Integrating machine-learning techniques in structural digital twins

Details

Digital twins are an emerging tool for the modelling and managing of structures. They are considered models which evolve in time together with the structure and it is desired that they integrate different types of data in the modelling procedure. Thus, the use of data-driven models which can deal with different types of data as part of a digital twin becomes a necessity. The current project will examine how different types of machine-learning algorithms and models could be integrated within a structural-digital-twin framework to provide more accurate predictions about the future of the structure. The framework shall aim at building tools which can interact with other parts of the digital twin (e.g. various datasets, different types of models, etc.) and can inform the analysers regarding the current condition or the future behaviour of a structure. The considered tools could span from simple machine- learning models to more complicated and recently-developed deep learning algorithms, which have proved to be quite successful in various fields of science.

The project aims at evaluating the performance of different machine-learning methods for the definition of digital twins. The methods will span from simple techniques to more complicated deep-learning techniques and the main objective is to integrate different types of data and even various types of models in the inference procedure.

During this project, you will gain significant experience in:

·        Signal processing for structural modelling

·        Analysing datasets from data acquired from structures

·        Machine learning techniques for digital twins

·        Presentation skills

·        Working in a multidisciplinary/international environment

Requirements:

Education A very good 4-year/master degree in Mechanical, Aeronautical, Marine, Civil, Chemical Engineering, Computer Science, Applied Mathematics or Physics (at least a UK 2:1 honours degree).

The successful candidate will work under the supervision of Dr George Tsialiamanis and will be part of a vibrant team of PhD students and RAs, the Dynamics Research Group. The group includes people working in different areas of structural dynamics and machine learning and provides the opportunity to its members to define their own path in research and at the same time to collaborate with other members