DEEP LEARNING DAMAGE IDENTIFICATION METHOD FOR STEEL-FRAME BRACING STRUCTURES USING TIME-FREQUENCY ANALYSIS AND CONVOLUTIONAL NEURAL NETWORKS

Lattice bracing, commonly used in steel construction systems, is vulnerable to damage and failure when subjected to horizontal seismic pressure. To identify damage, manual examination is the conventional method applied. However, this approach is time-consuming and typically unable to detect damage in its early stage. Determining the exact location of damage has been problematic for researchers. Nevertheless, detecting the failure of lateral supports in various parts of a structure using time-frequency analysis and deep learning methods, such as convolutional neural networks, is possible. Then, the damaged structure can be rapidly rebuilt to ensure safety. Experiments are conducted to determine the vibration acceleration modes of a four-storey steel structure considering various support structure damage scenarios. The acceleration signals at each measurement point are then analysed with respect to time and frequency to generate appropriate three-dimensional spectral matrices. In this study, the MobileNetV2 deep learning model was trained on a labelled picture collection of damaged matrix images. Hyperparameter tweaking and training resulted in a prediction accuracy of 97.37% for the complete dataset and 99.30% and 96.23% for the training and testing sets, respectively. The findings indicate that a combination of time-frequency analysis and deep learning methods may pinpoint the position of the damaged steel frame support components more accurately.