A new regime-switching cointegration method for structural health monitoring under changing environmental and operational conditions

Traditional vibration-based detection methods seek to detect structural damage based on changes in dynamic features, which assume that changes in dynamic features are solely caused by structural damage. However, environmental and operational variations (EOV) significantly influence structural dynamic features, and these changing conditions can cause nonlinear behavior in the dynamic features. A novel regime-switching cointe-gration method for removing nonlinear environmental influences is proposed to address this concern. In this method, the conventional Johansen cointegration is extended to a nonlinear context, which allows to establish the switching cointegrating relationship between the damage features via a switching point. A method based on principal component analysis (PCA) and Gaussian mixture model (GMM) is used to determine the appropriate switching point. Compared with the method of determining the switching point using ADF statistics, the pro-posed method is simpler and more computationally efficient because it determines the switching point only through one cluster analysis. Finally, the effectiveness of the proposed method is validated using a 7-DOF nu-merical example and monitoring data from the Z24 Bridge, the results show that damage occurrence can be accurately detected and nonlinear EOV effects can be successfully removed.