| contributor author | Hacıefendioğlu, Kemal | |
| contributor author | Mostofi, Fatemeh | |
| contributor author | Aslan, Tunahan | |
| contributor author | Toğan, Vedat | |
| date accessioned | 2026-08-20T21:27:29Z | |
| date available | 2026-08-20T21:27:29Z | |
| date copyright | 2026/01/29 | |
| date issued | 2026 | |
| identifier other | JCCEE5.CPENG-7116.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314485 | |
| description abstract | AbstractThis study introduces a novel unsupervised machine learning approach for structural
health monitoring (SHM), employing a dual-domain deep convolutional variational autoencoder
(DD-CVAE). Conventional SHM techniques often require expensive sensor ... | |
| publisher | American Society of Civil Engineers | |
| title | A Dual-Domain Deep Convolutional Variational Autoencoder Framework for Unsupervised Structural Health Monitoring Using Vision-Based Vibration Analysis | |
| type | Journal Article | |
| journal volume | 40 | |
| journal issue | 3 | |
| journal title | Journal of Computing in Civil Engineering | |
| identifier doi | 10.1061/JCCEE5.CPENG-7116 | |
| journal fristpage | 04026010-1 | |
| journal lastpage | 04026010-15 | |
| page | 15 | |
| tree | Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003 | |
| contenttype | Fulltext | |