Damage identification based on the inner product matrix and parallel convolution neural network for frame structure
Yingying He, Ji Feng, Baogang Sun, Feixue Wang, Likai Zhang, Jidi Jiang · 2024
Read Damage identification based on the inner product matrix and parallel convolution neural network for frame structure by Yingying He, Ji Feng, Baogang Sun, Feixue Wang, Likai Zhang, Jidi Jiang (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11655645.txt.
Description
Yingying He and co-authors detect frame structure damage with a combined neural method in a 2024 study. Vibration based monitoring is widely used to find structural damage. Standard learning models often take a single domain input, which limits the information for accurate calls. Their method pairs an inner product matrix with a parallel convolutional network to draw features from several domains at once.
One branch handles time series data and a second handles modal data, joined by fully connected layers. A five story steel frame model served as the test object, against five comparison methods under the same conditions. The combined model extracted signal features on its own and identified damage with high accuracy.
Catalog record
| CREATOR | Yingying He, Ji Feng, Baogang Sun, Feixue Wang, Likai Zhang, Jidi Jiang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |