Anomaly detection for dike monitoring using system identification

Structures such as seawalls, levees and dikes prevent low lying land from flooding. The structural health of these constructions is critical and needs to be maintained. In this paper, we present a data-driven approach that uses the information of different in-situ measurements to detect structural anomalies at an early stage. Our approach is based on system identification, in which the dike is modeled as a single-input, multiple-output, linear system whose parameters can be learned based on training data. A statistical test is then deployed to perform a systematic detection of anomalies. We demonstrate the performance of the proposed approach on real data from an experimental dike setup.