Damage detection in simply supported beams using machine learning
DOI:
https://doi.org/10.24193/subbeng.2022.1.1Keywords:
damage detection, machine learning, natural frequen-cy, structural health monitoring.Abstract
The more our infrastructure is aging, the risk of structural failure is higher, making the detection of damage using modal parameters a very important factor that can be applied in structural health monitoring. The most desired way to assess the health of engineering structures during operation is to use non-destructive vibration-based methods. In the current paper, a modal approach using a machine learning technique by training a feedforward backpropa-gation neural network for detecting transverse damages in simple supported beam-like structures is presented. A method for analyti-cal determination of the training data is used and the obtained da-taset values are employed for training an ANN that will be used to locate and evaluate the severity of transverse cracks in cantilever beams. The output from the ANN model is compared by plotting the calculated error for each case in comparison with FEM results us-ing the SolidWorks simulation software.
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