Techno Press


Steel and Composite Structures   Volume 29, Number 6, December25 2018, pages 703-716
DOI: http://dx.doi.org/10.12989/scs.2018.29.6.703
 
An intelligent health monitoring method for processing data collected from the sensor network of structure
Ramin Ghiasi and Mohammad Reza Ghasemi

 
Abstract     [Full Text]
    Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, Nyström method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.
 
Key Words
    damage detection; unsupervised feature learning; moving kernel principal component analysis; Nyström method
 
Address
Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran
 

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