Postgraduate research opportunities Data-driven structural health monitoring through AI-enhanced stochastic model updating and parametrisation

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Key facts

  • Opens: Tuesday 8 March 2022
  • Deadline: Monday 31 October 2022
  • Number of places: 1
  • Duration: 3 years
  • Funding: Home fee, Travel costs, Stipend

Overview

This project aims at a complete framework of Structure Health Monitoring with the aid of precise and robust models calibrated by stochastic model updating, whereby the robust and real-time features will be ensured by the AI techniques based on a database of various damage data.
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Eligibility

  • mathematics background (especially probabilistic and statistical approaches)
  • engineering mechanics and structural dynamics
  • computational intelligence techniques
  • finite element analysis and software skills
  • familiar with MATLAB and other programming tools such as C++ or Python.
THE Awards 2019: UK University of the Year Winner
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Project Details

Model updating [1] has been developed as a typical topic to calibrate the parameters or the model itself to turn the model prediction towards the experimental measurements. However, it is widely recognised that the unavoidable uncertainties in both experiments and simulations must be understood in model updating. Non-deterministic modelling approaches enable characterisation, propagation, and quantification of uncertainties, providing predictions over a possible range of outcomes rather than a unique solution with maximum fidelity to a single experimental observation.

Structural Health Monitoring (SHM) plays a significant role in providing insight into the structural properties in the life-circle of produces. Model updating has a natural connection with the topic System Identification, implying the inherent properties of the physical system can be identified (or predicted) from the numerical modelling. This brings SHM and model updating together in this project: to develop a reliable numerical model with a precise indicator to support decision-making in SHM.

For uncertainty treatment, a significant aspect is the advanced data technology to extract as much as possible uncertainty information from the available experimental data, to enhance the data-driven decision-making process. From the applicant’s recently organised special issue on advances of model updating , it is surprising to find that Artificial Intelligence (AI) techniques are absent.

This has been caused by two challenges:

  1. how to avoid non-unique solutions when the AI-based agent model is employed in model updating
  2. how to implement AI in the presence of gross data with multi-source of uncertainties

This project is consequently aimed at filling the gap between the popular AI techniques and their application to SHM and model updating with novel approaches of uncertainty treatment.

[1] Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment, pages. 115–129. 

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Supervisors

Dr Sifeng Bi

Strathclyde Chancellor's Fellow
Mechanical and Aerospace Engineering

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Dr Andrew Hamilton

Data Analytics Theme Lead
Digital Factory

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Number of places: 1

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Mechanical and Aerospace Engineering

Programme: Mechanical and Aerospace Engineering

PhD
full-time
Start date: Oct 2022 - Sep 2023