Post‐earthquake reconnaissance survey of structural damage is an effective way of documenting and understanding the impact of earthquakes on structures. This article aims at providing an efficient data‐based framework that reduces the required time for reconnaissance missions and predicts the damage intensities for every building in the affected region. We hypothesize that a joint selection of necessary structural and earthquake parameters along with sparse damage observations are sufficient to train a supervised learning algorithm and accurately infer the damage for other buildings in the region. Gaussian process regression is employed to prove the hypothesis for probabilistic inference of different damage indices. The algorithm performs efficiently by selecting a set of diverse and representative buildings for damage observations using K‐medoids clustering. To validate the hypothesis and the proposed method, the algorithm framework is implemented on two severe earthquake simulation testbeds. The impacts of different building and ground motion variables on the damage inference performance are discussed. Furthermore, the effectiveness of observation sampling by clustering in the post‐earthquake damage inference is compared with random sampling.

中文翻译：

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高斯过程回归的发展，以有效地进行区域地震后建筑物破坏推断

地震后对结构破坏的勘测是记录和理解地震对结构影响的有效方法。本文旨在提供一个有效的基于数据的框架，以减少侦察任务所需的时间，并预测受影响区域中每栋建筑物的破坏强度。我们假设联合选择必要的结构和地震参数以及稀疏的损坏观测值足以训练监督学习算法并准确推断该地区其他建筑物的损坏情况。采用高斯过程回归来证明不同损伤指数的概率推断假设。该算法通过使用K-medoids聚类选择一组不同且具有代表性的建筑物进行破坏观测，从而有效地执行了任务。为了验证该假设和所提出的方法，该算法框架在两个严重地震模拟试验台上实现。讨论了不同建筑物和地面运动变量对破坏推断性能的影响。此外，将在地震后破坏推断中通过聚类进行观察抽样的有效性与随机抽样进行了比较。