Failure prognosis for complex offshore structural systems through ultrasound and vibration measurements


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Modern industrial structures are increasingly complex in terms of geometry and materials. More specifically, the usage of composite and complex additively manufactured structures implies a greater range of possible structural damage modes for which the structure is required to be frequently and thoroughly inspected.

The massive cost implied by scheduled inspection techniques has led to intense industrial interest towards novel structural health monitoring (SHM) technologies which are by definition ‘automated’ and ‘online’.

A structure having permanently attached sensors however with the required accompanied power units, cables and processing equipment significantly adds to the complexity and cost of the holistic system, sometimes even resulting in technologies focusing on monitoring the SHM equipment. With the intense development and reduced cost of aerial and underwater unmanned vehicle (UV) technologies, a large portion of the SHM research is focusing on structural monitoring without permanently attached equipment. The initial attempts have focused on visual inspection for surface-visible damage, with sensing approaches that can provide more accurate damage information (ie ultrasound and vibration) not having being investigated.

This research and training program will investigate for the first time if UV measurements (both aerial and underwater) can provide accurate damage identification capability, or if the additional measurement uncertainty implied by the nature of removable sensors destroys the valuable information in the data. The developed tools will lead to robust structural damage localization and identification, as well as to effective estimation of major reliability indices such as the Remaining Useful Life (RUL) for a given component having no integrated sensing and actuation equipment.

Project goals

The goal is to deliver novel fast, robust, and rigorous methodologies for:

  • Predicting the UGW response on a structure subject to harsh offshore environments.
  • Detecting the presence of damage while identifying its type and size from raw UGW data.
  • Predicting the residual life of a structure subject using probabilistic DL.

The results from this research project will enhance the SHM technologies and boost the renewable energy’s cost efficiency by reducing monitoring costs and increasing availability for offshore wind and marine structures. In particular, this project will address and answer the following novel research questions:

  1. Can UGW and vibration signals be excited through UVs and how can the variability impact of extreme weather on wave propagation characteristics be efficiently predicted and eliminated?
  2. Can customised probabilistic DL tools accurately and efficiently estimate the presence and characteristics of damage in composite energy structures?

Supervision team

The University of Melbourne: Associate Professor Lihai Zhang

KU Leuven: Associate Professor Dimitrios Chronopoulos

*Click on the researcher's name above to learn more about their publication and grant successes.

Who we are looking for

We are seeking a PhD candidate with the following skills:

  • Demonstrated experience in the field of mechanical or infrastructure engineering.
  • Demonstrated experience with scientific computation.
  • Demonstrated ability to work independently and as part of a team.
  • Demonstrated time and project management skills.
  • Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date).
  • Excellent written and oral communication skills.
  • Demonstrated organisational skills, time management and ability to work to priorities.
  • Demonstrated problem-solving abilities.

Further details

The PhD candidate will benefit from the combined expertise of the project supervisors, and the embedding into two research environments.

Associate Professor Dimitrios Chronopoulos is an expert in ultrasound and vibration based digital-twin methodologies. These methodologies aim at identifying the health state of a structural system by quantifying and localising damaged areas on the system. His work mainly revolved around modelling ultrasound interaction with defects in complex composite and tessellated (3D printed) structures. A set of simulated signatures are numerically extracted for each assumed damage type. These signatures are then projected on experimental results in order to identify damage. A/Prof Chronopoulos will provide training elements to No2Failure related to: ultrasound modelling in complex structures and Bayesian methodologies for system identification.

Associate Professor Lihai Zhang is an expert in porous media mechanics and engineering reliability. By leading an Infrastructure Asset Protection & Management research group at The University of Melbourne, his research work mainly focuses on reliability-based life-cycle assessment of built infrastructure, building cladding subject to hailstorm impact, corrosion of concrete in marine environment, and structural health monitoring using non-destructive field testing methodology and unmanned vehicles (UV). His damage propagation expertise can be coupled as a natural extension to damage identification methodologies of KU Leuven to result in a holistic scheme for estimating the Remaining Useful Life (RUL) for a structural component. A/Prof Zhang will provide training elements to No2Failure related to Damage initiation and propagation under defined loading envelopes in composite structures and) Acquiring monitoring data through non-permanently attached sensors and through unmanned vehicles (UVs).

This PhD project will be based at KU Leuven with a minimum 12-month stay at the University of Melbourne.

The candidate will be enrolled in the PhD program at the Faculty of Engineering Technology at KU Leuven, and in the PhD program at the Department of Infrastructure Engineering at the University of Melbourne.

To apply for this joint PhD opportunity, and to view the entry requirements, visit How to apply.

First published on 4 February 2022.

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