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Multiscale modelling of bacteria-based self-healing bio-concrete

Summary

The aim of the project is to develop a multiscale framework for understanding and optimising the self-healing of bacteria-based bio-concrete under sustained loads

Keywords:

self-healing concrete; microbiologically induced calcium carbonate precipitation; multiscale modelling

Supervisor

Professor Luming Shen.

Research location

Civil Engineering

Synopsis

Self-healing concrete, which uses bacteria as a means to repair, has the potential to revolutionise the construction industry and reduce the annual cost of repair and maintenance by billions of dollars. To unlock its potential, we need to understand the mechanisms of bacterial self-healing and reliably predict its performance. This project will develop a multiscale model to describe the competing mechanisms between crack widening and healing at the macroscale, incorporated with key information of substances diffusion and bacteria-assisted reactive transport process of bio-cementation at the meso- and nano-scale. This will enable the prediction of the performance of self-healing concrete structures and optimise performance and enhance the durability of structures under sustained loads. Theoretical and computational advances of this project will include a multiscale framework for close-to-reality prediction of the coupled mechanical, hydraulic, chemical and bio-phenomena from nano to macro scales.

The following specific aims are set out:

  • develop a multiscale framework that can model the complex interactions between crack initiation and propagation, diffusion of substances, and bio-cementation from molecular to continuum scales,
  • utilise the multiscale framework calibrated and validated by the experimental data to reveal the underlying mechanisms between crack widening and healing in bio-concrete structures under sustained loads.

Additional information

How to apply:

To apply, please email [email protected] the following:

  • CV
  • Transcripts

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Opportunity ID

The opportunity ID for this research opportunity is 3558

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