Reliable estimation of wind‐induced displacement responses of long‐span bridges is critical to evaluating their wind‐resistant performance. In this study, two Bayesian approaches, Bayesian generalized linear model (BGLM) and sparse Bayesian learning (SBL), are proposed for characterizing the wind‐induced lateral displacement responses of long‐span bridges with structural health monitoring (SHM) data. They are fully model‐free data‐driven approaches, preferable for reckoning the wind‐induced total displacement intended for wind‐resistant performance assessment. With the measured displacement responses and wind speeds, a BGLM is developed to characterize the nonlinear relationship between the total displacement response and wind speed, where the Bayesian model class selection (BMCS) criterion is incorporated to determine the optimal model. In the model formulation by SBL, both wind speed and wind direction are treated as explanatory variables to elicit a probabilistic model with sparse structure. The SBL cleverly makes the resulting model to exempt from overfitting and generalizes well on unseen data. The two formulated models are then utilized to forecast the wind‐induced displacement responses in extreme typhoon events beyond the monitoring scope, and the predicted displacement responses are contrasted to the finite element analysis results and the design maximum allowable displacement under the serviceability limit state (SLS). The proposed methods are demonstrated using the monitoring data acquired by GPS sensors and anemometers instrumented on a long‐span suspension bridge. The results show that the SBL model is superior to the BGLM for wind‐induced displacement response prediction and is amenable to SHM‐based evaluation of wind‐resistant performance under extreme typhoon conditions.

中文翻译：

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利用结构健康监测数据评估大跨度桥梁抗风性能的贝叶斯方法

可靠地估计大跨度桥梁的风致位移响应对于评估其抗风性能至关重要。在这项研究中，提出了两种贝叶斯方法，即贝叶斯广义线性模型（BGLM）和稀疏贝叶斯学习（SBL），以利用结构健康监测（SHM）数据表征大跨度桥梁的风致侧向位移响应。它们是完全不受模型驱动的数据驱动方法，对于计算用于抗风性能评估的风致总位移而言更可取。利用测得的位移响应和风速，开发了BGLM来表征总位移响应与风速之间的非线性关系，其中结合了贝叶斯模型类别选择（BMCS）标准来确定最佳模型。在SBL建立的模型中，将风速和风向都视为解释变量，以得出具有稀疏结构的概率模型。SBL巧妙地使生成的模型免于过度拟合，并很好地概括了看不见的数据。然后使用这两个公式化模型来预测超出监测范围的极端台风事件中的风致位移响应，并将预测的位移响应与有限元分析结果和使用寿命极限状态（SLS）下的设计最大允许位移进行对比。 ）。利用大跨度悬索桥上的GPS传感器和风速计获取的监测数据，证明了所建议的方法。