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PhD Studentship: Embracing Physics and Uncertainty for Long-Term Structural Monitoring and Life Extension

The University of Edinburgh

Qualification Type: PhD
Location: Edinburgh
Funding for: EU Students, International Students, Self-funded Students, UK Students
Funding amount: Not Specified
Hours: Full Time
Placed On: 15th December 2023
Closes: 7th March 2024

To date, there has been limited investigation of how the differences between assumed and foreseeable conditions translate into the potential for extended operating lifetime of structures. Consequently, it is difficult to assess the probability of the assumed life extension being achieved, particularly as life-limiting factors are likely to differ between projects and designs. This is a very important aspect in different engineering applications for the adoption of a new circular economy action plan that aims to ensure that the resources and structures are kept operational as long as possible.

The rationale is that integrated continuous monitoring systems allow learning from the past to decide in the present about the structural integrity (short- and long-term diagnosis), and to predict in the future the remaining useful life (long-term prognosis). In recent years, research on Structural Health Monitoring (SHM) has sought for solutions to close the loop between designing, manufacturing, building, and maintaining structures driven by continuous measurements of structural data. However, reliable higher-levels in the SHM hierarchy (i.e. quantification and estimation of the useful remaining life) need further developments. In particular, they are only possible by merging physics-based models that underline the mechanics of the time-variant evolution of the structure with data measurements from the operational structure.

In practice, there are two main Challenges:

  1. Robust extraction of dynamical features (DF) for continuous monitoring which are insensitive to Environmental and Operational Variabilities (EOVs). These DF should be interpretable during the entire evolution of the structural performance and they should be able to accommodate dimensionality and complexity reduction of their associated non-linear time-variant nature.
  2. And there is a need of developing measures to quantify and propagate uncertainty towards the estimation of the remaining useful life on structures.

The main aim of the project is to reformulate the current techniques to make robust and reliable long-term monitoring by accommodating hybrid models to describe better the time-variant evolution of structural engineering systems. Moreover, the physics-based model will guarantee that predictions made, at future stages of the structure, will adhere to known underlying physical laws of the system model evolution. In particular, the project will evaluate the level of complexity needed of the physics-based models by means of different model order against their capabilities for long-term monitoring. In order to address the many challenges of the project, we shall adopt the following objectives:

O1 - Development of a methodology that can facilitate the extraction of adaptive features for robust continuous and automated monitoring adequate for a time-variant structure evolution.

O2 - Incorporate principled means of inference (physical principles) that can facilitate the interpretability of dynamic features on structures with unknown excitation and non-stationary dynamics.

O3 - Evaluate the contribution of including prior knowledge of the physics-based model coupled with continuous data measurements for predicting future states of the structure and estimation of remaining useful life.

This computational project is supervised by Dr David Garcia Cava (School of Engineering, University of Edinburgh). It will involve regular interaction with collaborators from academia and industry. Interested candidates may contact the supervisor for further information (david.garcia@ed.ac.uk).

Further Information: 

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Closing Date: Thursday, March 7, 2024

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