This project will be co-supervised by Geospatial Ventures Limited

Project application reference GEO23_02

GNSS technology is a key element of the deformation monitoring systems in engineering and geohazards projects. However, the relatively high cost of the geodetic-type GNSS stations limits the extensive deployment of GNSS stations leading to potentially less precise modelling of the deformation. The latter can be crucial for the applications of monitoring major civil engineering infrastructure (e.g. long-span bridges) or early warning systems of geohazards (i.e. earthquakes, volcanoes, floodings).

The recent developments of low-cost GNSS receivers have significantly improved their performance, reaching competent level of accuracy and precision and similar of geodetic-grade GNSS receiver. Recent studies proved that low-cost GNSS receivers can be applied for deformation monitoring of civil engineering structures. Furthermore, recent models of smartphones are equipped with low-cost dual-frequency GNSS receivers, which could potentially be adequately precise to be applied for deformation monitoring application. The latter could be revolutionary, as this would enable each smartphone, at the area of the occurring deformation (e.g. bridge, highway, regional ground motion), to function as a GNSS station of a broader GNSS monitoring network and monitor the deformation. This approach could be applied in monitoring even troposphere/ionosphere-related activities, such as rainstorms, as they strongly related to GNSS positioning solution.

This research project will focus on the investigation of the performance of smartphone GNSS receivers, in terms of accuracy and precision, to monitor deformation related to civil engineering structure response and geohazards ground motion. The investigation will be based on controlled experiments by examining deformation of various type and range (slow and dynamic) meeting the characteristics of structures response and geohazards ground motion. Also, the integration of the smartphone GNSS data with other sensors of the smartphone (i.e. accelerometer, tiltmeter) will be investigate to enhance even further the smartphone positioning performance.

The aim of this project is to develop a methodology where the smartphone GNSS receivers will function as “GNSS monitoring stations” and part of a broader monitoring network of a civil engineering or geohazards project (e.g. long-span bridge, ground motion, etc.) in order to densify the monitoring network project (e.g. long-span bridge) and improve the modelling of the deformation. Temporal and spatial analysis techniques will be adapted to analyse all GNSS data, including the smartphones and GNSS stations, to evaluate whether the deformation modelling becomes more precise and detailed leading to more informative characterisation of the civil engineering structure response/geohazard-related ground motion.

During the MRes, the main aim will be to have a preliminary experimental analysis of the positioning performance of a GNSS smartphone receiver in deformation monitoring. A range of different types of deformation (amplitude and frequency) will be examined, and the accuracy and precision of the smartphone GNSS receiver will be estimated. The first assessment of smartphone GNSS receiver in deformation monitoring will be estimated by defining the limitations of the method.

Experimental and analytical skills acquired during the MRes, will be used to develop the methodology of the application of smartphone GNSS receivers for deformation monitoring. Further experiments will be carried out to investigate the performance of smartphone GNSS data analysis in both the time and space domains. The GNSS data will be integrated with other smartphone sensors to evaluate the enhancement of smartphone GNSS positioning solution. A methodology will be developed for the spatial and temporal analysis of the smartphone GNSS data and permanent GNSS stations data to model the deformation and define its characteristics. The methodology will be applied on a real deformation monitoring project.

The successful candidate will have a strong background in Engineering, Mathematics, Geomatics or Physics. In the framework of this project, the candidate will focus on the area of GNSS technology, time series analysis and the application of GNSS technology in deformation monitoring. The candidate is expected to develop skills in GNSS measurements, advanced data analysis and programming, which will be needed for the investigation of smartphone GNSS positioning performance and its analysis for monitoring applications in engineering.

For further information, please contact Dr Panos Psimoulis

This project will be co-supervised by Bays Consulting

Project application reference GEO23_03

Forests globally are under increasing stress – whether from climate change, pests and diseases or invasive species. To know where stressors are impacting on trees in a timely manner is extremly crucial for management of these important ecosystems, and would benefit immensely from an active monitoring approach that is autonomous. This project will use a number of case studies of forest stress (e.g., liana infestation captured using unoccupied aerial systems (UAS)) from around the World to exploit the power of AI on remotely captured data to detect the source of stress. The power of hyperspectral and/or hyperspatial and/or hypertemporal remotely captured data will then be used to develop autonomous remote sensing for forest monitoring. The learnings from forestry will be explored in the context of the wider UN Global Goals.

One particular forestry project will be the focus of the MRes research. Here a selection of AI methods will be applied to UAS data captured in Borneo to extract information on the impact of stressors of the forests automatically.

Learnings of how the AI methods used as part of the MRes performed will be applied to a different forest, with the aim of achieving full autonomy in data capture using UAS. You will work with data from a unique, long-term and large-scale liana removal experiment (LRE) in Panama and from Sherwood Forest in UK. Repeatedly captured data across the experiment will be used to engineer a trustworthy autonomous system for monitoring the two forest sites for change over time and space. The learnings developed for the Panama LRE and Sherwood sites will then be transferred to other scenarios within the UN Global Goals and used to build on the concept of autonomous remote sensing.

Good conceptual and practical knowledge of remote sensing/GIS is desirable. Programming (ideally in R) and machine learning skills are assets. However, enthusiasm for forests and curiosity about the best ways to conserve it under environmental change using geospatial science are by far the most important requirements.

For further information, please contact Professor Doreen Boyd.

This project will be co-supervised by JBA Trust

Project application reference GEO23_06

The communication of environmental risk, such as that from flooding, has traditionally involved the use of maps and posters at community engagement events. With flooding, the distribution of risk across a landscape, and through time, can be informed by past events or modelled scenarios, but such patterns are complex and dynamic, and are difficult to represent through traditional means such as maps and posters. A further challenge is to communicate the impact of such events on specific parts of the landscape, and so it is important to offer a clear frame of reference to help people understand the potential impact of events on people and places. Through a collaboration between the University of Nottingham and JBA Consulting the ‘Projection Augmented Relief Model (PARM)’ technique has been applied to flood risk. This utilises animated maps and imagery projected down onto 3D printed landscape models, creating a ‘tangible display’ that offers people a very natural view of a landscape. The PARM technique shows great promise as a tool for engagement, but as yet, there has been little research into evaluating its effectiveness in a rigorous manner. There is therefore an opportunity to design a strategy for evaluation to measure whether such displays promote a richer understanding of environmental risk than might be gained through more conventional methods. A methodology could be developed which draws upon techniques such as thematic analysis of discussion transcripts of people at the display and spatial recall tests as used in experimental psychology. The aim would be to explore if and how such displays provided a more effective spatial frame of reference and whether they promoted richer discussion of the issues and greater understanding of the patterns being visualised as a result.

In the first year, the MRes project will undertake a comparison of a projection-enhanced 3D model and a flat map for conveying spatial patterns. The aim would be to gain an understanding of the ways a 3D model might provide a stronger frame of reference than a conventional map-based approach.

The MRes project would provide some generic findings related to the basic capabilities of models for providing a spatial frame of reference, using individual viewers. The PhD research would extend this to consider how we could measure understanding gained from a display, related to the ‘learning objectives’ of a particular case study project. It would also consider group interaction and how we can measure the richness of discussion that emerges when using such a display, including ways of coding themes that emerge but also interactions between members of the group, and between people and the model.

For further information, please contact Dr Gary Priestnall

This project will be co-supervised by Geospatial Ventures Limited

Project application reference GEO23_08

Across the world, the climate crisis is becoming increasingly more threatening to not only the natural world and human populations but also has a negative social and economic impact on different communities around the globe. One of the systems that is largely affected by this is the Earth’s Ocean system. advancements in small satellite and remote sensing technologies have allowed to perform new tasks and new missions with microsatellites.  This project is aiming to use CubeSat platform to obtain accurate measurements of sea-surface height (SSH) and other related data with a shorter revisit time than conventional altimetry satellites while decreasing the complexity and associated costs. The CubeSats are intended to use the global navigation satellite system reflectometry (GNSS-R) to collect ocean topography with a measurement accuracy of better than 5cm. The study will be conducted in two phases: mission analysis dedicated to the optimization of the mission configuration, with particular attention on the constellation design aspect, second phase payload and bus design and optimization, were the attention is focused on the payload design, implementation and integration inside a CubeSat bus.

Work undertaken during the MRes will include;

Literature review of existing similar missions.

Familiarization of the current simulation software and processes and programming languages.

Identification of mission and system requirements

Following the MRes, during the PhD, the successful candidate will;

Adopt ECSS standards to design and implement a GNSS-R payload

Design and implement a CubeSat bus to host the GNSS-R payload

Mission design: Ground segment, space segment and launch segment

Experience of Spacecraft systems and design knowledge – systems, segments, missions, and payloads; Orbital Mechanics and Astrodynamics; GNSS basics – concept, signal parameters, navigation using GNSS, GNSS-R; Matlab or Python or C++; GMat, STK, FreeFlyer or similar software is desirable but not essential for this project.

For further information, please contact Dr Chantal Cappelletti

This project will be co-supervised by the National Astronomical Observatory of Japan (NAOJ)

Project application reference GEO23_09

In the recent years, there has been a renewed interest in sending humans beyond low Earth orbit, as well as ambitious robotic exploration missions to other planets and remote small bodies in space. Developments in both robotic and human spaceflight are driving the demand for autonomous spacecraft operation. Spacecraft navigation without any Earth-based tracking or updates is a particularly difficult problem. Autonomous navigation may be required for robotic missions involving high-speed flybys of planets or flybys that occur when the sun is between Earth and the planet. Also, they can be used to perform proximity rendezvous operations such as in-orbit servicing, debris removal/capture in LEO and Geostationary orbits. Currently methods of navigation in deep space and in case of proximity operations, involving ground-based tracking have limited accuracy, while robotic missions requires a more precise orbital determination or trajectory state estimation to perform proximity operations.

This research will focus on developing dedicated autonomous systems at NGI that could image targets (asteroids, planets, other minor small bodies, in-orbit satellites) in space and determine orbits precisely for in-orbit operations of satellites in space. This includes implementation of artificial intelligence techniques to fuse image-LIDAR based data to create shape model of the target bodies and to estimate gravitational field acceleration (in case of remote small planetary bodies) to precisely determine the orbit in space.

In year one, the MRes will include;

Familiarization of the current simulation software and processes and programming languages.

Identification of requirements for future autonomous missions including deep space targets and in-orbit proximity operations.

Simulation of performance for simple augmentations expected in the immediate future.

Following the MRes, in years 2 – 4, the PhD will include.

Adopt Machine Learning techniques to fuse LIDAR based and Image based data to predict gravity field estimation of existing irregular shaped minor planetary bodies or regular planetary bodies.

Develop algorithm to precisely estimate the orbital states of satellite in-orbit to perform proximity rendezvous operations around another dysfunctional satellite or tumbling orbital debris.

Design image-based orbital prediction methods for autonomous satellites (both small-satellites and large satellites)

Improvements to navigation filtering, modelling and algorithmic approaches.

The successful candidate should have experience in GNSS basics – concept, signal parameters, navigation using GNSS; Spacecraft systems knowledge – equipment, missions, and payloads Orbital Mechanics; Matlab or Python or C++ or Julia or Any Programming Language; Machine Learning.

For further information, please contact Dr Nishanth Pushparaj

This project would be co-supervised by the Met Office.

Project application reference GEO23_10

The main research gap concerns the very outdated return level maps for extreme wind speed return levels (such as the 50-year level, used in building design) and the failure of current standards to build in any resilience to a changing climate of extreme wind speeds. We propose a network of regional spatial Bayesian models for extremes, whose power in combining information from multiple sites will greatly improve (a) the reliability of return level maps in reflecting current conditions, and (b) the ability to detect and model the climate change signal as it applies to extreme winds. This latter can be used in conjunction with recently developed high resolution regional climate models, developed and used by the Met Office, to construct models for the future risks to structures and infrastructure posed by extreme wind speeds. This work will be co-supervised by Simon Brown, Climate Extremes Science Manager at the Met Office Hadley Centre, and the applicant. High quality data already exists as a result of recent collaboration between the supervisors, carried out specifically for the purpose of building enhanced models for extreme wind speeds with a much broader scope than existing models. Skills gained as part of this training would be the ability to build and implement sophisticated Bayesian spatial models, and the data management capabilities needed for such a project. 

During the first year, as part of the MRes in Geospatial Data Science, the project will include the design and implementation of a limited spatial Bayesian model for extreme wind speeds for a single region in the UK, with the possibility for incorporating a climate change signal.

This will then lead to the development into a national model, incorporating a network of regional models, and allowing for variation in possible climate change signals for extreme winds. The idea of using the model in conjunction with state-of-the-art regional climate models will be introduced and developed, with the aim of calibration of the changing extreme-wind risk within the climate models, and hence future risk-assessment as it applies to structures and national infrastructure.

Candidates should have a minimum of a 2:1 undergraduate degree (or equivalent) in Mathematics and/or Statistics. This degree would have incorporated appropriate skills in statistical inference, statistical modelling, data management and programming, for instance using R or Python. Knowledge of R would be extremely beneficial.

For more information, please contact Dr David Walshaw

This project would be co-supervised by Northern Gas Networks.

Project application reference GEO23_11

The UK Climate Resilience Demonstrator (CReDo) is a climate change adaptation digital twin that provides an example of how connected data can improve climate resilience across networks. CReDo looks at the impact of flooding on electricity, water and telecoms networks. It demonstrates how those who own and operate them can use information sharing to mitigate the effect of flooding. Climate projections were used to produce future extreme weather events. The likelihood of failure of assets in the networks was assessed for each scenario, and the effect of asset failures were propagated through the network.

This project will support CreDo, incorporating a gas network into the structure. For future extreme weather scenarios, it will consider the failure modes of gas network assets in a region, and the effects connections with other assets in the gas, telecoms, electricity and water networks. The modelling and quantification of the paths to failure of assets requires structural and probabilistic expert knowledge elicitation (EKE). As there are hundreds of assets, novel methods are required to scale-up the EKE process – by performing elicitation on classes of assets, using covariate information to adjust failure probabilities and calibrating this adjustment.

The project will begin by producing an inventory of gas network assets over a region, with the physical connections between assets (including electricity, water and telecoms assets). The assets will be classified into a small number of classes for elicitation. A structural elicitation will be carried out for one asset, to evaluate the failure modes in an extreme weather event, and the routes to failure. A probability elicitation will be carried out to evaluate the probability of asset failure in the scenario.

Candidates with a stastics background are encouraged to apply. An understanding of Bayesian statistics would be of benefit but is not essential.

For more information, please contact Dr Kevin Wilson

This project would be co-supervised by the North East Local Resilience Forums

Project application reference GEO23_13

This project aims to develop the potential of geospatial data for the management of a range of risks that the three North East England Local Resilience Forums (LRFs) face at all stages of implementing the Integrated Emergency Management Framework (IEMF). The LRFs are multi-agency partnerships made up of representatives from local public services, including the emergency services, local authorities, the NHS, the Environment Agency, and others. These agencies are known as Category 1 and 2 Responders. Evidence suggests that such responders are often aware of the potential of geospatial data but don’t possess the requisite knowledge, access or capacity to make best use of the data before (building resilience), during (response to) and after (recovery from) emergency incidents. This research will therefore build and disseminate exemplars of geospatial data capability in an area of public services where there is a recognised knowledge and competency gap.

This study aims to better exploit geospatial applications and so make improved decisions through a review of the risks and the potential application of geospatial data – identifying where there is most potential for decision makers (e.g. risks such as power loss or wild fires caused by extreme weather, fuel depot fires, coastal flooding) and how opportunities may be exploited. It is anticipated that the MRes research will result in a data ontology that describes the relations between multifarious geospatial primary and secondary data sources, including e.g. in-situ sensing from Newcastle’s Urban Observatory and satellite based sensing, for a range of emergency management scenarios.

With problem definition from the MRes, the PhD research will pick up on the findings to develop open prototype solutions to one or more of the identified core risks. Methods are expected to call on the big data, machine learning and visualisation skillsets of the students developed through the MRes taught programme. Focus will be on local (North East England) piloting of activity that has potential to be subsequently scaled up and rolled out nationally.

A good undergraduate or masters degree in computing science, civil engineering, geography, mathematics, geomatics or related disciplines is essential. Remote sensing expertise, programming skills (Python or C++), practical experience of emergency management are desirable.

For more information, please contact Professor Jon Mills

This project will be co-supervised by Atkins and the Environment Agency.

Project application reference GEO23_15

Natural hazards, including geological hazards pose a potential risk to infrastructure, and is an important consideration for future planning and development. Geological hazards in the UK include, landslides, swelling and shrinking clays, soluble rocks and coastal collapse. Mechanical layering and fractures have been shown to have an important control on hazards such as coastal collapse (e.g. Ferril & Ferril, 2021) and for the integrity of underground engineering construction projects, such as tunnelling. Accurately characterising the variations in mechanical properties of fine-grained sedimentary sequences is a vital part of geological characterisation for developing resilient infrastructure.

Mechanical stratigraphy, or layering, is the result of both original depositional variations, and post burial chemical and mechanical changes (e.g. Laubach et al., 2009). This approach to characterisation allows the rock to be subdivided based on mechanical properties such as tensile strength or brittleness (Laubach et al., 2009). As fine-grained sedimentary rocks are ubiquitous in in the rock record, understanding mechanical stratigraphy is of widespread importance. While there are established relationships for bed thickness and fracture spacing, there few studies that investigate how variations in mechanical stratigraphy may impact decision making and planning as part of coastal management or infrastructure resilience.

This project will investigate the use of high-resolution digital outcrop models to characterise the mechanical and fracture stratigraphy of coastal cliff sections. It will use terrestrial laser scanning and drone photogrammetry to generate point clouds to construct 3D models and investigate the reliability of using automated feature detection methods to characterise geological discontinuities. The interpretations will provide a detailed characterisation of the macro scale variations in the mechanical properties. Through collaboration with Atkins and the Environment Agency, will be a unique opportunity for shared learning and knowledge exchange.

In the MRes, we will construct, based on new outcrop data collection, a model of a vertical sequence with a dominance of fine-grained sediments, investigate the suitability of existing image analysis methods for the interpretation of mechanical and fracture stratigraphy in the outcrop models and characterise the macro scale variations in the mechanical properties of fine-grained rocks, the mechanical stratigraphy.

Continuing in to the PhD, we will develop automated workflows for the characterisation of discrete mechanical layers and fractures/joints from digital outcrop models; investigate the reliability of automated detection methods for characterising fracture/joint spacing in coastal exposures; use 3D outcrop models to investigate the variations in mechanical and fracture stratigraphy in coastal sections; investigate how variations in mechanical and fracture stratigraphy could influence coastal collapse and erosion and use 3D outcrop models to quantify the links between mechanical layering (including thickness variations) and cliff erosions profiles and collapse.

Candidates should have knowledge and experience in one of more of the following areas;

remote sensing and data acquisition;

image processing and analysis;

processing and interpretating remote sensing data;

engineering geology;

coastal engineering;

structural geology and fracture analysis.

Candidates should also have experience in one or more of the following methods/tools;

QGIS/ArcGIS

Python

Matlab

RStudio

Other image analysis or remote sensing software

For further information, please contact Dr Mark Ireland

This project will be co-supervised by the Department for Business, Energy & Industrial Strategy.

Project application reference GEO23_18

Direct air capture (DAC) of CO₂ will be required at the gigaton scale for climate change mitigation. Deployment and operation of DAC are only feasible following detailed appraisal of the spatially engineered energy system in which it will operate. This project will bring together world-leading expertise in energy system modelling and DAC. The primary objective will be to develop a methodology to optimize how and where DAC should be deployed and operated given the availability of renewable energy, the carbon-intensity of electricity, and weather phenomena which govern both DAC performance and renewable energy generation.

During the MRes, the successful applicant will;

Identify and produce detailed process models of prominent commercial-scale DAC processes and likely future market disruptors.

Create a spatially-coupled energy system model which can use available weather and climate data to quantify the performance of DAC given the locational availability of low-carbon generation. The model should have appropriate scale to capture the variability of DAC performance across different systems and regions, and enough detail to allow meaningful conclusions to be drawn. It should draw on appropriate future energy scenarios available from project partners.

Following the MRes, during the PhD, we will;

Integrate the DAC and energy system models to enable simulation of DAC operation in future energy systems.

Develop a deep understanding of the effect of local climate on the efficiency of these DAC processes.

Use the concept of unit commitment to develop optimisation methods which can determine the best locations and operating regimes for DAC.

Carry out sensitivity analysis to quantify how the cost and efficiency of DAC processes affect its viability.

Investigate how DAC would operate in the presence of different emissions reduction incentives (e.g. carbon taxes, cap and trade schemes, etc.).

Candidates should have some experience in developing mathematical simulation models in appropriate languages and experience in handling large data sets is desirable but not essential.

For further information, please contact Dr Greg A. Mutch

This project will be co-supervised by the Dolomiti National Park. 

Project application reference GEO23_19

Alpine regions face an existential threat due to anthropogenic effects. A full grasp of the problem requires a detailed historical understanding of the changes humans have imposed on these regions over time. The most profound shaping of the Alps was due to the spread of the farming during the Neolithic. This process is now well understood for the plains and coasts of Europe, but largerly unknown in the Alps. This motivates the creation of the first spatiotemporal model of the spread of agriculture into a sector of Alpine region. We will work in close collaboration with Dr Francesco Carrer who will provide archaeological data for a selected case study in the Southern Alps. We will combine a reaction-diffusion approach with Bayesian inference to constrain free model parameters. Building on this we will simulate demic diffusion and acculturation of local hunter-gatherer communities. This project is an interdisciplinary fusion of spatiotemporal mathematical modelling and quantitative archaeology, and it will advance the use of computational methods in European Prehistory 

During the MRes, the student will develop a numerical code to solve the 1D Fisher–KPP equation. Outputs of the code will be compared to key results in the literature and known analytic solutions. Parallel to this they will perform a detailed literature review on modelling approaches to population spread and prior reaction-diffusion approaches to the Neolithic epoch.

We will build on the work of the MRes by adapting the code to 2D spherical coordinates and then develop an approach to integrate Bayesian inference into our modelling efforts to use data to constrain parameters. Subsequently, we will extend the model to account for altitude and will then be in a position to perform the first quantitative study of the spread of farming into the Alps. Finally we will tackle the mechanism of spread, making the distinction between demic vs cultural diffusion, following the work of Fort (R. Soc. Interface, 2015) to improve our understanding of the acculturation of local hunter-gatherer communities. 

For further information, please contact Dr Andrew Baggaley

This Project will be co-supervised by Network Rail. 

Project application reference GEO23_22

To help Network Rail to improve the operation of the railway, expert reviews were commissioned from renowned specialists Lord Robert Mair and Dame Julia Slingo, published in 2021. These provided an independent assessment of current practice and offered more than 50 recommendations on how to improve safety and performance (e.g. Weather Advisory Task Force (https://www.networkrail.co.uk/wp-content/uploads/2021/03/Network-Rail-Weather-Advisory-Task-Force-Final-Report.pdf).

Landslides/earthwork failures are a major natural hazard in the UK, causing more than £10M of economic losses annually and posing clear threats to infrastructure and life. There is an urgent need to provide early warning and implement proactive mitigation of these destructive events, but slope stability analyses require geotechnical, geomorphological and meteorological data and so are currently restricted to fragmented analyses of past failures. The principal determinant of the landslide hazard is typically rainfall, but network operators currently rely on generic ‘on-off’ warnings based on rainfall intensity and duration derived from forecasts and isolated rain gauges, giving no strategic information about where the hazard is highest or evolving fastest. Rainfall in the UK has increased by 17% since 2008, a trend that is likely to continue, resulting in greater and more complex landslide hazards and related damages. There is a science need to use cutting edge spatial data capture and interpretation methods to support decision making for infrastructure resilience.

This project is part-funded by Network Rail and will explore ‘big data spatial analytics’ for a very large dataset of historical weather forecasts, recorded EWATs, actual weather information (from gauges, stations, radar, etc.) and recorded incidents (failures, speed restrictions) to address critical knowledge gaps concerning the evolution of earthwork failure responses under spatially variable rainfall.

The MRes project will be developed with Network Rail but will consider the links between extreme weather and incidents on the railway (earthwork failures etc) to identify how earthwork and rainfall characteristics are linked for the major failure types. Your approach will be co-created, leveraging techniques learnt in the taught modules around data capture, interpretation and decision making support.

The model developed in the MRes will lead in to the PhD and further research through the development and deployment of automated monitoring for spatial and temporal data capture, including permanent lidar units on high risk slopes where movement is expected, and developing new methods including satellite-based earthwork movement detection systems, which can be used together with the EWATs to determine risk of failure. This will provide a new potential monitoring system for high risk slopes and a system for integrating this information with extreme weather data for decision-making.

The project will require good coding skills and will use spatial analysis methods and statistics to explore big data. Field experience will be a necessity. The project will also be partnered with the impacts team at the UK Met Office, and a candidate could spend time there training or embedded within Network Rail.

For more information, please contact Dr Stuart Dunning

This project will be co-supervised by King Mongkut’s University of Technology North Bangkok

Project application reference GEO23_27

Structural health monitoring for smart cities and infrastructure are emerging importance for condition based maintenance in digital society. Corrosion is one of major issues of building intregrity that hardly indentified due to its long period of deterioration process.  To monitor its progress, a network of long-term corrosion sensors are install distributedly throughout the monitoring infrastructure. An RFID-based sensor requires no battery and, therefore its lifetime is virtually unlimited. It provides both spatial identification and sensing information for multi-scale structural monitoring. Although the RFID-based sensors require no battery, it still requires a reader to energise in order to activate and obtain its data. Apart from using fixture readers, a mobile autonomous (e.g., robot or drone) or energy havesting from environments such as radio frequency, wind or vibration, and various wireless power tranfer techniques for permanent installed sensing and monitoring could be also investigated. As required from industries of lamppost monitoring for smart cities, permanent installed RFID IoT monitoring of corrosion will be designed and developed for illustration of condition based maintenance.

Funded by EPSRC, one CASE studentship with International Paint Ltd has invented and demonstrated RFID sensor-based corrosion monitoring. This project will scale up for network and geospatial application for monitoring lamppost and bridge with data collection, data visualisation and decision making.

As illustrated above, in addition to regular personnel inspection, low-cost permanent installed RFID install sensors and IoT monitoring systems will be designed, tested and evaluated in the UK and Thailand to address relevant scientific and industrial challenges and problems in collaboration with relevant industries worldwide.

During the MRes, one research review report will be provided as contents listed below.

1 Understanding the state-of-the-art and challenges and problems of data collection, interpretation and geodata management.

2 Understanding challenges and opportunities of smart cities, multi-scale structural monitoring, Translation to Global Challenges

3 Be familiar with an RFID sensor for corrosion monitoring, previous relevant projects funded by EPSRC and industries in Newcastle

Following the MRes, during the PhD research will be conducted as listed below;

1 Design, develop and investigation of RFID sensor network for multi-scale structural monitoring;

2 Study data capturing, interpretation, visualistion of different corrosion and spatial data from RFID sensor and IoT based corrosion monitoring;

3 Investigation of decision making of condition-based maintenance and scale up for smart cities and translation for global challenges.

As this is a cross-disciplinary project, a good knowledge of electronics, sensors, measurement and instrument, signal processing and communication, geoinformation and management, material science, data science would be an advantage.

For further information, please contact Professor Gui Yun Tian

This project will be co-supervised by Ryder Geotechnical

Project application reference GEO23_28

The UK Government’s ambitious targets for offshore wind are now set to increase deployment from 11GW at present to 50GW by 2030, including delivery of 5GW from floating offshore wind. The dynamic performance of OWTs is of great importance as the turbines must be designed to withstand both the operational and extreme environmental loads inflicted by wind, waves, and currents, which can lead to dangerous resonance effects. Monitoring and inspection regimes ensure the integrity of the wind farm asset over its lifetime and are vital for the management of project risk.

In this research, a carefully designed monitoring programme of OWTs using an enhanced Global Navigation Satellite Systems (GNSS) is planned. The aim is to install and run several GNSS receivers on the turbines which are linked to a robust and self-healing wireless network, allowing near real-time data transfer that delivers a precise measurement of the dynamic movements of the turbines. The advantage of the wireless network is that it removes the requirement to physically visit each GNSS data receiver individually to collect data for onward processing and analysis.

Prediction of dynamic OWT performance is complex and requires models able to capture the often highly nonlinear fluid-structure-soil interactions. In particular, the behaviour of the foundation system is the result of mechanisms such as cyclic accumulation of plastic strain, pore water pressure generation, and stiffness degradation in the soil. In parallel with the monitoring work, we propose to use advanced numerical modelling software, incorporating state-of-the-art soil constitutive models, to conduct high-fidelity simulations of the OWT system. The monitoring data will be used to validate the model predictions and develop a ‘digital twin’ methodology, consisting of a refined modelling and monitoring framework, which could be used to inform decision-making during both design and operation of the wind farm, ultimately reducing costs.

This project will combine a novel GNSS-based monitoring programme with advanced structural modelling to improve our ability to predict the dynamic movement of offshore wind turbines. The outcome of the research will be a GNSS informed digital twin methodology that can help reduce both design and maintenance costs.

The MRes will include literature review and design of monitoring strategy within appropriately selected wind farm sites. Work with project partners to mobilise GNSS hardware on limited number of offshore wind turbines on one identified site. Data collection and validation.

Develop work undertaken in year 1 by increasing deployment of GNSS hardware. Diversity of sites, location, type of foundation, geology. Build numerical model and calibrate soil constitutive model. Perform initial performance assessment and validate numerical model. Present results. Use real time data within storm events to demine performance of the foundation and optimise design. Work with project partners to add value to asset inspection and maintenance programmes. Refine and further calibrate numerical models and improve and contribute to design guidance. Publish and present results at relevant conferences.

Experience in numerical modelling in geotechnical engineering and in particular with the use of advanced constitutive models would also be desirable.

For further information, please contact Dr Mohamed Rouainia

This project will be co-supervised by North of Tyne Combined Authority

Project application reference GEO23_29

The search for more inclusive forms of economy has become a central task for territorial development and policy to address increasing geographical inequalities in economic and social conditions. While some cities and regions are seen as dynamic and productive engines of their national economies, the potential benefits they can generate are not always equally shared nor accessible to their residents. Generating and spreading opportunity more widely is central to ensuring people and places are not ‘left behind’ by their evolving city and regional economies. Crucially, existing understandings, data sources and yardsticks for identifying and measuring inclusive economies are under-developed. Indicators and frameworks are missing for monitoring and evaluating policy interventions seeking to promote more inclusive forms of economy.

The project aims to address these two gaps. First it will develop a clearer (spatial) framework for defining, understanding and measuring inclusive economy. Second it will develop indicators and yardsticks to support stronger monitoring and evaluation activities, drawing on existing, as well as new and emerging data sources. The project provides a unique opportunity to research a timely and important topic with wide ranging policy relevance for international, national, regional, urban and local institutions. It will afford the student the chance to develop skills in critically engaging contemporary debates about moving ‘Beyond GDP’ as a goal for prosperity, conceptualising more inclusive forms of economy, and providing underpinning data sources to support policy formulation, monitoring and evaluation. Opportunities for innovation exist in devising new composite measures and indices across multiple dimensions of inclusive economies and in developing mapping and visualisation tools in different national settings.

The MRes will engage critically with conceptions of inclusive economy and review existing data sources, indicators and frameworks for its measurement drawing from international practices and case studies.

The PhD will develop a framework for defining, understanding and measuring inclusive economy and growth, including an integrated monitoring and evaluation component, explore the potential of new composite measures, and develop new visualisation and mapping tools. The framework and its tools will then be road tested using data from selected case study settings in the UK and internationally.

The successful candidate will have essential skills and knowledge of local, regional and urban development and policy issues and debates; essential knowledge and skills in spatial statistics and analysis including mapping and visualation.

Knowledge and skills in spatial data sources in particular national settings including the UK and other countries and knowledge of ‘Beyond GDP’ and inclusive economy/growth issues and debates are not essential but would be desirable.

For further information, please contact Professor Andy Pike 

This project will be co-supervised by Ramboll

Project application reference GEO23_30

The Newcastle Urban Development Model (UDM) has been widely employed to understand climate impacts on future urban development, infrastructure planning options and is currently employed to model national-scale urban development in the UK Climate Resilience Programme OpenCLIM (Open Climate Impacts Modelling Framework). However, UDM is limited to explore a relatively small number of possible future urban development scenarios on the basis of expected population, employment, and other attractors e.g. transportation and social infrastructure. This limits the possibility of exploring a large number of possible spatiotemporal urban development options in relation to multiple conflicting objectives such as reducing different climate impacts (e.g., flooding and heat), while reducing high density development, and minimizing computing etc.

This limitation can be addressed through the use of surrogate modelling to develop a UDM emulator. This project will use Gaussian processes with computationally-intensive Bayesian inference to create a UDM surrogate. Gaussian process emulation (GPE) has been extensively used over the last 20-years in the surrogate modelling of computationally-expensive numerical models which are too time-consuming to evaluate in high numbers. The resulting emulator will have many unknown parameters which will be estimated using Bayesian inference with Markov chain Monte Carlo sampling. This allows the model to account for the uncertainty in the true parameter values and provides a more ‘honest’ way of uncertainty quantification. This PhD project will also have scope to develop the decision support tools and interfaces required to make a UDM emulator accessible to a variety of stakeholders.

In year 1, during the MRes in Geospatial Data Science, the successful candidate will learn about Gaussian process emulation in a frequentist or Bayesian setting and emulating a simplified small set of data. The student will be using R for building the emulator, and part of the year will be spent building the necessary computational skills and learning about Markov chain Monte Carlo methods.

Following the MRes, the student will spend some time in understanding how UDM works and how it fits within the wider integrated urban assessment framework. Then, the student will be presented larger data sets and will need to emulate more complex problems. The computational work should be done in at least two different implementations which would minimise bugs and ensure that correct posterior distributions are sampled. The theoretical framework includes multiple-output Gaussian process emulators, dynamical emulators, and Bayesian hierarchical models. After the UDM is emulated, the research may be directed towards developing an appropriate suite of decision support tools required to investigate and understated emulator outputs for practical planning purposes.

The successful candidate will have an interest in urban development problems. Excellent computing skills, knowledge of Bayesian inference, familiarity with Markov chain Monte Carlo methods and knowledge of R are essential.

For further information, please contact Dr Aleksandra Svalova

This project will be co-supervised by Historic England

Project application reference GEO23_32

Every landscape has a unique character, which is the product of complex historical processes driven by natural forces and human agency. This historic landscape character is an ecological, cultural, and economic asset of critical importance, and its protection is a priority in many countries around the world. In the UK, landscapes have long had a central role in heritage management and spatial planning, leading to the development of a new mapping approach known as Historic Landscape Characterisation (HLC). HLC uses GIS technologies to map the spatial distribution of predefined landscape character types and their transformation over time (Dabaut & Carrer 2020). HLC thematic maps are produced in GIS and are visually assessed by experts to identify spatiotemporal patterns in landscape character. Spatial analysis and modelling are rarely used to investigate HLC datasets, and this has limited the influence of HLC on spatial planning and heritage management so far.

Our project will investigate how statistical methods can be used to explore the spatial organisation and temporal transformation of landscape character. Cutting-edge spatial analysis methods, including local measures of spatial association (LICD), will be used to test explicit research hypotheses on the origin and evolution of landscape character in selected study areas. Regression modelling will identify the driving forces that created the historic landscape and contribute to its transformation over time. The project will be carried out in collaboration with Prof Sam Turner (HCA, Newcastle University), leading expert on UK historic landscapes and HLC. The results of the analysis will unravel the complex history of the investigated landscape and its future trajectories of change. Historic England, industry partner of the project, will help converting these outcomes into recommendations for the sustainable management of the historic landscape in the study areas. This project will also provide a new analytical protocol that could be applied to other HLC datasets.

During the MRes the student will familiarise themselves with the basic statistical methods used to analyse areal data, as well as their current implementation in the R programming language. The student will also study the basic principles of Historic Landscape Characterisation and will identify the key challenges of performing spatial analysis on HLC datasets. Once the student has acquired some familiarity with spatial statistics, R programming language and HLC, they will apply LICD (Carrer et al. 2021) to a selected HLC dataset and provide an interpretation of the identified patterns.

Building on the results of the MRes, the student will extend the application of local measures of spatial association to address other research questions emerged during the interpretation of the previous results. Methods will include LICD as well as alternative analytical approaches like the co-location cluster statistics (Anselin & Li 2019). The interpretation of the results will shed new light on the main processes of formation and transformation of the landscape in the study areas. The student will research the history of the study areas to identify the driving forces that influenced these processes. Depending on the student’s background, they might attend courses on regression modelling, necessary for the next stages of the project.

Following this, year 3 of the studentship, the student will use regression to model the correlation between landscape character types and their evolution over time against external covariates representing proxies of the driving forces identified in year 2. The modelling approach will be informed by the results of statistical analysis (year 1-2). The student will work closely with the industry partner (Historic England) to identify the goals of the model and to interpret the results. The first iteration of the model will lead to research outputs through a journal article and a conference presentation (Computer Applications in Archaeology conference).

The regression model will be finalised in year 4. The implications of the model outcomes for the sustainable management of the investigated landscape and for spatial planning in the study areas will be discussed with Prof Turner and Historic England. The student will provide a thorough interpretation of the evolution of the landscape character in the study areas and will infer the possible future trajectory of landscape change. This will lead to another journal publication and to the participation to another international conference (e.g. Landscape Archaeology Conference). A key output of the research will also be the code used for the analysis and its documentation, which will be reused by other researchers in the future.

The successful candidate will have experience in GIS and a knowledge of programming languages (R, Python), spatial analysis and a willingness to work collaboratively as a member of an interdisciplinary research team would be desirable.

For further information, please contact Dr Francesco Carrer.