Location

London

Event Website

http://www.csce2016.ca/

Description

Many structural damage detection methods using machine learning algorithms and clustering methods have been proposed and developed in recent years. Novelty detection is a common method that is based on an unsupervised learning technique to detect structural damage. The detection process involves applying the novelty detection algorithm to recognize abnormal data from the testing data sets. In order to make these algorithms capable of identifying abnormal data, sufficient normal data must first be obtained and used as training data. It is the fact that sufficient normal data is relatively convenient to measure compared to abnormal data for large-scale civil structures. Abnormal data from the testing data sets can be identified by using the well-trained normal model established by the algorithms. In this paper, a machine learning based novelty detection method called the Density Peaks based Fast Clustering Algorithm (DPFCA) is introduced and some improvements to this algorithm are made to increase the precision of detecting and localizing the damage in an experimental structure. Feature extraction is also an important factor in the process of damage detection. Thus, two damage-sensitive features such as crest factor, and transmissibility are extracted from the measured responses in the experiments. Experimental results showed good performance of the innovative method in detecting and locating the structural damage positions in various scenarios.


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Jun 1st, 12:00 AM Jun 4th, 12:00 AM

STR-903: UNSUPERVISED NOVELTY DETECTION BASED STRUCTURAL DAMAGE DETECTION METHOD

London

Many structural damage detection methods using machine learning algorithms and clustering methods have been proposed and developed in recent years. Novelty detection is a common method that is based on an unsupervised learning technique to detect structural damage. The detection process involves applying the novelty detection algorithm to recognize abnormal data from the testing data sets. In order to make these algorithms capable of identifying abnormal data, sufficient normal data must first be obtained and used as training data. It is the fact that sufficient normal data is relatively convenient to measure compared to abnormal data for large-scale civil structures. Abnormal data from the testing data sets can be identified by using the well-trained normal model established by the algorithms. In this paper, a machine learning based novelty detection method called the Density Peaks based Fast Clustering Algorithm (DPFCA) is introduced and some improvements to this algorithm are made to increase the precision of detecting and localizing the damage in an experimental structure. Feature extraction is also an important factor in the process of damage detection. Thus, two damage-sensitive features such as crest factor, and transmissibility are extracted from the measured responses in the experiments. Experimental results showed good performance of the innovative method in detecting and locating the structural damage positions in various scenarios.

http://ir.lib.uwo.ca/csce2016/London/Structural/58