Abstract
In this paper, a convolutional neural network (CNN) was used to extract the damage features of a steel frame structure. As structural damage could induce changes of the modal parameters of the structure, the convolution operation was used to extract the features of modal parameters, and a classification algorithm was used to judge the damage state of the structure. The finite element method was applied to analyze the free vibration of the steel frame and obtain the first-order modal strain energy for various damage scenarios, which was used as the CNN training sample. Then vibration experiments were carried out, and modal parameters were obtained from the modal analysis of the vibration signals. The experimental data were inputted into the CNN to verify its damage detection capability. The result showed that the CNN was effective in detecting the intact structure, single damage, and multi damages with an accuracy of 100%. For comparison, the same samples were also applied to the traditional back propagation (BP) neural network, which failed to detect the intact structure and multiple-damage cases. It was found that: (1) The proposed CNN could be trained from finite element simulation data and used in real frame structure damage detection, and it performed better in structural damage detection than BP neural networks; (2) the measured data of a real structure could be supplemented by numerical simulation data, and satisfactory results have been demonstrated.
Highlights
Structural damage detection is an important research field of structural health monitoring (SHM) to prevent the sudden collapse of structures and avoid casualties and heavy economic losses.The assessment of structural safety in practice generally depends on the engineering judgment of experts by visual inspections
Based on the network trained by simulation data, the experimental data were inputted into the neural network to detect the structure state
Based on the network trained by simulation data, the experimental data were inputted into following parts: (1) The modal parameters were obtained through modal analyses of vibration the neural network to detect the structure state
Summary
Structural damage detection is an important research field of structural health monitoring (SHM) to prevent the sudden collapse of structures and avoid casualties and heavy economic losses.The assessment of structural safety in practice generally depends on the engineering judgment of experts by visual inspections. Though promising results have been achieved in structural surface crack detection [1], visual inspections may be costly or inefficient, and the safety rating assigned by trained inspectors may be subjective [2,3]. Vibration-based methods have increasingly become a hot research topic in structural damage detection due to their flexibility of measurement, cost-effectiveness, and non-destructiveness [4]. By collecting the vibration excitation and response data of a structure, the modal parameters can be obtained, and the potential damage of the structure can be able to be detected by analyzing the change of its modal parameters, e.g., natural frequencies, modal shapes, or damping [4,5,6]. A frequency-based structural damage detection method has been used in damage detection of composite structures [7,8].
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