Advancing post-earthquake structural evaluations via sequential regression-based predictive mean matching for enhanced forecasting in the context of missing data
Advancing post-earthquake structural evaluations via sequential regression-based predictive mean matching for enhanced forecasting in the context of missing data
复制标题
通过基于序贯回归的预测均值匹配推进震后结构评估，以增强缺失数据背景下的预测
DOI：
10.1016/j.aei.2020.101202
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发表时间：
2021
影响因子：
8.8
通讯作者：
Paal, Stephanie German
中科院分区：
文献类型：
--
作者：
Luo, Huan;Paal, Stephanie German
地震后，每一座受损建筑都需要得到适当评估，以确定其承受余震的能力，并评估居住者返回的安全性。这些评估具有时间敏感性，因为评估完成得越快，灾害造成的生命和金钱损失就越少。在这个方向上，通常没有足够的时间或资源来获取关于结构的所有信息以进行高级结构分析。地震后的破坏调查数据可能是不完整的，并包含缺失值，这会延迟分析过程，甚至使结构评估变得不可能。本文提出了一种新的多重插补（MI）方法来解决缺失数据的问题，填补在每个缺失值与多个现实的，有效的候选人，占缺失数据的不确定性。所提出的方法，称为序贯回归为基础的预测均值匹配（SRB-PMM），利用贝叶斯参数估计连续推断模型参数的变量与缺失值，条件的基础上充分观察和插补变量。给定模型参数，一个混合的方法集成PMM与交叉验证算法，以获得最合理的插补数据集。两个例子进行了验证的有效性的SRB-PMM方法的基础上的数据库，包括262钢筋混凝土（RC）柱试件进行地震荷载。从这两个例子的结果表明，建议SRB-PMM方法是一种有效的手段来处理地震后结构评估中突出的数据缺失问题。
After an earthquake, every damaged building needs to be properly evaluated in order to determine its capacity to withstand aftershocks as well as to assess safety for occupants to return. These evaluations are time-sensitive as the quicker they are completed, the less costly the disaster will be in terms of lives and dollars lost. In this direction, there is often not sufficient time or resources to acquire all information regarding the structure to do a high-level structural analysis. The post-earthquake damage survey data may be incomplete and contain missing values, which delays the analytical procedure or even makes structural evaluation impossible. This paper proposes a novel multiple imputation (MI) approach to address the missing data problem by filling in each missing value with multiple realistic, valid candidates, accounting for the uncertainty of missing data. The proposed method, called sequential regression-based predictive mean matching (SRB-PMM), utilizes Bayesian parameter estimation to consecutively infer the model parameters for variables with missing values, conditional based on the fully observed and imputed variables. Given the model parameters, a hybrid approach integrating PMM with a cross-validation algorithm is developed to obtain the most plausible imputed data set. Two examples are carried out to validate the usefulness of the SRB-PMM approach based on a database including 262 reinforced concrete (RC) column specimens subjected to earthquake loads. The results from both examples suggest that the proposed SRB-PMM approach is an effective means to handle missing data problems prominent in post-earthquake structural evaluations.
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影响因子：
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作者：
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通讯作者：
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DOI：
--
发表时间：
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期刊：
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影响因子：
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作者：
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通讯作者：
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