Standard Article

Artificial Neural Networks

Signal Processing

  1. Steve Reed

Published Online: 15 SEP 2009

DOI: 10.1002/9780470061626.shm055

Encyclopedia of Structural Health Monitoring

Encyclopedia of Structural Health Monitoring

How to Cite

Reed, S. 2009. Artificial Neural Networks. Encyclopedia of Structural Health Monitoring. .

Author Information

  1. QinetiQ, Farnborough, UK

Publication History

  1. Published Online: 15 SEP 2009


Artificial neural networks (ANNs) are able to learn from experience, generalize from examples, and identify underlying information from within noisy data. These characteristics, and the explosion in ANN techniques over the past 15 or 20 years, have led to an ever-increasing role for ANNs within structural health monitoring (SHM) systems. Within this article, the development of ANNs and the basic principles behind their functionality, training, and deployment are described. Emphasis is placed upon the multilayer perceptron (MLP), as the workhorse of ANN methods; however, other techniques, including radial basis functions (RBF) and self-organizing feature maps (SOFMs), are also detailed. Examples of the use of ANNs within SHM systems are presented and reference has been made to a wide range of texts throughout this article.


  • artificial neural networks;
  • novelty detection;
  • classification;
  • regression;
  • damage detection;
  • structural usage monitoring