Intelligent cognition of traffic loads on road bridges: From measurement to simulation – A review. (15th August 2022) - Zheng, Jiayan; Tang, Junyi; Zhou, Zhixiang; Heng, Junlin; Chu, Xi; Wu, Tong
Highlights: An in-depth overview was made on the application of traditional vehicle detector and machine vision. Various kinds of simulation models for traffics on road bridges were summarised and compared. The potentials of intelligent cognition in supporting design, operation, and maintenance were discussed. Comparison of various frontier load cognition methods offers reference to academia and professionals. Abstract: 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 road 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.