Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring

We present a multiresolution classification framework with semi-supervised learning for the indirect structural health monitoring of bridges. The monitoring approach envisions a sensing system embedded into a moving vehicle traveling across the bridge of interest to measure the modal characteristics of the bridge. To enhance the reliability of the sensing system, we use a semi-supervised learning algorithm and a semi-supervised weighting algorithm within a multiresolution classification framework. We show that the proposed algorithm performs significantly better than supervised multiresolution classification.

[1]  James H. Garrett,et al.  Exploring Indirect Vehicle-Bridge Interaction for Bridge SHM , 2010 .

[2]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

[3]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[4]  David G. Stork,et al.  Pattern Classification , 1973 .

[5]  I. Daubechies.  Orthonormal bases of compactly supported wavelets II: variations on a theme , 1993 .

[6]  Jelena Kovacevic,et al.  A multiresolution approach to automated classification of protein subcellular location images , 2007, BMC Bioinformatics.

[7]  R. Coifman,et al.  A general framework for adaptive regularization based on diffusion processes on graphs , 2006 .

[8]  James H. Garrett,et al.  Indirect structural health monitoring of a simplified laboratory-scale bridge model , 2014 .

[9]  Zoubin Ghahramani,et al.  Learning from labeled and unlabeled data with label propagation , 2002 .

[10]  James H. Garrett,et al.  Indirect structural health monitoring in bridges: scale experiments , 2012 .

[11]  Jelena Kovacevic,et al.  Multiresolution identification of germ layer components in teratomas derived from human and nonhuman primate embryonic stem cells , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[12]  Ronald R. Coifman,et al.  Signal processing and compression with wavelet packets , 1994 .

[13]  Yeong-Bin Yang,et al.  EXTRACTING BRIDGE FREQUENCIES FROM THE DYNAMIC RESPONSE OF A PASSING VEHICLE , 2002 .

[14]  Yeong-Bin Yang,et al.  EXTRACTING BRIDGE FREQUENCIES FROM THE DYNAMIC RESPONSE OF A PASSING VEHICLE , 2002 .

[15]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[16]  Jelena Kovacevic,et al.  TOWARDS AN IMAGE ANALYSIS TOOLBOX FOR HIGH-THROUGHPUT DROSOPHILA EMBRYO RNAI SCREENS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[17]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[18]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.