MATEC Web Conf.
Volume 262, 201964 Scientific Conference of the Committee for Civil Engineering of the Polish Academy of Sciences and the Science Committee of the Polish Association of Civil Engineers (PZITB) (KRYNICA 2018)
|Number of page(s)||6|
|Section||Mechanics of Structures and Materials|
|Published online||30 January 2019|
Axial force prediction based on signals of the elastic wave propagation and artificial neural networks
University of Technology, Department of Structural Mechanics, Rzeszow, Poland
* Corresponding author: firstname.lastname@example.org
The identification of internal forces is not only important to preserve the structure integrity but also to understand how their certain elements and connections work. Two examples of laboratory test are discussed in this paper. The first is related to an aluminium rod mounted in a stand where compression load was applied. Due to the relaxation phenomenon force prediction becomes even more important in case of compressed bolts. Thus, the second example is related to a bolted flange connection during static tensile test. Four out of six bolts were equipped with washer load cells. Alternatively, selected bolts were equipped with piezoelectric transducers (actuator and sensor) in order to measure signals of elastic waves. It was noted that the load increasing causes changes in the measured signals. Principal components analysis was used for dimensionality reduction of measured signals. The aim of this study is to investigate the use of elastic waves and artificial neural networks for the purpose of the force of identification. Examples of preliminary results have shown that axial forces may be estimated with relatively good accuracy.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.