Monitoring of a Frame Structure Model for Damage Identification using Artificial Neural Networks

DOI

10.2991/emeit.2012.88How to use a DOI?

Keywords

structural health monitoring, damage detection, time-delay neural networks, vibration signature analysis, bridge truss

Abstract

A structural parameter identification and damage detection approach using displacement measurement time series is proposed, and the performance of the approach is validated experimentally with a frame structure model in a healthy condition and with joint connection damages. The proposed approach is carried out using two neural networks: one is called time-delay neural network (TDNN) and the other is called traditional neural network (TNN).The theoretical basis and the selection of the input and output of the TDNN and the TNN are explained. The performance of the proposed methodology for damage detection of the frame structure model with different joint damage scenarios is introduced by direct use of displacement measurement under base excitations. A simulation study has been carried out for the incomplete measurement data. The proposed approach provides an alternative way for damage detection of engineering structures by direct use of structural dynamic displacement measurements.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Niu Lin
PY  - 2012/09
DA  - 2012/09
TI  - Monitoring of a Frame Structure Model for Damage Identification using Artificial Neural Networks
BT  - Proceedings of the 2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT 2012)
PB  - Atlantis Press
SP  - 438
EP  - 441
SN  - 1951-6851
UR  - https://doi.org/10.2991/emeit.2012.88
DO  - 10.2991/emeit.2012.88
ID  - Lin2012/09
ER  -