Site-specific vehicle load models based on WIM data for long span bridges assessment

2014 ◽  
pp. 1035-1040
Author(s):  
R Zhu ◽  
X Shi ◽  
X Ruan
Keyword(s):  
Site Specific ◽  
Long Span Bridges ◽  
Load Models ◽  
Long Span ◽  
Vehicle Load

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

Research on Design Vehicle Load for Long-Span Bridges

2011 ◽  
Vol 90-93 ◽  
pp. 909-914
Author(s):  
Da Lin Hu ◽  
Ding Ding ◽  
Long Gang Chen ◽  
Chun Mei Xia
Keyword(s):  
Simulation Analysis ◽  
Extreme Value Distribution ◽  
Bending Moments ◽  
Long Span Bridges ◽  
Long Span ◽  
Vehicle Load ◽  
Long Span Bridge ◽  
Load Effects ◽  
Vehicle Loads ◽  
Characteristic Values

This paper presents simulation analysis of load effects of bridges under random fleet. Based on actual data of vehicle loads on Guangzhou-Shenzhen Expressway and relevant statistical results, mid-span bending moments of long-span virtual simple-supported beams are calculated. Then probability distribution of the bending moments and extreme value distribution of the load effects within design reference period are obtained. Finally, characteristic values of mid-span bending moments and recommended values of design lane load are calculated, sequentially. The results studied in this paper can be as a reference for long-span bridge design, and also can be a reference for overloading control or weight charge policy.

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.

Weather Forecasting Technology Applied to Structures Improves Resiliency

2019 ◽  
Author(s):  
Ron J. Chapman ◽  
Zach J. Taylor
Keyword(s):  
Weather Forecasting ◽  
Weather Forecast ◽  
Complex Structures ◽  
Ice Accretion ◽  
Storm Events ◽  
Site Specific ◽  
Long Span Bridges ◽  
Weather Forecasts ◽  
Long Span ◽  
Historical Database

<p>Designers are creating taller and more complex structures in the urban built environment. These complex structures include long span bridges, buildings and monuments all of which can push the limits of design and engineering processes. These structures are challenging to construct, operate, and rehabilitate during their life-cycle. Analytics used during the design phase can be combined with weather forecasting technology to provide an advanced site and structure-specific weather forecast. This site and structure-specific weather forecast helps to ensure efficient, safe construction, and maximizes operation of the structural asset. Examples discussed include forecasting of wind conditions for construction/maintenance activities on bridges, prediction of falling snow/ice accretion from cable stay bridges/buildings and prevention of high-sided vehicle blow over on bridges. Analysis of weather forecasting data combined with a historical database of site-specific weather monitoring provides knowledge about deviations from the normal climate. This analysis can provide advanced storm warning thereby mitigating potential damages. The ability to provide site-specific and structure-specific weather forecasts is increasingly important because of the increased intensity and frequency of storm events due to climate change.</p>

Vehicle Load Spectrum Simulation of Long-Span Bridges

2015 ◽  
Vol 648 ◽  
pp. 35-44 ◽  
Author(s):  
Ting Xia Xu ◽  
Yan Li ◽  
Zhi Wen Wu
Keyword(s):  
Traffic Load ◽  
Load Spectrum ◽  
Characteristic Parameters ◽  
Time Duration ◽  
Long Span Bridges ◽  
Vehicle Type ◽  
Long Span ◽  
Vehicle Load ◽  
Spectrum Simulation ◽  
Load Simulation

Based on the requirement for fatigue damage evaluation of long-span bridges, and existing research literatures, the vehicle type, vehicle weight and time duration are taken as characteristic parameters of random vehicle load. Taking the Second Nanjing Yangtze River Bridge as an example, the characteristics of traffic load are obtained by traffic investigation and the data from existing research results. In the paper the method and process for vehicle load spectrum simulation which includes data acquisition of random traffic load, statistical analysis and Monte Carlo simulation is proposed. The simulation program of random traffic flow is verified using the measured data. The proposed method can provide a reference for random traffic load simulation.

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