Development of digital twins linked to risk-based monitoring of structural systems

The performance of structural systems depends on appropriate quantification of uncertainties associated with the loading, geometric and material characteristics. Such quantification requires robust information on random variables, which is usually based on experimental studies, periodic inspections, or historical behaviour, considered together with expert inputs where relevant. With the advent of modern structural health monitoring systems, it is possible to develop approaches to closely follow the structural state remotely and dynamically. This offers unique opportunities to study the underlying reliability by classifying the primary uncertainties, including the inspection related factors. Such an approach can then form the basis for optimising the risk-based monitoring decisions for pre-defined target performance. An effective integration of the chosen structural system with the corresponding virtual representation (digital twin) will greatly assist in automating the risk-based decision process.

This research will aim to enhance the fundamental understanding in developing digital twins of structural systems by developing novel approaches for integrating the stochastic deterioration models. The proposed project will utilise multi-scale uncertainty characterisation linked to suitable surrogate (machine learning) models to develop computationally efficient system representation. The project will also consider appropriate probability representation of structural damage states based on the available data streams. The research will focus on a chosen safety critical structural system, to be agreed before the PhD commences.

Selection will be made on academic merit. The successful candidate should have (or expect to achieve) a minimum of a UK Honours degree at 2.1 or above (or equivalent) in Aerospace/ Civil / Marine/ Mechanical engineering.

Essential knowledge: Structural mechanics, numerical analysis, computer programming, optimisation, structural reliability.

APPLICATION PROCEDURE:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php

• Apply for Degree of Doctor of Philosophy in Engineering

• State name of the lead supervisor as the Name of Proposed Supervisor

• State ‘Self-funded’ as Intended Source of Funding

• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)

• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding

Informal inquiries can be made to Dr S Sriramula (s.sriramula@abdn.ac.uk) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School (pgrs-admissions@abdn.ac.uk)