Journal
JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
Volume 18, Issue 8, Pages 785-792Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1045389X06073640
Keywords
damage detection; frequency response function (FRF); strain frequency response function (SFRF); signal anomaly index (SAI); pattern recognition; neural network (NN)
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Funding
- Korea Agency for Infrastructure Technology Advancement (KAIA) [03산C02-01] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [R11-1997-045-13006-0] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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A bi-level damage detection algorithm that utilizes dynamic responses of the structure as input and neural network (NN) as a pattern classifier is presented. The signal anomaly index (SAI) is proposed to express the amount of changes in the shape of frequency response functions (FRFs) or strain frequency response function (SFRF). SAI is calculated by using the acceleration and dynamic strain responses acquired from intact and damaged states of the structure. In a bi-level damage identification algorithm, first the presence of damage is identified from the magnitude of the SAI value. Then the location of the damage is identified using the pattern recognition capability of the NN. The proposed algorithm is applied to an experimental model bridge to demonstrate the feasibility of the algorithm. Numerically simulated signals are used for training the NN, and experimentally acquired signals are used to test the NN. The results of this example application suggest that the SAI based pattern recognition approach may be applied to the structural health monitoring system for a real bridge.
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