期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Rapid visual screening of soft-story buildings from street view images using deep learning classification 被引量:2
1
作者 Qian Yu Chaofeng Wang +4 位作者 Frank McKenna Stella XYu Ertugrul Taciroglu barbaros cetiner Kincho HLaw 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2020年第4期827-838,共12页
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 ver... 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. 展开更多
关键词 soft-story building deep learning CNN rapid visual screening street view image
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部