Structural damage detection based on decision-level fusion with multi-vibration signals
Jiqiao Zhang, Zihan Jin, Shuai Teng, Gongfa Chen, David Bassir
Abstract
Abstract When a structure is damaged, its vibration signals change. If a single vibration signal is used for structural damage detection (SDD), it may sometimes lead to low detection accuracy. To avoid this phenomenon, this paper presents a SDD method based on decision-level fusion (DLF) with multi-vibration signals. In this study, acceleration (ACC), strain (E), displacement (DIS), and the fusion signal of all three of these signals (ACC, E and DIS), are studied. The damage information can be extracted from the vibration signal of a structure by using convolution neural networks (CNN). The above four vibration signals are used as the inputs to train four CNN models, and each model outputs a corresponding result. Finally, a DLF strategy is used to fuse the detection results of each CNN. To demonstrate the effectiveness and correctness of the proposed method, a steel frame bridge is investigated with numerical simulations and vibration experiments. The research shows that the damage detection method based on DLF with multi-vibration signals can effectively improve the accuracy of the CNN damage detection.