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PhD Studentship: Resilience-based Management of Transport Infrastructure Enhanced by Digital Data and Artificial Intelligence (AI)

University of Birmingham - Department of Civil Engineering

Qualification Type: PhD
Location: Birmingham
Funding for: UK Students, EU Students, International Students
Funding amount: Funding: Competition-based
Hours: Full Time
Placed On: 7th December 2022
Closes: 7th March 2023

The candidate may have any background in civil and/or structural engineering and will have an understanding of digital data and value of digital twins, and how to process data using streamlined Artificial Intelligence methods for solving engineering problems. The main scope of this PhD is to update advanced FE models based on data obtained from disparate sources e.g. digital twins, point clouds and/or other forensic evidence, obtained for example from inspections, to improve the predictive capacity of models of transport assets and/or networks, such as bridges, roads and/or railways in support of resilience-based decision making. These models will enable condition prediction and probability of failure for different hazard scenarios, e.g. climate projects and/or anthropogenic stressors, such as explosions as a result of terror attacks and similar types of hostility attacks. The research will deliver a toolkit for classifying assets based on their performance and ultimately facilitate decision-making in view of the asset accelerated deterioration due to climate chance and High Intensity Low Probability events.

The project will be supervised by Dr Stergios-Aristoteles Mitoulis & Dr Asaad Faramarzi.

References:
Mitoulis SA, Bompa DV, Argyroudis SA (2022). Sustainability and resilience trade-offs in post-disaster bridge recovery: floods and climate projections. Transportation Research Part D: Transport and Environment (in review). Available at: https://ssrn.com/abstract=4151393
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
Dabiri, H., Faramarzi, A., Dall’Asta, A., Tondi, E., & Micozzi, F. (2022). A machine learning-based analysis for predicting fragility curve parameters of buildings. Journal of Building Engineering, 62, 105367.

Funding Details

Funding: Competition-based. 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., http://www.infrastructuresilience.com/recharged).

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