Intelligent cognition of traffic loads on road bridges: From measurement to simulation - A review

Traffic load is a crucial but complicated factor in determining the in-service performance and deterioration behavior of bridges. A better understanding of traffic loads in different traffic densities has become increasingly important in structure health monitoring. As a result, for the traffic load measurement, the relevant technologies had great progress in the past decades. Therefore, we focus on introducing the state-of-the-art approaches most relevant to the traffic load cognition on road bridges, including in-site measurement and data-driven simulation. General principles of the traffic load cognition are firstly presented by reviewing different statistical analysis techniques for determining the spatial-temporal factors of vehicles. Then, this paper reviews various measurement methods carried out for the essential data of traffic loads. The methods are roughly grouped into mechanical, optical and microwave sensor-based methods. Within each category, technical descriptions of the sensor types, properties and applications are discussed in terms of theoretical formulas and feasible scenarios. This paper also implements qualitative and comprehensive comparisons between multiple measurement sensors to show the efficiency of each method or technique. Base on in-site measurement, several kinds of simulation models can be established for traffic loads on mad bridges, including the modelling of single vehicles and the overall traffic flow. Considering the significant contribution of statistics-based deterministic, direct probabilistic methods, and artificial intelligence to traffic load cognition, we carried out the investigation on them in vehicle modelling. For on-bridge traffic flow simulation, three representative microscopic models are reviewed, involving the car-following, hydrodynamic, and cellular automatic models. Overall, this study highlights the application of intelligent cognition methods in identifying and simulating traffic loads on road bridges, potentially providing support for digitalised design, operation, and maintenance.

Automated summary

Traffic load is a crucial factor in determining the performance and deterioration behavior of bridges. This paper introduces state-of-the-art approaches for traffic load cognition on road bridges, including in-site measurement and data-driven simulation. It reviews statistical analysis techniques for determining the spatial-temporal factors of vehicles and various measurement methods for obtaining essential data of traffic loads. The paper also discusses simulation models for traffic loads and reviews microscopic models for on-bridge traffic flow. The study highlights the application of intelligent cognition methods in identifying and simulating traffic loads on road bridges.