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  IMPACT-RISE: Infrastructural surrogate Modelling using Physics-informed And interpretable machine learning for CommuniTy ResIliency and Sustainability Evaluation.

   College of Engineering, Mathematics and Physical Sciences

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  Dr Jawad Fayaz, Dr E Keedwell  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Project Description

The IMPACT-RISE project is a pioneering initiative that seeks to revolutionize the field of community resiliency and sustainability analysis through a machine learning (ML) and explainable artificial intelligence (XAI) outlook. The project marks a significant advancement in improving public safety against both low-probability high-impact events and high-probability events with long-term impacts. It focuses on the development of state-of-the-art infrastructural surrogate models using physics-informed and interpretable ML techniques. Our aim is to comprehensively analyse and mitigate the risks posed by diverse extreme events, both natural and anthropogenic (including earthquakes, floods, storms, climate change), on built environment. The primary goal is to enhance our understanding and predictive capabilities, thereby improving decision-making processes to effectively reduce the impact of these hazards on infrastructure systems.

Central to IMPACT-RISE project is the development of data-driven deep learning (DL) based surrogate models that simulate the complex behaviours of infrastructure systems under conditions posed by various hazards (occurring independently and concurrently). These models will be trained while appropriately infusing physics (such as structural dynamics), ensuring not only high accuracy but also enhanced interpretability – a crucial factor for decision-makers in risk management and emergency response. To further boost the interpretability of the DL based surrogate models, principles of explainable artificial intelligence (XAI) will be integrated for a deeper understanding of the models' decision-making processes. Working on the project involves the meticulous collection, development, and analysis of diverse infrastructural and hazard related data sets, ranging from historical incident records to real-time infrastructural sensor data, community maps, and more. Furthermore, the project requires augmentation of real recorded data with simulation data obtained through structural finite-element modelling and analyses.

IMPACT-RISE project aims to provide accurate, reliable, and accessible models, thereby playing a pivotal role in fortifying community resilience and sustainability against various hazards. These innovative tools will be instrumental in pinpointing vulnerabilities, optimizing resource distribution, and crafting effective emergency response plans. IMPACT-RISE is grounded in collaborative effort, bringing together a diverse team of specialists in machine learning, civil engineering, and risk analysis. We are committed to align our models with the practical realities and unique challenges of different communities. Through this integrated and cooperative approach, IMPACT-RISE is set to establish new standards in community protection and infrastructure resilience, confronting the diverse challenges of the 21st century with advanced technological solutions and strategic insights.

Key Research Aims

• Development and validation of accurate and efficient physics-informed ML/DL surrogate models of infrastructural systems.

• Simulation of infrastructural behaviour through advanced hazard modelling and finite-element modelling and analysis.

• Extensive data analysis of infrastructural responses to various extreme events for community resiliency and sustainability assessment.

• Integration of multidisciplinary approaches for enhanced predictability and reliability of trained DL models.

• Utilize principles of XAI to develop interpretability tools for the trained DL models.


• Conduct cutting-edge research in physics-informed ML/DL, Bayesian statistics, and XAI.

• Obtain and innovatively implement knowledge in advanced structural analysis, hazard analysis, and finite element infrastructural modelling.

• Collaborate with internal/external interdisciplinary teams, including engineers, data scientists, and risk analysts (as required).

• Publish findings in high-impact journals and present at international conferences.

• Engage in departmental activities and contribute to broader research goals.

Computer Science (8) Engineering (12)

Funding Notes

This award provides annual funding to cover Home tuition fees and a tax-free stipend. For students who pay Home tuition fees the award will cover the tuition fees in full, plus at least £18,622 per year tax-free stipend. International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

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