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Structural modification assessment using supervised learning methods applied to vibration data
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DOI：10.1016/j.engstruct.2015.05.003.png)
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Abstract
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Structural systems are usually subjected to degradation processes due to a combination of causes, such as design or constructive problems, unexpected loadings or natural ageing. Machine learning algorithms have been extensively applied to classification and pattern recognition problems in the past years. Some papers have addressed special attention to applications regarding damage assessment, especially how these algorithms could be used to classify different structural conditions. Most of these works were based on the comparison of measured vibration data such as natural frequencies and vibration modes in undamaged and damaged states of the structure. This methodology has proven to be efficient in various studies presented in the literature. However, its application may not be the most adequate in cases where the engineer needs to know with certain imperativeness the condition of a given structure. This paper proposes a novel approach introducing the concept of Symbolic Data Analysis (SDA) to manipulate raw vibration data (signals, i.e. acceleration measurements). These quantities (transformed into symbolic data) are combined to three well-known classification techniques: Bayesian Decision Trees, Neural Networks and Support Vector Machines. The objective is to explore the efficiency of this combined methodology. For this purpose, only raw information are used for feature extraction. In order to attest the robustness of this approach, experimental tests are performed on a simply supported beam considering different damage scenarios. Moreover, this paper presents a study with tests conducted on a motorway bridge, in France where thermal variation effects also have to be considered. In summary, results obtained confirm the efficiency of the proposed methodology. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Pattern recognition
Damage assessment
Learning algorithms
Symbolic data
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