.png){kind=icon}
返回
Damage localization using a deep learning-based response modeling method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compstruc.2025.107697.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Existing multi-damage localization methods usually need to be trained using labeled data obtained from various damage cases, and such methods can identify multiple damages with high accuracy. However, it's extremely challenging to obtain labeled data from engineered structures under various damage states, especially in multiple damages case. Thus, damage localization methods that need to be trained using only structural health data have received much attention as an alternative. In addition, existing multi-damage localization methods are mainly based on structural dynamic responses, such as acceleration, whereas structural quasi-static responses are also sensitive to damage location and perform well in damage localization, such as strain response. However, existing quasi-static response-based damage localization methods usually focus on the single-damage localization problem, ignoring the double- and multi-damage localization problems. Therefore, this paper develops a multidamage localization method based on strain response modeling using the DL-AR-ATT model. The proposed method was compared with one of the latest methods, the BiLSTNet-A-based method, and validated using both simulation and experimental datasets. The results illustrate that the proposed method can accurately locate single and double damage and outperformed the BiLSTNet-A-based method, especially in high noise levels and minor damage cases.
Keyword:
Structural damage detection
Damage localization
Deep learning
Response modeling
Structural health monitoring
期刊
C
IF:
4.8
论文数:
6.2K
被引数:
1.7W
机构
引用论文
Probabilistic damage localization by empirical data analysis and symmetric information measure.png)
MEASUREMENT
MEASUREMENTIF5.6
PREAI
分享收藏Reconstruction of structural long-term acceleration response based on BiLSTM networks基于BiLSTM网络的结构长期加速度响应重构
PREAI
分享收藏
查看更多内容