Identifying the outlier in tunnel monitoring data: An integration model

Abstract

Structural health monitoring (SHM) system based on the Internet of Things is an important method to evaluate the safety of tunnel operation through the real-time monitoring data analysis. Identifying the outlier in SHM data is a non-trivial task but it is challenging for the tunnel engineers because the measurements are quite complicated with the characteristics of time series, unlabeled, high-dimensional, inter-correlations between variables, etc. To detect the outliers, an integration model is developed based on the independent data analysis from the Probabilistic, Proximity-Based (global), Proximity-Based (local), Linear Model and Outlier Ensembles. The model is examined with the shuttle data set in the University of California Irvine (UCI) database and its precision rate is up to 94.5%, highlighting the favorable performance in identifying the outliers. This method is thus applied to the outlier detection of the SHM data in the Nanjing Yangtze River tunnel. 6698 data sets collected from SHM are evaluated and 270 groups of outliers are identified effectively. By eliminating these outliers, comparisons between the proposed integrated model and the single model (i.e. IForest, ABOD, KNN, LOF) are further conducted to discuss the model performance based on the regression analysis. Results show that the integrated model is better than the single model and it possesses the great potential to detect the outliers in SHM system.