Study of structural damage identification through various convolutional neural network models: exploring the accuracy and efficiency

Abstract

In recent years, Artificial Intelligence (AI) has found widespread application in various fields, owing to its efficient data processing capabilities. In Structural Health Monitoring (SHM), AI methods are employed to analyze structural vibration frequency signals, thereby enabling the identification of structural damage. However, due to the existence of numerous methods, further research is essential to investigate the impact of different approaches of damage identification results. In this study, three types of convolutional neural networks (CNNs) were employed to identify structural damage of the ASCE benchmark structure. Additionally, an efficient time-history data processing technique was incorporated into these CNNs. Subsequently, the damage identification results, considering different input data and network structures, were compared. The results reveal the following key findings: (1) The three utilized networks (1D-CNN, 2D-CNN, and PCNN) exhibit high identification accuracy when dealing with relatively simple damage scenarios. ( 2) The transformation of onedimensional vector data into frequency-domain signals through the Fast Fourier Transform (FFT), followed by inputting this FFT signal into the 2D-CNN, results in a significant improvement in damage identification accuracy, particularly in more complex damage scenarios. (3) The PCNN_FFT model demonstrates high accuracy when dealing with relatively simple damage scenarios, but its accuracy can be substantially reduced in more complex situations.