The STUDIA UNIVERSITATIS BABE┼×-BOLYAI issue article summary

The summary of the selected article appears at the bottom of the page. In order to get back to the contents of the issue this article belongs to you have to access the link from the title. In order to see all the articles of the archive which have as author/co-author one of the authors mentioned below, you have to access the link from the author's name.

 
       
         
    STUDIA ENGINEERING - Issue no. 1 / 2022  
         
  Article:   DAMAGE DETECTION IN SIMPLY SUPPORTED BEAMS USING MACHINE LEARNING.

Authors:  ALEXANDRA-TEODORA AMAN, CRISTIAN TUFISI, GILBERT-RAINER GILLICH.
 
       
         
  Abstract:  DOI: 10.24193/subbeng.2022.1.1

Published Online: 2022-11-11
pp. 7-15

VIEW PDF

FULL PDF: VOL.67, No.1, 2022

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 backpropagation neural network for detecting transverse damages in simple supported beam-like structures is presented. A method for analytical determination of the training data is used and the obtained dataset 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 using the SolidWorks simulation software.

Keywords: damage detection, machine learning, natural frequency, structural health monitoring
 
         
     
         
         
      Back to previous page