A convolution neural network-based technique for health monitoring of connections of a multi-story 3D steel frame structure

Joint damage initiates a consequential form of damage in the beam-to-column connection in a steel frame structure. Many traditional damage detection techniques are not suited for such cases. However, available vibration-based methods are unable to provide a general joint damage detection technique that can be applied to all types of structures. The primary objective of this study is to develop a connection damage identification technique for a 3D frame using a convolutional neural network (CNN) model. For that purpose, a five-story steel 3D frame is considered. An impact hammer is utilized to excite the structure and collect acceleration responses at various points under both undamaged and damaged conditions. From these responses, scalogram images are generated, which serve as input for the CNN-based deep learning technique. The results are then compared with those obtained using the AlexNet model. The training and testing results demonstrate that the technique can effectively differentiate between undamaged and damaged classes, showcasing its potential as an automated tool for the health monitoring of frame connections. The robustness of the technique is further computationally verified through environmental variability, along with the localization and severity of the damage.