| contributor author | Wang, Zixin | |
| contributor author | Jahanshahi, Mohammad R. | |
| contributor author | Lund, Alana | |
| contributor author | Shahriar, Adnan | |
| contributor author | Montoya, Arturo | |
| date accessioned | 2026-08-20T10:49:10Z | |
| date available | 2026-08-20T10:49:10Z | |
| date copyright | 2025/10/07 | |
| date issued | 2025 | |
| identifier other | JENMDT.EMENG-8325.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311302 | |
| description abstract | AbstractIn structural health monitoring (SHM), structural damage detection and localization
have made significant advances with deep learning–based methods. While finite element
model (FEMs) have been employed to simulate a variety of damage scenarios for ... | |
| publisher | American Society of Civil Engineers | |
| title | Physics-Informed Machine Learning for Hybrid Digital Twin–Enhanced Damage Detection and Localization | |
| type | Journal Article | |
| journal volume | 151 | |
| journal issue | 12 | |
| journal title | Journal of Engineering Mechanics | |
| identifier doi | 10.1061/JENMDT.EMENG-8325 | |
| journal fristpage | 04025080-1 | |
| journal lastpage | 04025080-19 | |
| page | 19 | |
| tree | Journal of Engineering Mechanics:;2025:;Volume ( 151 ):;issue: 012 | |
| contenttype | Fulltext | |
