A Sliding Windows Singular Decomposition Model of Monitoring Data for Operational Tunnels

Abstract

In order to extract the valuable information from massive and usually unstructured datasets, increasingly, a novel nonparametric approach is proposed for detecting early signs of structural deterioration in civil infrastructure systems from vast field-monitoring datasets. The process adopted six-sample sliding window overtime at one-hour time increments to overcome the fact that the sampling times were not precisely consistent at all monitoring points. After data processing by this method, the eigenvalues and eigenvectors were obtained for each moving window, and then an evaluation index was constructed. Monitored tunnel data were analyzed using the proposed method. The required information extracted from an individual moving window is represented by a set of principal components, which become the new orthogonal variables. The resulting evaluation indicator was strongly correlated with measured and calculated values up to 0.89, even for tiny monitoring datasets. Experiments have verified the rationality and effectiveness of the algorithm, which provides a reference for the application of the method in the monitoring data processing.

Keywords

• tunnel deformation
• big data
• principal component analysis
• data-driven
• orthogonal decomposition