Damage identification of composite beam structure using fuzzy logic-based model

Deepak K. Agarwalla

International Journal of Data Science, 2018, vol. 3, issue 2, 170-187

Abstract: Damage identification of beam structures has been in practice for last few decades. The methodologies adopted were upgraded over the time depending upon the complexities of the damage or crack and the desired accuracy. The utilisation of artificial intelligence (AI) techniques has also been considered by many researchers. In the current research, damage detection of a glass fibre-reinforced composite cantilever beam subjected to vibration has been carried out. A fuzzy-based model using triangular, trapezoidal and Gaussian membership functions has been developed separately to predict the damage characteristics, i.e., relative damage position (RDP) and relative damage severity (RDS). The inputs required for the fuzzy-based model, i.e., first three relative natural frequencies and first three mode shape differences have been determined by finite element analysis of the damaged cantilever beam subjected to the natural vibration. An experimental setup has been used to justify the robustness of the proposed technique for damage identification.

Keywords: damage; glass fibre-reinforced composite cantilever beam; fuzzy model; triangular membership function; trapezoidal membership function; Gaussian membership function; relative natural frequency; mode shape difference; RDP; relative damage position; RDS; relative damage severity. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=92281 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsci:v:3:y:2018:i:2:p:170-187

Access Statistics for this article

More articles in International Journal of Data Science from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().