Loading condition monitoring on trusses applying a machine learning approach with training data of a finite element model: A study case

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F19%3APU138224" target="_blank" >RIV/00216305:26620/19:PU138224 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Loading condition monitoring on trusses applying a machine learning approach with training data of a finite element model: A study case

Original language description

Structural health monitoring (SHM) techniques deal with the changes in the dynamic or static characteristics of the structures that affect its performance during the service [1]. Mainly, these techniques are based on vibrations, and their implementation includes complex integrated systems not addressed from the structural design. Despite the numerous applications of SHM, loading condition monitoring (application place, direction, and magnitude) is not a very implemented strategy in this engineering field. This paper presents a methodology to monitor the application of external forces on structures using a learning machine process and finite element analysis (FEA). In Figure 1a is described the proposed monitoring methodology, which is applied to a truss structure to validate this study. The truss contains nine structural elements, and each one presents a piezoelectric transducer to measure the forces in their links, as illustrated in Figure 1b. The real truss was modeled by means of a FEA (implemented in Matlab with truss elements) considering their mechanical properties and loading conditions, as observed in Figure 1c. To simulate different loading conditions, a force F_s is applied in node 3 varying two parameters, angle β, and magnitude. This is carried out to establish a database using the internal forces (in each bar) obtained by FEA.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

<a href="/en/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 35th conference Computational Mechanics 2019

ISBN

978-80-261-0889-4

ISSN

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e-ISSN

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Number of pages

3

Pages from-to

205-206

Publisher name

University of West Bohemia,

Place of publication

Plzen, Czech Republic

Event location

Srní

Event date

Nov 4, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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