Abstract
This study carries out numerical simulations to identify the magnitude of prestress force in a highway bridge by making use of the dynamic responses from moving vehicular loads. The prestressed bridges are modeled using four-node isoparametric flat shell element taking into account the transverse shearing deformation in the finite element model. The vehicle is modeled as a multiple degrees-of-freedom system. An approach based on dynamic response sensitivity-based finite element model updating is proposed to identify the elemental prestress force. The identified results are obtained iteratively with the penalty function method with regularization from the measured structural dynamic responses. A single-span prestressed Tee beam and two-span prestressed box-girder bridge are studied as two numerical examples. The effects of road surface roughness, measurement noise, and speed of moving vehicle on the identification results are investigated. Studies indicate that the proposed method is efficient and robust for prestress force identification. Good identified results can be obtained from several measured acceleration responses.
Highlights
There are many researches in the literature which deal with the bridge vibration caused by the passing of vehicles or trains [1,2,3,4,5,6,7,8,9,10,11]
The randomness of the road surface roughness of the bridge can be represented with a periodic modulated random process. It is specified by its power spectral density function (PSD) as [23]
We make use of the noise-free acceleration response of the bridge induced by the vehicle passing on top of the bridge at a speed of 10 m/s to identify the prestress force with different road surface roughness
Summary
There are many researches in the literature which deal with the bridge vibration caused by the passing of vehicles or trains [1,2,3,4,5,6,7,8,9,10,11]. Wang et al [9] studied the nonlinear dynamic response of a long-span suspension bridge under running train and turbulent wind These researches can be classified into the following categories: models of the vehicle, road surface roughness, bridge-vehicle interaction, the effect of vehicle braking, and so forth. In these studies, the parameters of the bridge and the vehicles are known and the dynamic responses of the bridge and/or the vehicle-bridge system can be obtained from forward analysis. Two numerical examples are studied to illustrate the correctness of the proposed method Some aspects such as the effects of road surface roughness, measurement noise, and speed of the moving vehicle on the identification results are investigated. Simulation studies indicate that the proposed method is efficient and robust, and good identified results can be obtained
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