By Topic

Structural damage detection using artificial neural networks and wavelet transform

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Shi, A. ; Dept. of Electr. Eng., California Polytech. State Univ., San Luis Obispo, CA, USA ; Xiao-Hua Yu

With the ever-increasing demand for the safety and functionality of civil infrastructures, structure health monitoring (SHM) has now become more and more important. Recent developments in computational intelligence and digital signal processing offer great potentials to develop a more efficient, reliable, and robust structure damage identification system. In this paper, the application of artificial neural networks and wavelet analysis is investigated to develop an intelligent and adaptive structural damage detection system. The proposed approach is tested on an IASC (International Association for Structural Control)-ASCE (American Society of Civil Engineers) SHM benchmark problem. Satisfactory computer simulation results are obtained.

Published in:

Computational Intelligence for Measurement Systems and Applications (CIMSA), 2012 IEEE International Conference on

Date of Conference:

2-4 July 2012