Detecting the damage of bridges under changing environmental conditions using the characteristics of the nonlinear narrow dimension of damage features

Abstract

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