Instrumented Vehicle-Aided Simultaneous Bayesian inference for Bridge Influence Line and Unknown Traffic loading

Traditional methods for Bridge Influence Line (BIL) identification generally necessitate traffic interruption to achieve reliable results, significantly restricting their practical applicability. Under uninterrupted traffic conditions, bridge responses exhibiting substantial randomness due to unknown traffic loading, significantly complicating parameter identification. To address this issue, this study proposes an instrumented-vehicle-aided detection framework. An instrumented vehicle with known weight and speed traverses the bridge, while computer version technology tracks the positions of all vehicles. And a new physical model is developed to indicate the relationship of the measured responses, BIL, vehicle positions, known weights of instrumented vehicles, unknown BIL and interfering vehicles weights (IVWs). The identification of unknown BIL and IVWs is formulated as a multi-parameter inverse optimization problem and addressed within a Bayesian inference framework. The physical model is firstly embedded into the Bayesian framework by deriving the posterior distribution of the unknown parameters. After optimal conditional function of BIL is derived and solution set of IVWs is defined, a coupled iterative optimization algorithm, optimizing different groups of parameters iteratively, is then designed to simultaneously estimate the BIL and IVWs. At last, the uncertainty is quantified through a derived Hessian matrix. Numerical simulations confirm that the proposed method accurately identifies both BIL and IVWs under stochastic traffic flows. Additionally, two field tests validate the effectiveness and practicality of the approach under uninterrupted traffic conditions.