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

Pengyu Li; Yongjie Cao

doi:10.1117/12.3028960

22 May 2024 Study of structural damage identification through various convolutional neural network models: exploring the accuracy and efficiency

Pengyu Li, Yongjie Cao

Author Affiliations +

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131761T (2024) https://doi.org/10.1117/12.3028960
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

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 one-dimensional 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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Pengyu Li and Yongjie Cao "Study of structural damage identification through various convolutional neural network models: exploring the accuracy and efficiency", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131761T (22 May 2024); https://doi.org/10.1117/12.3028960

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KEYWORDS

Education and training

Convolutional neural networks

Sensors

Data modeling

Structural health monitoring

Wavelets

Fourier transforms

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