TY  - EJOU
AU  - Silik, Ahmed 
AU  - Wang, Xiaodong 
AU  - Mei, Chenyue 
AU  - Jin, Xiaolei 
AU  - Zhou, Xudong 
AU  - Zhou, Wei 
AU  - Chen, Congning 
AU  - Hong, Weixing 
AU  - Li, Jiawei 
AU  - Mao, Mingjie 
AU  - Liu, Yuhan 
AU  - Noori, Mohammad 
AU  - Altabey, Wael A. 

TI  - Development of Features for Early Detection of Defects and Assessment of Bridge Decks
T2  - Structural Durability \& Health Monitoring

PY  - 2023
VL  - 17
IS  - 4
SN  - 1930-2991

AB  - Damage detection is an important area with growing interest in mechanical and structural engineering. One of the critical issues in damage detection is how to determine indices sensitive to the structural damage and insensitive to the surrounding environmental variations. Current damage identification indices commonly focus on structural dynamic characteristics such as natural frequencies, mode shapes, and frequency responses. This study aimed at developing a technique based on energy Curvature Difference, power spectrum density, correlation-based index, load distribution factor, and neutral axis shift to assess the bridge deck condition. In addition to tracking energy and frequency over time using wavelet packet transform, in order to further demonstrate the feasibility and validity of the proposed technique for bridge condition assessment, experimental strain data measured from two stages of a bridge in the different intervals were used. The comparative analysis results of the bridge in first and second stage show changes in the proposed feature values. It is concluded, these changes in the values of the proposed features can be used to assess the bridge deck performance.
KW  - Structural health monitoring; strain monitoring; distribution factor; wavelet packet transform

DO  - 10.32604/sdhm.2023.023617