A New Approach to Structural Damage Identification Based on Power Spectral Density and Convolutional Neural Network

Author:

Fang Youliang123ORCID,Li Chanpeng1ORCID,Wu Sixiang1ORCID,Yan Menghao1ORCID

Affiliation:

1. College of Civil Engineering and Architecture, Hebei University, Baoding 071000, P. R. China

2. Engineering Research Center of Zero-carbon, Energy Building and Measurement Technique, Ministry of Education, Baoding 071000, P. R. China

3. Technology Innovation Center for Testing and, Evaluation in Civil Engineering of Hebei Province, Baoding 071000, P. R. China

Abstract

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is [Formula: see text] in the case of no noise addition; the optimal training MSE is [Formula: see text] in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage.

Funder

Accelerated Bridge Construction University Transportation Center

Publisher

World Scientific Pub Co Pte Ltd

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Convolutional Neural Networks Model Based on Wavelet Packet Transform for Enhanced Earthquake Early Warning;International Journal of Structural Stability and Dynamics;2024-06-24

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