This documentation provides information about the experimental work described in our paper: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks.
In this work, a large-scale laboratory structure was constructed at Qatar University structures laboratory. The structure was used to obtain a large dataset of vibration signals collected under several structural damage scenarios.
The dataset is published here as a new benchmark problem for vibration-based structural health monitoring (SHM). Our goal is to provide SHM researchers with a new testbed for verifing the newly-developed vibration-based damage detection approaches.
The documentation contains the following sections:
- Qatar University Grandstand Simulator (QUGS)
- Damage Scenarios
- Dataset Description