Granular flows, such as landslides and rock-falls, present one of the most impactful natural disasters, and may pose a serious risk to life and property, alike. As the frequency and magnitude of granular flows continues to intensify (due to climate change), leading to increased impact (due to increased exposure because of population, activities and infrastructure growth), it is timely relevant and important to design and undertake a systematic and in-depth study of the involved processes towards developing methods and tools that can help offer more robust response to predict and resiliently address this challenge .
This project aims to address both fundamental and applied research questions:
1) Shed more light on the effect of particle-scale dynamical processes of the energetics and kinematics of granular flows by identifying governing processes and quantifying the effect of macroscopic parameters controlling those.
2) Quantify the impact of granular flows on built infrastructure (i.e. road pavements or protective structures, such as retaining walls) and how can the existing engineering practices benefit from using innovative tools and methods, based on smart sensors and artificial intelligence, as well as novel hierarchically based decision and design support frameworks, for increasing resilience against such earth surface hazards.
Listed below are the major objectives and methods proposed for this research.
1) Study and better understand the dynamical processes of granular flows at the particle-scale, using miniaturized â€œsmart-rocksâ€ developed by Dr Valyrakis’ research group [2, 3]. This involves using particles instrumented with inertial sensors for measuring metrics characterising various granular flows.
2) Explore the link between specific metrics of particle-scale granular flow dynamics to their macroscopic features such as runout characteristics. This will be pursued by also using image processing analysis (such as high-speed cameras and particle tracking velocimetry techniques ) and force diagnostic tools (eg using load cells).
3) Apply the above knowledge linking the macroscopic and microscopic processes of granular flows in gaining a more in depth understanding of the impacts of granular flows on infrastructure (such as road pavements and retaining walls) and evaluating the effectiveness of various designs against such earth surface hazards. This will be done using forces diagnostic (load cells) for various engineering designs having various degrees of deflection of the granular flows, towards identifying more robust engineering designs, against such hazards. Validate simple discrete element models using the above experimental data .
4) Consider all the previous research objectives, to define the proper methodologies for the use of innovative tools (instrumented particles), towards monitoring the stability of ground surface around infrastructure with modern instrumentation.
5) To develop proper predictive methods (using both knowledge of low-level physical processes and data rich machine learning/artificial intelligence methods  to predict landslide initiation) to enhance hazard assessments and susceptibility analysis and assist decision-making under a hierarchical framework using sound science, essential for ensuring societal resilience against any geotechnical and/or hydraulic hazards .
Phase I – laboratory experiments
Training in the use of facilities and specialized equipment
Preparation of literature review and input from BGS supervisors for design of representative experiments (referring to cases in Scotland and the rest of the UK)
Design and conduct of series of laboratory experiments, also using â€œsmart-rocksâ€
Data analysis Regional Conference to present laboratory data
Phase II – Numerical modeling
Training in use of existing DEM algorithms
Design of representative local geometries and conducting runs
Submission of Journal paper based on laboratory data
National or International Conference to present numerical modeling results (calibrated by the laboratory data)
Phase III – Field campaign
Training in field skills and specialized equipment use
Testing and field deployment of â€œsmart-rocksâ€
Data collection and analysis
International conference to present field data
International conference participation to present field data
Submission of Journal paper based on above findings (demonstrating the application of the lab pre-calibrated â€œsmart-rocksâ€, on the field).
Lab health and safety, design of experiments and equipment training
Training in specialized software usage and coding for post-processing of data and analysis and presentation of results with scientific programming languages
Training in scientific writing
Training in the design and conduct of field trips to acquire real-world data with specialized instrumentation
References & further reading
 P Michalis, F Konstantinidis, M Valyrakis, V Avdikos (2019), Current and Future Directions towards Civil Infrastructure 4.0, ICONHIC 2019. M Valyrakis, E Pavlovskis (2014), ” Smart pebble” design for environmental monitoring applications, EGU General Assembly Conference Abstracts 16. C Houston, D Muir, J Trinder, M Valyrakis (2018), Development of a miniaturized “smart-sphere” for environmental hydraulics and geomorphology applications. EGU General Assembly Conference Abstracts 20, 18931. M Valyrakis, H Farhadi (2017), Investigating coarse sediment particles transport using PTV and “smart-pebbles” instrumented with inertial sensors, EGU General Assembly Conference Abstracts 19, 9980. M Valyrakis, P Diplas, AO Celik, CL Dancey (2008), Investigation of evolution of gravel river bed microforms using a simplified Discrete Particle Model, Proceedings of River Flow. M Valyrakis, P Diplas, CL Dancey (2011), Prediction of coarse particle movement with adaptive neuroâ€fuzzy inference systems, Hydrological Processes 25 (22), 3513-3524.
Applications: to apply for this PhD please use the url: https://www.gla.ac.uk/study/applyonline/?CAREER=PGR&PLAN_CODES=CF18-7316
If you want to know more about the project, feel free to contact Manousos Valyrakis at Manousos.Valyrakis@glasgow.ac.uk