Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.

快速准确地识别潜在的结构缺陷是评估一个地区大型建筑存量的地震脆弱性的关键任务。对于多层结构，已知楼层刚度的垂直垂直变化会显着增加中度或严重地震时倒塌的可能性。因此，识别和改造具有此类不规则性的建筑物（通常称为软层建筑物）对于防震和减灾工作至关重要。通过常规方式进行软高层建筑识别是一项劳动密集且耗时的过程。在这项研究中，基于深度学习技术设计了一种自动程序，用于从区域规模的街景图像中识别软楼房。为了开发深度学习模型，开发了一个数据库，该数据库包含大量建筑物图像和可自动注释新数据库条目的半自动图像标记方法。进行了广泛的计算实验，以检验所提出程序的有效性，并获得对自动软层建筑物识别的认识。