Improving risk and reliability assessments of networked infrastructure
using advanced graphical models
Application deadline: 31 May 2014
Award type: PhD
Start date: As soon as possible
Duration of award: Three years
Eligibility: UK/EU
Apply now
Supervisors
Dr Alireza Daneshkhah - Lecturer in Utility Asset Management
Prof. Paul Jeffrey
Supported by
EPSRC and UKWIR CASE Industrial PhD Studentship of up to £18,000 p.a.
for three years plus UK/EU tuition fees* is available.
Overview
This is an EPSRC Industrial CASE Studentship which is jointly funded
by the EPSRC and UKWIR (UK Water Industry). This project will examine
how the performance of water distribution networks (which are normally
affected by many factors, including physical, operational, maintenance
and environmental conditions as well as the conditions in and around
the distribution network) can be improved by using the advanced
graphical models (including Bayesian networks, dynamic Bayesian
networks etc)
Many of these factors exhibit dependencies which are poorly understood
or represented in risk assessment and system reliability analyses. In
this multidisciplinary study, we aim to use these models to tackle
this issue and improve the accuracy of corresponding risk and
reliability assessments, and preventive maintenance to manage these
assets more efficiently.
The study will be conducted in close cooperation with the water
industry, and the student should work closely with the water industry
engineers/experts in some stages of the study.
The relevant areas of this study are Bayesian statistics, graphical
models, data mining, Machine learning, reliability analysis, risk
analysis and utility asset management.
For initial enquiries please contact Dr. Alireza Daneshkhah
([log in to unmask]).
If you are eligible to apply for this research studentship, please
complete the online application form.
Applications are not accepted by email
Please specify project title and reference number on the application form
Early applications are encouraged.
For further information contact us today:
School of Applied Sciences
T: +44 (0)1234 754086
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