Time-Frequency Methods for Structural Health Monitoring

Abstract

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).

Russian-Dutch double-degree Master’s programme in computational science in the age of global education

“…Three PhD students, who have been doing a collaborative research under a joint supervision and joint funding from UvA and ITMO [24], [36], [37] [38], [39], [40], defended their PhD theses 14 in September 2014, but only in UvA. The reason is that under the current Russian regulations, the future doctors in ITMO University have to pass 3 exams, translate their theses into Russian language and go through several additional bureaucratic procedures.…”

Combining Data-Driven Methods with Finite Element Analysis for Flood Early Warning Systems

“…Points that belong to the clusters of normal behaviour produce high confidence values of normal behaviour (close to 1). More details can be found in [15], [16] and [17].…”

Value of Information of Structural Health Monitoring in Asset Management of Flood Defences

“…However, the focus was generally on technological development of techniques rather than assessing their value in a decision support context. The specific focus of a SHM system depends on the type of flood defence considered: for flood defences with relatively low reliability requirements, the aim is typically to provide insight into anomalies and system responses during (frequently occurring) crisis situations [22]. For flood defences with relatively high reliability requirements, such observations will be rare, and SHM systems will be mostly aimed at reducing uncertainty in reliability estimates [23,24].…”