TIME-DOMAIN STRUCTURAL DAMAGE IDENTIFICATION: FROM A DICTIONARY LEARNING PERSPECTIVE

Structures inevitably deteriorate during their service lives. To accurately evaluate their structural condition, the methods capable of identifying and assessing damage in a structure timely and accurately have drawn increasing attention. Compared to widely-used frequency-domain methods, the processing of time-domain data is more efficient, but remains difficult since it is usually hard to discern signals from different conditions. In fact, the signal processing fields have observed the evolution of techniques, from such traditional fixed transforms as Fourier, to dictionary learning (DL). DL leads to better representation and hence can provide improved results in many practical applications. In this paper, an innovative time-domain damage identification algorithm is proposed from a DL perspective, using D-KSVD algorithm. The numerical simulated soil-pipe system is used for verifying the performance of the proposed method. The results demonstrate that this damage identification scheme is a promising tool for structural health monitoring.

[1]  Wei-Xin Ren,et al.  Damage detection by finite element model updating using modal flexibility residual , 2006 .

[2]  Ying Wang,et al.  FEM Calibrated ARMAX Model Updating Method for Time Domain Damage Identification , 2013 .

[3]  Marco Domaneschi,et al.  Vibration based damage localization using MEMS on a suspension bridge model , 2013 .

[4]  Arun Kumar Pandey,et al.  Damage detection from changes in curvature mode shapes , 1991 .

[5]  Baoxin Li,et al.  Discriminative K-SVD for dictionary learning in face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[7]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[8]  Ying Wang,et al.  Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion , 2014 .

[9]  S. Law,et al.  Structural Damage Detection from Modal Strain Energy Change , 2000 .

[10]  Hong Hao,et al.  Civil structure condition assessment by FE model updating: methodology and case studies , 2001 .

[11]  Jean Ponce,et al.  Task-Driven Dictionary Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[13]  Ying Wang,et al.  Damage Identification Scheme Based on Compressive Sensing , 2015, J. Comput. Civ. Eng..

[14]  Ying Wang,et al.  Guided-wave-based method for concrete de-bonding damage identification using DISC , 2014 .

[15]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[16]  O. S. Salawu.  Detection of structural damage through changes in frequency: a review , 1997 .

[17]  Christian Cremona,et al.  Assessment of vibration-based damage identification techniques , 2006 .

[18]  Ying Wang,et al.  SIMPLIFIED PIPELINE-SOIL INTERACTION MODEL FOR VIBRATION-BASED DAMAGE DETECTION OF ONSHORE PIPELINES , 2010 .

[19]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[20]  Michael I Friswell,et al.  Damage identification using inverse methods , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .