Big Data Analytics for Monitoring-Based Management of Long-Span Bridges - Engineering/Computer Science - EPSRC DTP funded PhD Studentship Ref: 2912

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Supervisors
Dr. Prakash Kripakaran

Dr. Ki Koo

Prof. Richard Everson

Location 
Streatham Campus, Exeter

Project Description


Motivation: Many iconic long-span bridges around the world are now being equipped with sophisticated Structural Health Monitoring (SHM) systems with capabilities for recording several types of response (e.g. strain, acceleration), load (e.g. wind speed, vehicle load) and environmental parameters (e.g. temperature) at various bridge locations. The fundamental purpose of having such systems is to enable effective management of these critical infrastructure assets in order to provide a safe and disruption-free transport network. However, at the moment, there are no reliable approaches for linking the large amounts of data from monitoring to structural performance. This research envisions coupling state-of-the-art SHM systems with Big Data technologies for analysing and visualizing the enormous amounts of data generated by monitoring.  

Research aims: The aims are to develop

1. a suite of data-driven strategies for data fusion and integrated analysis of heterogeneous measurement histories in order to relate raw measurements to structural performance-related parameters, and 

2. a visualization platform that integrates recent developments in visualization of multi-dimensional data with Building Information Models (BIM) in order to enable engineers to view and navigate results from data analytics.

Research approach: This research will differ from previous work by investigating data-driven methods of integrating the individual effects of all loads (e.g. vehicle, wind, temperature) applied to a bridge at the time of measurement, and also immediately prior to it. It will also focus on using BIM for visualizing results from data interpretation in order to ensure that the developed data-driven methods are useable for translating data into decisions. The work will draw upon the Vibration Engineering Section’s extensive experience in structural health monitoring of bridges. The study will also have access to live SHM data from full-scale long-span bridges in the UK, and will also actively collaborate with long-span bridge operators, bridge owners and companies involved in SHM.

Entry Requirements

You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in [engineering, computer science]. Experience in [data mining and/or structural management] is desirable.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests.
 

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.

Summary

Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral Collegepgrenquiries@exeter.ac.uk

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
        transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see
        http://www.exeter.ac.uk/postgraduate/apply/english/.

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email: pgrenquiries@exeter.ac.uk.
Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.