Institute of Risk http://www.liv.ac.uk/risk-and-uncertainty PhD Project ...

Name of Project: Efficacious Signal Processing Techniques for Risk Assessment of Civil
Infrastructure subject to Natural Hazards
Supervisor (s): Ioannis Kougioumtzoglou (Engineering) and Michael Beer (Engineering)
further co-supervision depending on student’s background and interest
Outline of Project:
Structural systems are often subject to natural and man-made hazards and to extreme
events due to climate change, such as seismic motions, winds, ocean waves, impact and
blast events, hurricanes, storms and floods, which inherently possess the attribute of
evolution in time. Further, structures can become nonlinear and inelastic with restoring
forces depending on the time history of the response under severe time-varying excitation
(i.e. exhibit hysteresis). Therefore, representation of these complex phenomena by nonstationary
stochastic processes is necessary to capture accurately the system/structure
behaviour. Although several research efforts have focused on determining the response of
nonlinear systems, limited results exist in the context of a joint time-frequency analysis.
Further, it is noted that the envisaged research project is largely motivated by the evident
changes in our environment and their consequences (see the recent Stern report). In this
context, extreme events related to climate change can cause major losses, and thus, the
issue of risk/uncertainty quantification arises in many activities. It may emanate from an
inevitably incomplete understanding of infrequent climate change events to failure in
recognising what fragilities might exist in systems/structures. These extreme events have
increased the exposure of public and private services to risk. As a consequence, decision
makers are challenged to assess exposure to risk and to identify risk emergent events.
In this research project, the available database related to natural hazards will be utilized to
translate the raw data into useful probabilistic engineering excitation models. To this aim,
advanced signal processing methodologies will be developed for determining the content
and quantifying the uncertainty of (most often) irregularly sampled data with missing
observations. Further, joint time-frequency response analyses will be performed based on
the localization capabilities of the wavelets and of other newly proposed potent transforms.
Subsequent applications will include risk quantification and reliability assessment of
buildings, bridges and related infrastructure utilities following complex hysteretic behaviour
and subjected to natural hazards such as extreme events due to climate change (e.g. storms,
floods and hurricanes).
Any special features: (e.g. equipment, collaboration, industrial links, underpinning
expertise)
The project requires a strong mathematical background and a keen interest to work in the
interface of signal processing and probabilistic engineering mechanics. Applicants should
have a background in Engineering (preferably Civil, Mechanical, Aerospace or Naval/Marine
Engineering) or in relevant fields from Mathematical Sciences. A combined education in
these fields would be an advantage.
The student will join a multi-disciplinary research group in the Institute for Risk.