期刊
JOURNAL OF BUILDING ENGINEERING
卷 25, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jobe.2019.100767
关键词
Masonry infills; Failure mode; Reinforced concrete frames; Seismic performance; Machine learning
资金
- National Science Foundation [1554714]
The failure modes of reinforced concrete frame structures with masonry infill panels have strong implications to their overall seismic performance. This paper explores a data-driven approach to classifying the in-plane failure modes of infill frames by employing machine learning methods. To this end, an experimental database consisting of 114 infill frame specimens is constructed. Six machine learning algorithms are implemented and evaluated for failure-mode classification using nine structural parameters as input variables. The validation results indicate that most of the models are able to achieve more than 80% prediction accuracy, with the highest accuracy of 85.7% achieved by the Adaptive Boosting and Support Vector Machine algorithms.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据