Frequency domain CNN and dissipated energy approach for damage detection in building structures

Recent developments tools and techniques for structural health monitoring allow the design of early warning systems for the damage diagnosis and structural assessment. Most methods to damage detection involve vibration data analysis by using identification systems that generally require a mathematical model and much information about the system, such as parameters and states that are mostly unknown. In this paper, a novel frequency domain convolutional neural network (FDCNN) proposed aims to design an identification system for damage detection based on Bouc-Wen hysteretic model. FDCNN, unlike other works, only requires acceleration measurements for damage diagnosis that are very sensitive to environmental noise. In contrast to neural network (NN) and time domain convolutional neural network, FDCNN reduces the computational time required for the learning stage and adds robustness against noise in data. The FDCNN includes random filters in the frequency domain to avoid measurement noise using a spectral pooling operation, which is useful when the system bandwidth is unknown. Incorrect filtering can produce unwanted results, as a shifted and attenuation signal relative to the original. Moreover, FDCNN allows overcoming the parameterization problem in nonlinear systems, which is often difficult to achieve. In order to validate the proposed methodology, a comparison between two different architectures of convolutional neural networks is made, showing that proposed CNN in frequency domain brings better performance in the identification system for damage diagnosis in building structures. Experimental results from reducing scale two-storey building confirm the effectiveness of the proposed.