Unit influence surface identification of long-span bridge based on spatial-temporal vehicle load monitoring

On-side bridge unit influence surface (UIS) calibration traditionally relied on the vehicle load test, which is expensive, time-consuming and traffic-interruptive, especially for long-span bridges. This paper proposes a novel method for bridge UIS identification based on the vehicle load monitoring. By employing a multi-vision system and computer vision algorithms, the distribution of the vehicles on the bridge deck is obtained. Then the data fusion between the vision system and weigh-in-motion (WIM) system is implemented to acquire the spatial-temporal vehicle loads on the deck. In the meanwhile, the deflection of the main-span is also obtained by the SHM system of the bridge. Thus, by means of the iterative computation and surface fitting, the UIS of the deflection is identified. The proposed method is arranged and applied to a practical long-span suspension bridge. Results have shown the feasibility of the method.

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