• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.22 no.1
• /
• pp.9-14
• /
• 2012

This paper investigates the prediction of the dynamic strain response using acceleration response only. Two methods are proposed for the strain prediction; one is based on beam theory and the other is calculated by the frequency response function between acceleration and strain. First, it is estimated the dynamics of the simple notched beam, including the non-linearity, through the uni-axial vibration testing. Then, the dynamic strain response is predicted under two different methods using acceleration response. The validation of proposed methods is conducted by the comparison between measured strain and predicted values. The comparison reveals that the proposed method based on the FRF between acceleration and strain is more reliable one than that stemmed from beam theory and the maximum relative error is less than 8 %.

• Journal of Korean Society of Steel Construction
• /
• v.17 no.1 s.74
• /
• pp.1-11
• /
• 2005

A bi-level damage detection algorithm that utilizes the dynamic responses of the structure as input and neural network (NN) as pattern classifier is presented. Signal anomaly index (SAI) is proposed to express the amount of changes in the shape of frequency response functions (FRF) or strain frequency response function (SFRF). SAI is calculated using the acceleration and dynamic strain responses acquired from intact and damaged states of the structure. In a bi-level damage identification algorithm, the presence of damage is first identified from the magnitude of the SAI value, then the location of the damage is identified using the pattern recognition capability of 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.