Sensor placement for structural damage detection considering measurement uncertainties
MetadataShow full item record
Structural damage detection methods using vibration measurements have been developed for decades. Measurement selections may affect damage detection results, because inevitable uncertainties are involved in vibration testing. A new sensor placement index is defined as the ratio of two parameters, namely, the contribution of measurement points to a Fisher information matrix, and the damage sensitivity to the measurement noise. A large value of the contribution vector represents that the corresponding measurement points are sensitive to the damage and measurements at these points are more prominent for structural damage identification, whereas a small noise sensitivity value indicates measurement points that are less influenced by noises. Consequently, the points with large index values are chosen as the measurement subset. The effectiveness of the proposed technique is verified using a laboratory-tested steel frame. The damage detection using different measurement selection schemes shows that the present technique can identify multiple damages of the structure more accurately. The effect of the measurement number is also investigated.
Copyright © 2013 Multi-Science Publishing
Showing items related by title, author, creator and subject.
Structural damage detection based on the reconstructed phase space for reinforced concrete slab: Experimental studyNie, Z.; Hao, Hong; Ma, H. (2013)In this paper, a parameter based on geometry changes of the reconstructed multidimensional phase space of the measured vibration signals for structural damage identification is proposed. The choice of the proper delay ...
Li, Jun; Hao, Hong (2015)This paper investigates the feasibility and effectiveness of using a recently developed relative displacement sensor for the structural health monitoring of joint conditions in steel truss bridges. The developed relative ...
Substructuring technique for damage detection using statistical multi-stage artificial neural networkBakhary, N.; Hao, Hong; Deeks, A. (2010)Artificial Neural networks (ANN) have been proven in many studies to be able to efficiently detect damage from vibration measurements. Their capability to recognize patterns and to handle non-linear and non-unique problems ...