A hybrid virtual-real traffic simulation approach to reproducing the spatiotemporal distribution of bridge loads

Current traffic simulation approaches analyze vehicle loads and load effects from a statistical perspective; however, they fail to reproduce the spatiotemporal distribution of bridge loads and resultant load effects at every moment, hindering real-time bridge health management. This paper proposes a two-step hybrid virtual-real traffic simulation (HvrTS) approach to reproduce the spatiotemporal distribution of bridge loads. Vehicle load sequences are first identified using computer vision based on traffic video from two surveillance cameras installed along the bridge. Next, traffic microsimulation is optimized with the identified vehicle load sequences from the two cameras serving as the known input and validated output. Using a cable-stayed bridge under free-flowing traffic, the HvrTS approach achieved a weighted mean square matching error of <= 0.3 m for the vehicle longitudinal location and a matching error of <= 7% for the vehicle lane position, whereas with congested traffic, the matching errors were much higher due to the inherent complexities and challenges associated with reproducing vehicle behavior in heavily congested situations. The traffic load effects calculated via HvrTS presented excellent spatiotemporal matching with those measured by a structural health monitoring system, especially in free-flow traffic conditions. Applications on a continuous rigid frame girder bridge further validate these findings. Hence, the proposed HvrTS approach can overcome the challenge of spatiotemporal matching between vehicle loads and load effects in field monitoring.