Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle's speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.

Department of Bioprocess Engineering School of Chemical and Energy Engineering Faculty of Engineering Universiti Teknologi Malaysia Skudai Johor 81310 Malaysia

Department of Civil Engineering Islamic Azad University Tabriz 5157944533 Iran

Institute for Nanomaterials Advanced Technologies and Innovation Studentska 2 461 17 Liberec Czech Republic

Institute of Noise and Vibration Universiti Teknologi Malaysia City Campus Jalan Semarak Kuala Lumpur 54100 Malaysia

School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia Skudai Johor 81310 Malaysia

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A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques