Detecting the damage of bridges under changing environmental conditions using the characteristics of the nonlinear narrow dimension of damage features
作者： Changxi Yang , Yang Liu
DOI: 10.1016/J.YMSSP.2021.107842
关键词:
摘要: Abstract Changes in environmental conditions have a considerable influence on the results of damage detection bridges, and these changing may cause nonlinear behavior features used for detecting bridges. Some approaches been developed to perform under linear condition features; however, few studies considered possessing performance caused by conditions. To address this concern, characteristics narrow dimension (CNND) is proposed analyze features. For with strong CNND, method based k-segments algorithm principal curve equivalently solve issue using piecewise linearization. Compared kernel component analysis (KPCA)-based method, popular unknown high-dimensional space, has better because it eliminates effects original data space prevent excessive elimination information related structural damage. Moreover, also avoids disadvantage some which partition observations structure. Finally, numerical examples monitoring from an actual bridge are validate effectiveness applicable method.
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