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2015年 第9卷 第1期

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《结构与土木工程前沿（英文）》 >> 2015年 第9卷 第1期 doi: 10.1007/s11709-014-0277-3

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

1. Department of Structures, Av. Brasil 101, Lisbon 1700-066, Portugal.2. Technical Center for Bridge Engineering, CEREMA, B.P. 214, Provins Cedex 77487, France.3. Materials and Structures Department, IFSTTAR, University Paris-Est, 14-20 Boulevard Newton, Champs sur Marne, Marne la Vallée Cedex2, F-77447, France.4. Department of Civil Engineering, Technical University Lisbon, Avenida Rovisco Pais, Lisbon 1096, Portugal

录用日期： 2014-12-12 发布日期： 2015-04-02
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A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

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