Efficient Structural Reconnaissance Surveying for Regional Postseismic Damage Inference with Optimal Inspection Scheduling
Efficient Structural Reconnaissance Surveying for Regional Postseismic Damage Inference with Optimal Inspection Scheduling
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DOI：
10.1061/(asce)em.1943-7889.0002069
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发表时间：
2022-02
影响因子：
3.3
通讯作者：
M. Sheibani;Yinhu Wang;Ge Ou;Nikola Marković
中科院分区：
文献类型：
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作者：
M. Sheibani;Yinhu Wang;Ge Ou;Nikola Marković
准确估计地震后的破坏程度需要劳动密集型的侦察调查，这可能需要几个月的时间来覆盖受影响地区的整个建筑库存。本文提供了一个数据驱动的框架来指导调查小组有效地完成侦察任务，并通过仅检查一小部分建筑物来估计区域损害。首先，通过考虑训练数据中相对较少的代表性建筑物，减少了对整个建筑物库存进行检查的必要性，并且可以在地震后2周内准确估计区域损坏。其次，为了制定一个经济有效的解决方案，优先考虑建筑物和设计有效的检查路线的问题被制定为一个定向问题。利用每个巡检日结束时的稀疏场观测结果对高斯过程回归模型进行再训练，并应用该模型对未巡检建筑进行损伤估计。利用区域地震模拟试验台对该方法进行了验证和评价。
Accurately estimating the extent of damage after an earthquake requires labor-intensive reconnaissance surveys, which may take months to cover the entire building inventory in an impacted region. This paper provides a data-driven framework to guide a survey team efficiently through a reconnaissance mission and estimate regionwide damage by inspecting only a fraction of buildings. First, it is shown that by considering a relatively small set of representative buildings in the training data, the necessity of inspecting the entire building inventory is diminished, and accurate estimation of the regional damage is made possible within 2 weeks after the earthquake. Second, to develop a cost-effective solution, the problem of prioritizing buildings and designing efficient inspection routes is formulated as an orienteering problem. The results of the sparse field observations obtained by the end of each inspection day are used to retrain a Gaussian process regression model, which is applied to estimate damage for the uninspected buildings. A regional earthquake simulation testbed was used to validate and evaluate the performance of the proposed method.