Comparative analysis between SOM Networks and Bayesian Networks applied to structural failure detection

• Pedroza Torres, Mauricio ^[1] ; Mariotte Parra, Efraín Guillermo ^[1] ; Quiroga Mendez, Jabid Eduardo ^[1] ; Muñoz, Yecid Alfonso ^[2]
  1. [1] Universidad Industrial de Santander

     Universidad Industrial de Santander

     Colombia

     
  2. [2] Universidad Antonio Nariño

     Universidad Antonio Nariño

     Colombia

     
• Localización: TECCIENCIA, ISSN-e 2422-3670, ISSN 1909-3667, Vol. 11, Nº. 20, 2016 (Ejemplar dedicado a: TECCIENCIA (JAN-JUN 2016)), págs. 47-55
• Idioma: inglés
• Enlaces
  • Texto completo (pdf)
• Resumen

  • In this paper is carried out a comparative study between Self-Organizing Maps (SOM) and Bayesian Networks, to evaluate their performance in the field of structural health as damage detector of structural failures type III, which detects where the damaged area is and percentage of damage within this the area. The implemented classifiers are trained to detect structural failures in a simply supported beam and a truss of 13 elements, the detection is performed using modal information from different test scenarios, obtained by OpenSees® and MATLAB®. The simulations show a satisfactory performance of Bayesian networks to provide the range of stiffness variation in each element of the studied systems. Meanwhile, SOM networks are useful in predicting the decrease in elastic modulus, which is assumed as specific percentage of damage. Based on this results is proposed a hybrid methodology (BAYSOM) seeking to reduce computational cost and improve performance in diagnosis and detection of fault conditions in structures.