Contenido Bridge Traffic Loading: From Research to Practice

There is considerable uncertainty about what level of traffic loading bridges should be designed for. Codes specify notional load models, generally to represent extreme levels of normal traffic, but these are often crude and have inconsistent levels of safety for different load effects. Over the past few decades, increasing quantities of reliable truck weight data has become available and it is now possible to calculate appropriate levels of bridge traffic loading, both for specific bridges and for a road network.

Bridge Traffic Loading brings together experts from all over the world to deliver not just the state-of-the-art of vertical loading, but also to provide recommendations of best-practice for all the major challenges in the field – short-span, single and multi-lane bridge loading, dynamic allowance and long-span bridges. It reviews issues that continue to be debated, such as which statistical distribution is most appropriate, whether free-flowing or congested traffic governs and dealing with future traffic growth. Specialist consultants and bridge owners should find this invaluable, as will regulators.

Contents
Preface
Acknowledgements
Editors
Contributors

1 Introduction
ANDRZEJ NOWAK, SYLWIA STAWSKA, AND JACEK CHMIELEWSKI

1.1 Introductory remarks
1.2 Design traffic load
1.3 Statistical background to bridge loading

2 Vehicles and gross vehicle weight
ANDRZEJ NOWAK, JACEK CHMIELEWSKI, AND SYLWIA STAWSKA

2.1 Categories of vehicle
2.2 Truck weight data
2.3 Quality Control of traffic data
2.4 Variations in WIM data
2.5 Bridge load effects
2.6 Fatigue damage
2.7 Legal limits on vehicle loads
2.8 Traffic load factors
2.9 Limit states and reliability index
2.10 Extrapolation of imposed traffic load effects
2.11 Traffic load factors

3 Short-to-medium span bridges
COLIN CAPRANI AND ROMAN LENNER

3.1 Introduction
   3.1.1 The physical and statistical phenomenon
   3.1.2 Load modeling approaches
   3.1.3 WIM-based load modeling
3.2 Traffic data
   3.2.1 WIM data and recordings
   3.2.2 WIM data filtering and cleaning
   3.2.3 Measurement duration and extent
   3.2.4 Overloaded and permit vehicles
3.3 Loading events
   3.3.1 Direct use of measured WIM data
   3.3.2 Generation of artificial traffic streams
      3.3.2.1 Monte Carlo simulation
      3.3.2.2 Modeling vehicles
      3.3.2.3 Generating gaps
   3.3.3 Traffic loading in multiple lanes
      3.3.3.1 Opposing-direction traffic
      3.3.3.2 Same-direction traffic
      3.3.3.3 Scenario modeling
3.4 Load effects
   3.4.1 Influence lines
   3.4.2 Influence surfaces
   3.4.3 Load movement
3.5 Dynamic interaction
3.6 Statistical prediction
   3.6.1 Prediction of extremes
   3.6.2 Composite Distribution Statistics
   3.6.3 The governing form of traffic
3.7 Notional Load Models
   3.7.1 General method
   3.7.2 Specific considerations
      3.7.2.1 Design versus assessment
      3.7.2.2 Load model consistency
      3.7.2.3 Multi-lane factors
3.8 Recommendations

4 Dynamic load allowance
JENNIFER KEENAHAN, EUGENE OBRIEN, ALEŠ ŽNIDARI?, AND JAN KALIN

4.1 Introduction
   4.1.1 The phenomenon
   4.1.2 Basic definitions
   4.1.3 Factors influencing dynamic amplifications
4.2 Codes
   4.2.1 AASHTO
   4.2.2 Eurocode
   4.2.3 Australian Standard
   4.2.4 Chinese Standard
4.3 Statistical approach to dynamics
   4.3.1 Assessment Dynamic Ratio
   4.3.2 The shift in probability paper plots due to dynamics
   4.3.3 The contribution of surface roughness to dynamics
4.4 Field measurements
   4.4.1 History of using Bridge Weigh-in-Motion to estimate dynamic amplification
   4.4.2 DAF results inferred from Bridge Weigh-in-Motion
      4.4.2.1 Examples of DAF calculation
     4.4.2.2 Decrease in DAF with increasing GVW
4.5 Conclusions

5 Long-span bridge loading
COLIN CAPRANI, MICHAEL QUILLIGAN, AND XIN RUAN

5.1 Introduction
5.2 Load models for long-span bridges
   5.2.1 Development of North American load models
      5.2.1.1 Ivy et al. (1954)
      5.2.1.2 Lions Gate simulation studies
      5.2.1.3 Current AASHTO load model
  5.2.2 Development of United Kingdom load models
  5.2.3 Development of other European load models
     5.2.3.1 Eurocode
     5.2.3.2 Storebælt East Bridge, Denmark
5.2.4 Asian long-span load models
     5.2.4.1 China
     5.2.4.2 Japan
     5.2.4.3 Korea
5.3 Modeling long-span traffic loading
   5.3.1 Traditional approaches
   5.3.2 Traffic microsimulation
   5.3.3 Lane changing and types of congested traffic
   5.3.4 Extreme traffic load effects
   5.3.5 Conclusions and future directions
      5.3.5.1 Inter-vehicle gap data and mix of vehicle types
      5.3.5.2 Load effect calculation
      5.3.5.3 Influence of truck percentage
      5.3.5.4 Future developments
5.4 Case studies
   5.4.1 Model and traffic basis
   5.4.2 Two-pylon cable-stayed bridge
   5.4.3 Bridges with multiple pylons
      5.4.3.1 Cable-stayed bridge
      5.4.3.2 Suspension bridge
5.5 Conclusions

6 Factors affecting the accuracy of characteristic maximum load effects
EUGENE OBRIEN, DONYA HAJIALIZADEH, BERNARD ENRIGHT, AND CATHAL LEAHY

6.1 Introduction
6.2 Choice of extrapolation method
  6.2.2 Extreme Value theory methods
     6.2.2.1 Peaks-Over-Threshold
     6.2.2.2 Block Maximum
     6.2.2.3 Box-Cox-GEV distribution
  6.2.3 Tail fitting
     6.2.3.1 Castillo’s approach
     6.2.3.2 Normal distribution
     6.2.3.3 Rice’s formula
  6.2.4 Distribution of the prediction
     6.2.4.1 Bayesian Updating
     6.2.4.2 Predictive Likelihood
  6.2.5 Comparative study of extrapolation methods
     6.2.5.1 Simple Extreme Value problem
     6.2.5.2 Traffic load effect problem
  6.2.6 Extrapolation recommendations
6.3 The nature of extreme traffic loading events
   6.3.1 Correlations in same-direction multi-lane traffic
      6.3.1.1 Simulating trains of truck traffic
   6.3.2 Consistency of safety in notional load models
      6.3.2.1 HL-93 – Single-lane bridges
6.4 Separating standard from non-standard trucks
   6.4.1 Removing apparent non-standard vehicles from WIM data
   6.4.2 Modeling non-standard vehicles
6.5 Allowing for growth in vehicle weights and frequencies
   6.5.1 Illustration of non-stationary extremes
   6.5.2 Influence of growth on bridge traffic load effects
   6.5.3 Recommendations for considering traffic growth
6.6 Traffic loading on secondary road bridges
   6.6.1 Sources of extreme vehicles
      6.6.1.1 Standard vehicles
      6.6.1.2 Non-standard vehicles
   6.6.2 Bayesian Updating and the ‘megasite’ concept
      6.6.2.1 Bayesian Updating and Kernel Density Estimation
      6.6.2.2 The megasite data
      6.6.2.3 Updating the characteristic maximum GVW for the Maryland site
6.7 Discussion

References
Index