Table 2.

Examples of vibration-based datasets for deep learning (DL)-based SHM.

Reference(s)	Goal	Dataset
Zhang et al. [208]	Vibration-based structural state identification	8595, 14,465, and 4800 raw acceleration data (9 Ch. × 10,000) for each of the bridges
Pathirage et al. [28]	Damage identification by making a deep mapping between the modal characteristics and structural damage	20,000 data samples containing the first three frequencies and mode shapes obtained by Eigen analysis of finite element model
Avci et al. [52]	Wireless vibration-based bolt loosening detection	330 signals each containing 245,760 samples of velocity
Pathirage [63]	Vibration-based damage detection and finding the stiffness reduction of elements	Modal information of 10,300 damage cases that include the first seven frequencies (7 arrays) and the regarding mode shapes at 14 beam-column joints (98 arrays)
Tang et al. [58]	Data anomaly detection and classification	10,014 time and frequency response of a long-span cable-stayed bridge stacked in two channels with the resolution of 100 × 100
Wang and Cha [55]	Vibration-based loosened bolt localization	6800 frequency domain 50 × 50 matrices calculated by Fast Fourier Transformation (FFT) of acceleration signals of a lab-scale bridge
Yu et al. [209]	Damage identification and localization of buildings controlled with smart devices	1900 group of 5 × 2832 matrices of power spectral density
Lin and Nie [54]	Vibration-based feature extraction for damage detection	459 set of vertical acceleration signals collected from nine nodes in 1024 × 9 matrices
Bao et al. [30]	Vision-based anomaly detection and classification in a long-span cable-stayed bridge	333,792 of acceleration signals plotted in 100 × 100 one channel images
Abdeljaber et al. [53]	Bolt loosening localization on a lab-scale steel grandstand simulator	749 × 12 vectors of acceleration signals with 128 × 1 dimension