Climate-resilience and sustainability optimisation in transport infrastructure adaptation at University of Birmingham on FindAPhD.com

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Dr Stergios-Aristoteles Mitoulis , Dr Jelena Ninic Applications accepted all year round Competition Funded PhD Project (Students Worldwide)

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Birmingham United Kingdom Civil Engineering Climate Science Geotechnical Engineering Structural Engineering

About the Project

The candidate may have any background in infrastructure and/or environmental engineering and will have an understanding of hazards, such as climate and/or human induced hazards and stressors. Such stressors could include flooding and scour, foundation settlements, corrosion, explosion as a result of attacks and/or other accidental loading. To this end multihazard scenarios and climate projections will be analysed and assessed. The main scope of this PhD is to process openly available digital data from e.g., UAV-enabled mapping, GIS, satellite imagery and/or forensic evidence to develop optimised resilience and sustainability solutions for assets, such as bridges or other critical infrastructure. Scenarios of proactive (ex-ante) and reactive (ex-post) adaptation of critical infrastructure will be evaluated to make the case for resilient infrastructure that contributes to Net-Zero.

Funding Notes

Funding: Competitive. The successful candidate will be able to have a salary top-up if they are happy to be seconded to another collaborative University and help towards the delivery of ongoing research projects (see e.g., View Website).

References

Loli M, Kefalas G, Dafis S, Mitoulis SA, Schmidt F (2022). Bridge-Specific Flood Risk Assessment of Transport Networks Using GIS and Remotely Sensed Data. Science of the Total Environment. Vol. 850, 157976. https://doi.org/10.1016/j.scitotenv.2022.157976
Mitoulis SA, Argyroudis S, Loli M, Imam B (2021). Restoration models for quantifying flood resilience of bridges. Engineering Structures, 238, 112180 https://doi.org/10.1016/j.engstruct.2021.112180
Huang, M. Q., Ninić, J., & Zhang, Q. B. (2021). BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives. Tunnelling and Underground Space Technology, 108, 103677.