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Full-scale identification of the wave forces exerted on a floating bridge using inverse methods and directional wave spectrum estimation

机译:使用反演方法和定向波谱估计全面识别作用在浮桥上的波浪力

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The dynamic behaviour of long-span bridges is governed by stochastic loads from typically ambient excitation sources. In real life, these loads cannot be measured directly at full scale. However, inverse methods can be utilised to identify these unknown forces using response measurements together with a numerical model of the relevant structure. This paper presents a case study of full-scale identification of the wave forces on the Bgsoysund bridge, a long-span pontoon bridge that has been monitored since 2013. First, a numerical model of the structure is formed, resulting in a reduced-order state-space model that takes into account the frequency-dependent hydrodynamic mass and damping from the fluid, based on fitting of rational transfer functions. Using acceleration data of the structure measured during several events of moderate and strong seas, the wave forces are identified using stochastic-deterministic methods for combined state and input estimation. In addition, a separate frequency-domain assessment of the wave forces is performed, in which the spectral density of the first-order wave forces is constructed from an estimated directional wave field model driven by wave elevation data. When compared in the frequency domain, the force estimates from the two approaches are of comparable magnitude. However, uncertainties in the assumptions and models behind the force estimates from the two approaches still play a significant role. (C) 2018 Elsevier Ltd. All rights reserved.
机译:大跨度桥梁的动态行为受通常来自环境激励源的随机载荷支配。在现实生活中,这些负载无法直接在满量程下进行测量。但是,可以使用反演方法使用响应测量值以及相关结构的数值模型来识别这些未知力。本文以案例研究为例,对自2013年以来一直受到监视的大跨度浮桥Bgsoysund桥上的波浪力进行全面识别。首先,形成了结构的数值模型,从而降低了阶次基于有理传递函数拟合的状态空间模型,该模型考虑了频率相关的流体动力质量和流体阻尼。利用在几次中度和强海事件中测得的结构加速度数据,使用随机确定性方法(结合状态和输入估计)识别波浪力。此外,对波力进行了单独的频域评估,其中一阶波力的频谱密度是根据由波高数据驱动的估计方向波场模型构建的。在频域中进行比较时,两种方法得出的力估算值具有可比性。但是,这两种方法得出的力估计背后的假设和模型的不确定性仍然发挥着重要作用。 (C)2018 Elsevier Ltd.保留所有权利。

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