Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Downloads (Pure)

Abstract

Failure of large complex structures such as buildings and bridges can have monumental repercussions such as human mortality, environmental destruction and economic consequences. It is therefore paramount that detection of structural damage or anomalies are identified and managed early. This highlights the need to develop automated Structural Health Monitoring (SHM) systems that can continuously allow the safety status of structures to be determined, even in the worst and most isolated conditions, to ultimately help prevent destruction and save lives. Signal processing is a crucial step to detecting structural anomalies and recent work demonstrates the opportunities for neural networks, however the encoding of data for SHM requires the extraction of features due to often, noisy data. This paper focuses on feature extraction methods for artificial neural networks (ANNs) and spiking neural networks (SNNs) and aims to identify bespoke features which enable SNNs to encode data and perform the classification of anomalies. Results show that extraction of particular features in large real-world applications improve the classification accuracy of SNNs.
Original languageEnglish
Title of host publicationProceedings of the 15th International Joint Conference on Computational Intelligence
Pages514-523
Number of pages10
Volume1
EditionNCTA
ISBN (Electronic)978-989-758-674-3
DOIs
Publication statusPublished online - 15 Nov 2023
Event15th International Conference on Neural Computation Theory and Applications - Rome, Italy
Duration: 13 Nov 202315 Nov 2023
https://ncta.scitevents.org/

Publication series

Name
ISSN (Electronic)2184-3236

Conference

Conference15th International Conference on Neural Computation Theory and Applications
Abbreviated titleNCTA
Country/TerritoryItaly
CityRome
Period13/11/2315/11/23
Internet address

Keywords

  • Structural Health Monitoring,
  • feature extraction
  • spiking neural networks
  • classification

Fingerprint

Dive into the research topics of 'Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies'. Together they form a unique fingerprint.

Cite this