Researchers 

Reliable drainage solutions are critical for ensuring the long-term and cost-effective provision of railway infrastructure. Water plays a significant role in the degradation of railway infrastructure and can cause poor track geometry and accelerated deterioration of ballast, with high associated maintenance and repair costs. Excessive amounts of water may also cause catastrophic failure of railway infrastructure systems, which represent a real threat to public safety. 

Climate change is predicted to result in more extreme weather and flash flood events. The railway drainage systems will therefore be put under severe strain with increased likelihood of disruption to rail services. Much of the UK railway drainage infrastructure is old and in need of repair or replacement. In addition, the UK railway industry is experiencing significant growth in the number of passengers and the amount of freight carried, which reduces the opportunities available to carry out maintenance. 

In light of these issues, railway drainage system modernisation is considered to be a key factor for improving railway network safety and capacity, and ensuring the infrastructure's resilience to changing weather and climate events. 

In a practical sense, this project focuses on providing novel and easily installed railway drainage solutions which make use of lightweight and cost-effective 'new materials'. The project includes a range of experimental testing, including trials of a new material drainage system within a full-scale railway track model, as well as advanced small-scale physical modelling using the University of Nottingham geotechnical centrifuge. Numerical models will also be developed to gain a better understanding of the effects of key parameters within the drainage system. 

An important component of the project is the development of methodologies which will allow for the assessment of the full lifecycle performance and costs of the developed new material drainage solutions. State of art prognostics and health management (PHM) methodologies will be developed to this end. These tools have the potential to help railway operators make anticipated and informed decisions relating to the selection of track and drainage system maintenance with quantified uncertainty.

The project benefits from the involvement of experts from railway industry, including URS, a leading provider of engineering, construction and technical services within the railway sector, and ASPIN, who provide a range of consultancy services to the railway industry. The project will also benefit from access to information from Network Rail, the owner of the UK railway infrastructure, through proven links between the research team and representatives from Network Rail.

Juan Chiachio is a Research Fellow in Infrastructure Asset Management in the Resilience Engineering Research Group. He received his PhD in Structural Engineering in 2014 (Summa Cum Laude, International Mention) by the University of Granada (UGR), Spain. Prior to join the University of Nottingham, Dr Chiachio worked as guest scientist at NASA Ames Research Center, where he developed a significant part of his research background in Prognostics and Health Management (PHM).

Email: juan.chiachioruano@nottingham.ac.uk

Most existing infrastructures in developed countries continue to grow in size and complexity, but also to age as a consequence of increasing demand of use. Accordingly, increasingly greater safety and economic challenges are coming into play in relation to the management of those infrastructures. Resilience engineering has recently emerged as key technology to deal with management frameworks capable to make infrastructures survive perturbations by adaptation and evolution but also by anticipation of changes in internal (and also environmental) conditions.

However significant challenges are nowadays waiting for researchers to unveil answers, such as tackling the highly complex interactions in infrastructures, how to integrate monitoring information from sensor networks into a resilience formulation framework, or how to make management models to autonomously learn and adapt as new (probably unexpected) perturbation arises, to name but a few. Enabled by today’s advances in sensor technology, information sciences and artificial intelligence, new management frameworks are possible with potential to fully exploit infrastructure operation within the constraints of safety and available resources. In this spirit, my research work at the Resilience Engineering Research Group has focused on research activities to explore, understand, and formulate theoretical methods capable to allow ageing infrastructures and asset management being considered from an artificial intelligence perspective. In general, I can say that my research focus on uncertainty quantification methods and algorithms, risk and reliability analysis, and artificial intelligence methods in application to a variety of engineering areas, which range from mechanical engineering to bioengineering applications.

Manuel Chiachio is Postdoctoral Research Fellow at the Resilience Engineering Research Group, University of Nottingham, UK. He holds a PhD in Structural Mechanics (Summa Cum Laude and international mention) awarded by the University of Granada, (Spain), a MSc in Civil Engineering (2007), and also a MSc in Structural Engineering (2011), by the same University. Manuel has worked as guest scientist at world-class universities and institutions, like Hamburg University of Technology (Germany), California Institute of Technology (Caltech), and NASA Ames Research Center (USA). This research has led to several publications in highly ranked journals and also invited lectures and talks in international conferences. He has been awarded by the National Council of Education of Spain through one of the prestigious FPU fellowships, by the Andalusian Society of promotion of the Talent, by the European Council of Civil Engineers (ECCM) with the Silver Medal prize in the 1st European Contest of Structural Design (2008), and also by the Prognostics and Health Management Society with a Best Paper Award in 2014. Prior to joining the University of Granada in 2011, Manuel worked as structural engineer for four years in top engineering companies in Spain.

Email: manuel.chiachio-ruano@nottingham.ac.uk

My project is to investigate how by considering whole system approaches to railway maintenance and considering whole life costs maintenance can be planned and completed more effectively.

I have so far focused mainly on the track asset; I’m trying to model around 100 miles of railway simultaneously.

Further into the project I’m hoping to work with other members of the risk and reliability research group so all assets can be simulated in the same model.

I’m using the hieratical Petri net modelling technique to create the whole system model

Rail Technical Strategy;

Capability Delivery Plan - https://www.rssb.co.uk/rail-technical-strategy/explore-the-capability-delivery-plan - Outlines how the rail industry needs to adapt over the next 30 years and how my research links into delivering the railway of the future.

I started work on my PhD in October 2015 and I’m currently in the second year of my PhD. I completed my undergraduate studies at the University of Nottingham obtaining a 1st in Mathematics with Engineering.  I have always been very interested in railways and trains, I’m very passionate about being part of the future of rail in the UK. And after finishing my PhD I would very much like to continue to work in the railway industry.

E-mail: jack.litherland@nottingham.ac.uk

Dr Dovile Rama is a Research Fellow in the Asset Management Section of the Resilience Engineering Research Group at the University of Nottingham. Her research interests are in 1) asset and system dependability, resilience and costs analysis, 2) asset degradation and intervention modelling with a view to support asset management strategies and 3) optimisation of system design, operation and maintenance strategies.

Current research activities include:

1) Development of methodologies for fault diagnostic and prediction of railway assets and other engineering systems.

2) Development of models for capturing and predicting railway asset deterioration profiles using physical models and various data sources.

3) Development of efficient tools for asset management decision optimisation.

4) Development of a holistic modelling framework for railway infrastructure asset management for assessment and prediction of risks faced by infrastructure owner under various intervention strategies.

Dr. Dovile Rama joined the University of Nottingham as a Research Fellow in Asset Management in 2011. Dr. Dovile Rama has a B.Sc. and M.Sc. (with distinction) degrees in Applied Mathematics and in 2010 she was awarded a Ph.D. in Systems, Risk and Reliability by Loughborough University. Prior to her appointment at the University of Nottingham Dovile worked for two years in the railway industry as a Safety Assurance Engineer.

Email: dovile.rama@nottingham.ac.uk

Development of mathematical and statistical methods, such as machine learning, data mining and Bayesian networks-based algorithms, in order to assess the health state of bridges and railway infrastructure by analysis the measurement of the infrastructure behaviour (acceleration, strain, displacements, etc.).

Project Website links: http://trussitn.eu/research/rail-and-road-infrastructure/esr9/

Matteo Vagnoli gained an MSc with honours in Nuclear Engineering (2014) from Politecnico di Milano in Italy. He worked as a research fellow and teaching assistant in collaboration with the Laboratory of Signal and Risk Analysis (LASAR) in 2015. Currently, he is a Marie-Curie fellow at the University of Nottingham. His work is centered on developing innovative methods of post-processing and on-line data analysis, computational methods for dynamic reliability analysis of critical systems, as well as methods for system health monitoring and fault diagnosis.

Matteo.vagnoli@nottingham.ac,uk

Matteo.vagnoli@gmail.com

Blog: http://esr9truss.blogspot.co.uk/

Publication: https://scholar.google.co.uk/citations?user=pRvEpW0AAAAJ&hl=en
                    https://www.researchgate.net/profile/Matteo_Vagnoli

The UK has a commitment to reduce greenhouse gas emissions by 80% by 2050. To achieve this the UK energy sector has to migrate towards supplying innovative, high quality, highly reliable, low or zero emission energy generation sources. Hydrogen and fuel cells have emerged as potential initiatives that could serve as alternative energy sources. They are currently being engineered for a range of applications including automotive, stationary power, aerospace and consumer electronics.

The area of focus of this research aims to improve the durability and reliability of this new energy source by better system integration and design optimisation, coupled with effective health management to maximise the life of the power source.

The outcome is a real time dynamic and adaptive intelligent lifecycle infrastructure with leading edge research in system design for reliability, prognostics and diagnostics, and semantically modeling relationships been the product and the environment for fuel cells, achieved through a multidisciplinary approach, including the areas of mathematics, information science and engineering.

MEng Systems and Control Engineering at Sheffield University 2009-2013,
PhD in Automotive Engineering at Loughborough University 2013-present.

Email: andrey.vasilyev@nottingham.ac.uk

The aim of the DaMSSLE project is apply risk and reliability engineering techniques to nuclear power installations, such as to be able to safety maximise their working lifespan through the optimisation of maintenance and monitoring procedures, extending existing methodologies for inclusivity of ageing phenomena. This work is conducted in conjunction with groups at Loughborough University, and at IGCAR and BARC of the Indian government’s Department of Atomic Energy.

The project is funded by EPSRC, grant reference EP/M018210/1, with further reading to be found at:

http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/M018210/1

http://gtr.ukri.org/projects?ref=EP%2FM018210%2F1

Mark Wootton completed an integrated Master of Physics at Loughborough University, graduating in 2013 with first-class honours.

In 2017, Mark was awarded a Ph.D. from Loughborough University. In his thesis, “Radiation Damage in Advanced Materials for Next Generation Nuclear Power Plants”, a study of the effects of radiation on high purity iron-chromium binary alloys was conducted by way of atomistic modelling methodologies, such as a Molecular Dynamics and Atomistic Kinetic Monte Carlo.

In late 2016, Mark took up a post-doctoral position at the University of Nottingham, applying methods such as Petri Net modelling to access risk and reliability concerns relevant to nuclear power stations in collaboration with the Indian research centres, BARC and IGCAR, as part of the DaMSSLE project.

Email: mark.wootton@nottingham.ac.uk