari

Caprani

2015

Engineering Structures

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Section: Codes Of Practice and Previous Research On Long-span Bridge mentioning

confidence: 99%

Micro-simulation of single-lane traffic to identify critical loading conditions for long-span bridges

O’Brien

Lipari

Caprani

2015

Engineering Structures

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“…The aim of much traffic loading research is to propose an appropriate model for long-span bridges. Given the problems with collecting congested traffic data, most work to date is based on available free-flowing data [10][11][12][13][14][15][16]. Many researchers create congestion by 'collapsing' free-flowing traffic, i.e., by reducing the recorded inter-vehicle gaps (axle-to-axle or bumper-to-bumper) to minimum values [5,[15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge

et al. 2020

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This paper examines the nature of traffic loading in recurrent congested traffic conditions on a long-span suspension bridge. Traffic flow and percentage of trucks are extracted from image data and a cluster analysis performed to classify the data into four clusters. One cluster (MTHF, medium truck percentage and high flow) is identified that incorporates almost 50% of the hours of traffic data scattered throughout the day. Site-specific load assessment confirms that this MTHF cluster is the most critical for the bridge considered, the Forth Road Bridge in Scotland. For non-recurrent congestion, another cluster (HTLF, high percentage of trucks and low flow) is shown to govern but this finding is highly site-specific, depending on the relative frequency of the different types of congestion. A comparison of the maximum hourly/daily MTHF load effect of the cable force for five notional bridges shows that a 100% increase in the bridge span generates an increase of about 65% in the characteristic load effect.

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“…In recent years, a number of studies have investigated types of congestion (Caprani, OBrien, & Lipari, 2016;Krauß, 1998) as well as driver behaviour under different traffic conditions (Aghabayk, Sarvi, & Young, 2012;Al-Kaisy, Jung, & Rakha, 2005;Ferrari, 1989;Sarvi, 2013;Yoo & Green, 2009). Other studies have been published on traffic load for long-span bridges (Bailey, 1996;Bruls, Croce, De Falena, & Sedlacek, 1996;Buckland, 1991;Buckland, McBryde, Navin, & Zidek, 1978;Ditlevsen, 1994;Hayrapetova, O'Connor, & OBrien, 2012;OBrien, Hayrapetova, & Walsh, 2012). Treiber, Hennecke, & Helbing (2000) propose microsimulation for the modelling of congested traffic.…”

Section: Introductionmentioning

confidence: 99%

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

Micu

O’Brien

Malekjafarian

et al. 2018

BJRBE

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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 (freeflowing) 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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North American and British Long‐Span Bridge Loads

Cited by 15 publications

References 4 publications

Micro-simulation of single-lane traffic to identify critical loading conditions for long-span bridges

Micro-simulation of single-lane traffic to identify critical loading conditions for long-span bridges

Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge

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

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