Artificial intelligence and finite element modelling for monitoring flood defence structures

We present a hybrid approach to monitoring the stability of flood defence structures equipped with sensors. This approach combines the finite element modelling with the artificial intelligence for real-time signal processing and anomaly detection. This combined method has been developed for the UrbanFlood early warning system and successfully tested on a large-scale sea dike during a simulated strong storm with very high water level. The artificial intelligence module detects the onset of dike instability after being trained on the data from the Virtual Dike finite element simulation

Flood early warning system: sensors and internet

The UrbanFlood early warning system (EWS) is designed to monitor data from very large sensornetworks in flood defences such as embankments, dikes, levees, and dams. The EWS, based on the internet, uses real-time sensor information and Artificial Intelligence (AI) to immediately calculate the probability of dike failure, the ensuing scenarios of dike breaching, predicted flood spreading and escape routes for people from the affected areas. Results are presented on interactive decision support systems that assist flood defence managers and public authorities during flood events. It can also be applied for policy development and for everyday dike condition assessment. The separate Virtual Dike module can be used for advanced research into failure mechanisms and dike stability. By consulting international stakeholders the designers ensured that the EWS is well aligned with user requirements

Interpreting sensor measurements in dikes - experiences from UrbanFlood pilot sites

The UrbanFlood project is creating an Early Warning System framework that can be used to link sensors via the Internet to predictive models and emergency warning systems. The project includes four pilot sites to apply and validate at full scale the technology being developed in the project: Amsterdam (Netherlands), Boston (UK) and Rhine River (Germany). This paper focuses on a description of the sensor instrumentation installed at the pilot sites and the emerging conclusions from the analysis of the results obtained to date. The sensors installed at the various sites include various MEMS modules to measure displacement and pore pressure and fibre optic cables able to detect strains. The gathered data are used for dike stability evaluation with different models and also, combined with an Artificial Intelligence (AI) component, for detection of anomalies in dike behaviour. Detected anomalies trigger assessment of the likelihood of levee breach and the consequences in terms of flood propagation and damage in the defended urban area

Time-Frequency Methods for Structural Health Monitoring

Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM) of flood protection systems (levees, earthen dikes and concrete dams) using sensor data. We present a robust data-driven anomaly detection method that combines time-frequency feature extraction, using wavelet analysis and phase shift, with one-sided classification techniques to identify the onset of failure anomalies in real-time sensor measurements. The methodology has been successfully tested at three operational levees. We detected a dam leakage in the retaining dam (Germany) and “strange” behaviour of sensors installed in a Boston levee (UK) and a Rhine levee (Germany)