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Integrated approach to assess tree stability in storms and risk assessment (SRPe-Industry Doctorate Programme)

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  • Full or part time
    Dr M Ciantia
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

More frequent intense storm events, associated with climate change, increase the likelihood of tree collapse adjacent to transport corridors that lead to costly disruption of road and rail journeys. Tree stability in windstorms and tree failures raise additional concern in urban areas where the risks of damage to people and property is higher. The 2014 winter storms in the UK caused the ’greatest loss of trees in a generation’ (Guardian newspaper, 7/03/2014), with the National Trust survey reporting that up to 500 trees were lost at the Killerton Estate in Devon.
Current methods of managing urban trees, such as pruning and assessing mechanical strength, are mainly based on the study of a single tree. These are based on visual tree assessment (VTA) by trained arborists and sometimes by means of expensive static pull tests. Despite both of these methods suffering significant limitations, they are still the most common techniques used as the regional authorities are responsible for thousands of trees and there is as yet no simple, detailed and affordable method of assessing many trees which would be necessary to accurately quantify risk.
In this project, a cheap novel approach to assess tree stability at the regional scale aimed at addressing the current limitations listed above will be developed. This will utilise advanced and rigorous numerical modelling, informed by small scale (laboratory) testing and available wind data, combined with wireless real time monitoring of tree arrays using low-cost sensors. Tree behaviour will be up-scaled to the regional scale by means of mathematical and statistical methods. The approach will be validated against field scale experimental testing, with a view to developing future urban risk maps. To achieve these goals a multiscale and multidisciplinary approach will be adopted, involving geotechnical engineers, fluid dynamicists and plant scientists.

The deliverables of this research project will be the validated methodology along with two example urban tree failure risk maps developed from its application; one of Dundee and the other of Milan, two cities for which there is already data collected using existing methods for validation. While for this research project these two cities are selected, one of the objectives is to develop a methodological approach which can readily be applied to generate tree stability risk maps for any city or urban environment. The project is four years long and it is structured into three work packages and five principal tasks:
WP1: Development of a predictive tree behavioural model
1. Propose a statistically robust model to generate representative root morphologies. This will be validated using ground radar data provided by Agroservice (AS) database for trees in Milan.

2. Develop a macrolement (mechanistic model used to describe soil structure interaction) using the root morphologies from (1). Validate this mechanical model for specific morphologies based on the AS database of tree pull tests for trees in Milan.

WP2: Remote sensing and Artificial intelligence (AI) model training

3. Field test phase: Develop a methodology whereby remote monitoring of tree deflection due to the action of natural wind loads using low-cost MEMS accelerometers is directly used to asses tree state.
By using wind induced tree deflection and genetic/AI algorithms, the macroelement model from (2) will be trained to reproduce such behaviour. The model, trained exclusively with historical wind data, will then be validated against pull test results performed on the same tree. Strength and stiffness of tree trunk will be required. Potential collaboration with Dr Ridley-Ellis (Napier University Centre for Wood Science and Technology) is hence foreseen.

WP3: Upscaling to the regional scale

4. Integrate the AI-Macrolement model developed in (3) in a Geographic Information System (GIS) framework which is well suited to gathering, managing, and analysing data at large spatial scales. In this way a GIS based model that can estimate risk after been parametrized by means of low-cost wind data and tree deflection monitoring at key locations will be obtained.
Produce example risk maps for the case study areas of Dundee and Milan using the GIS model from (4).

Funding Notes

SRPe IDP Studentship:
The successful applicant will receive a full scholarship which covers fees for 4 years and a stipend at around £15,000 per annum for 3.5 years. Funding is only available to UK and EU students.

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