Abstract
We present in this paper a framework for damage detection and localization using neural networks. The data we use to train the network are pixel images consisting of measurements of the relative variations of m natural frequencies of the structure under monitoring over a period of d-days. To measure the relative variations of the natural frequencies, we use the stretching method, which allows us to obtain reliable measurements amidst fluctuations induced by environmental factors such as temperature variations. We show that even by monitoring a single natural frequency over a few days, accurate damage detection can be achieved. The accuracy for damage detection significantly improves when a small number of natural frequencies is monitored instead of a single one. More importantly, monitoring multiple natural frequencies allows for damage localization provided that the network can be trained for both healthy and damaged scenarios. This is feasible under the assumption that damage occurs at a finite number of damage-prone locations. Several results obtained with numerically simulated data illustrate the effectiveness of the proposed approach.
Highlights
The necessity and significance of Structural Health Monitoring (SHM) to ensure the long-term integrity of structures is well recognized
We show the results for damage detection and localization using measurement image matrix (MIM) to train our neural network
We start with the simple network that uses MIMs for a single natural mode and show that using machine learning improves the accuracy of damage detection compared to the visual inspection of the ∆ν/ν curve
Summary
The necessity and significance of Structural Health Monitoring (SHM) to ensure the long-term integrity of structures is well recognized. Damage inevitably occurs during the lifetime of a structure. To monitor structural health conditions, SHM systems have been implemented in almost all areas of engineering including civil, mechanical and aerospace engineering applications. In this context, SHM is defined as the process of detecting, localizing and possibly quantifying structural damage. A standard classification for vibration-based SHM procedures is that of parametric (response-based) and non-parametric (model-based) methodologies [1]. The latter category usually requires a numerical model for the structure, which may be a data-driven simulation of the existing structure. A recent review on vibration-based damage identification for civil engineering structures can be found in [2]
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