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Enhancing Urban Flood Resilience with Innovative Digital Design Technologies (Advert Reference: RDF22-R/EE/MCE/FENG)


   Faculty of Engineering and Environment

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  Dr Haibo Feng  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

In England, 3.2 million households are located in areas at risks of urban flooding, with annual damages exceeding £300 million. Due to the extreme environmental conditions, unprecedented downpours across the country in recent years further increases the flooding events on urban road network (URN). Flooding of a small part of the network can result in many thousands of people being stranded and in multi-million damages as experienced on the urban area in recent years.

Flood resilience aims to minimise the damage during the flooding event, reduce the costs for flood recovery, and efficiently deploy recovery measures. Available studies have investigated how to define, measure, and assess the flood resilience from different perspectives. However, there is still a dearth of: (1) studies proposing operational frameworks, enabled by emerging digital technologies, for minimising flood impact and achieving resilience targets through improved monitoring and reporting; (2) limited evidence of wider testing of proposed solutions at ‘network’ level – testing is often performed in specific or siloed case studies – involving a ‘multi-agency’ approach for sharing information about flood reporting and monitoring; and (3) lack of integration of proposed solutions within legacy systems of government agencies (i.e. Northumbrian Water) and their infrastructure strategy’s key performance indicators.

The aim of this research is twofold: to develop an operational framework, enabled by digital technologies (e.g. Internet of Things (IoTs), Drones, Satellites, cloud computing) and data-analytics methods, for improving infrastructure resilience through enhanced monitoring and reporting of flood events on the URN; to develop and test a novel tool for prediction of flood on the URN that uses both existing infrastructure data and data generated from the deployment of the operational framework. The Industrial partners (BIM Academy and Northumbrian Water) will be actively involved to provide industrial resources and expertise, as well as infrastructure data.

 A group of academic expertise are gathered from various departments for this proposal. Prof. Kassem Mohamad is a professor with expertise on digital technologies and BIM applications in construction. Prof. Shengfeng Qin is a professor from School of Design, and he was involved from the beginning as the expertise on IoT monitoring system design for sewage system. Dr. Vikki Edmondson from Civil Engineering provided knowledge on flooding issues and urban road structures in the UK based on her work experience and research connections. Dr. Lanlin Jin is a sensor device expert from Computer Science provided supports on the technical information of various sensors and monitoring network design. With the supervision team, the fundamental knowledge on flooding issues monitoring, and the proposed sensor systems were defined and established with the support of the pump-priming fund.

A few industrial partners are ready to participate based on the work done in the summer. Northumbrian Water will provide infrastructure support to ensure our proposed devices are able to be tested. BIM Academy and Environmental Monitoring Solutions (EMS) will support on the development of the monitoring system for commercialisation. Costain Group plc will provide financial support on the research of road flooding issues for their future implementation.

The Principal Supervisor for this project is Dr Haibo Feng.

Eligibility and How to Apply:

Please note eligibility requirement:

  • Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere or if they have previously been awarded a PhD.

For further details of how to apply, entry requirements and the application form, see

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF22-R/…) will not be considered.

Deadline for applications: 20 June 2022

Start Date: 1 October 2022

Northumbria University takes pride in, and values, the quality and diversity of our staff and students. We welcome applications from all members of the community.

Funding Notes:

Each studentship supports a full stipend, paid for three years at RCUK rates (for 2022/23 full-time study this is £16,602 per year) and full tuition fees. Only UK candidates may apply.

Studentships are available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £9,961 per year and full tuition fees) in combination with work or personal responsibilities.

Please note: to be classed as a Home student, candidates must meet the following criteria:

• Be a UK National (meeting residency requirements), or

• have settled status, or

• have pre-settled status (meeting residency requirements), or

• have indefinite leave to remain or enter.


References

1. Zhang, H., Hewage, K., Karunathilake, H., Feng, H., Sadiq, R. (2021). Research on policy strategies for implementing energy retrofits in the residential buildings. Journal of Building Engineering.
2. Kassem, M., Mahamedi, E., Rogage, K., Duffy, K., Huntingdon J. (2021). Measuring and benchmarking the productivity of excavators in infrastructure projects: A deep neural network approach. Automation in Construction.
3. Edmondson, V., Cerny, M., Lim, M., Gledson, B., Lockley, S. Woodward, J. (2018). A smart sewer asset information model to enable an ‘Internet of Things’ for operational wastewater management. Automation in Construction.
4. Jiang, H., Qin, S., Fu, J., and Ding G. (2021). How to model and implement connections between physical and virtual models for digital twin application. Journal of Manufacturing Systems.
5. Wilcox, T., Jin, N., Flach P., and Thumim J. (2019). A big data platform for smart meter data analytics. Computers in Industry.
6. Feng, H., Liyanage, D. R., Karunathilake, H., Sadiq, R., and Hewage, K. (2020). BIM-based life cycle environmental performance assessment of single-family houses: Renovation and reconstruction strategies for aging building stock in British Columbia. Journal of Cleaner Production.
7. He, P., Feng, H., Hu, G., Hewage, K., Ahari, G., Wang, C., and Sadiq, R., (2020). Life cycle cost analysis for recycling high-tech minerals from waste mobile phones in China. Journal of Cleaner Production.
8. Hu, G., Feng, H., He, P., Li, J., Hewage, K., and Sadiq, R., (2020). Comparative life-cycle assessment of traditional and emerging oily sludge treatment approaches. Journal of Cleaner Production.
9. Zhu, S., Li, D., and Feng, H., (2019). Is smart city resilient? Evidence from China. Sustainable Cities and Society, 50:101636.

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