This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, global positioning system receivers and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources.

这项研究提出了一种用于近实时地震破坏评估的概率框架，该框架利用有关地震输入和地震结构响应的异构信息源。建立贝叶斯网络来描述在地震破坏评估中起作用的各种随机变量之间的关系，范围从描述地震源（幅度和位置）的那些到描述结构性能（漂移和加速度）的那些，以及相关的损害和损失措施。在一个先验通过使用来自多个数据源（例如，自由场地震台，全球定位系统接收器和结构化加速度计）的信息进行贝叶斯推断，可以更新基于地震源信息的破坏估计。考虑使用桥梁模型来说明该框架的应用，并且通过比较先验和后验统计分布来证明源自传感器数据的不确定性降低。基于前后后方差和相对熵降低的概念，使用两种方法来量化来自观测值的信息附加值。结果揭示了各种信息来源对响应评估的有效性，