PhD Students

Cross-Laminated Timber (CLT) is a material composed of alternating orthogonally layered timber planks affixed with adhesives to form large panels for construction purposes. Orthogonal layering of the highly anisotropic timber provides CLT with increased bidirectional strength in- & out-of-plane. Increased mechanical properties therefore enable creation of multi-storey structures up to 10-15 storeys tall entirely composed of CLT panels as walls and floor slabs without requirement for additional beams or columns. However, CLT exhibits brittle failure mechanics (as does timber) whilst orthogonal laminations and natural material variations complexify calculations of material response. Due to the brittle behaviour, all structural ductility must be provided by steel connections between CLT panels to provide robust designs. Most definitions of structural robustness in research are qualitative and the few quantitative methods are typically based on structural redundancy and minimum energies required to induce collapse. The objective of the research is therefore to present a more appropriate measure of structural robustness of CLT buildings with comparison and adaptation of existing methods. A secondary research goals is therefore to determine whether any form of CLT or CLT hybrid structure can be considered structurally robust. This secondary objective is given greater pertinence given the increase in the number of high-rise CLT structures being built in over the last 5-10 years alongside the current status of inadequate design codes & industry led design.

Before I began research towards a PhD in October 2018, I attained a 1st MEng in Civil Engineering at the University of Nottingham. Highlights of my undergraduate included an individual research project using Bayesian updating with accelerometer data on a reinforced concrete bridge in undamaged and imposed damaged states to predict the loss of structural strength. Additionally, I led the structural design for a proposed Marine Renewable Test Centre which used curved & tapered symmetric pinned Glue-Laminated Timber portal frames. Frames required design checks from first principles given the limitations of codified design for engineered wood products. I also have industry experience for structural engineering particularly for firms specialising in steel from fabrication and design to construction.

Oil and gas facilities such as vessel-based systems have more chances to be subjected to faults and hazardous conditions due to the flammable materials stored in these systems. However, if these faults are not accurately detected on time it may result in costly and serious incidents such as explosion and emergency shutdown of the entire production system.

My research is on fault detection and failure diagnosis for oil and gas facility using dual approaches; Bayesian Belief Network BBN and Petri Nets. Specifically, I am working on using Bayesian Stochastic Petri Nets (BSPN) formalism for developing a fast and accurate fault diagnostic model for three-phase separator. 

I graduated with a Bachelor of Technology degree in Computer Engineering from Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria, in 2011. I also obtained a Masters of Technology degree in Computer Science from LAUTECH in 2018. I am currently a PhD student in the Resilience Engineering Research Group, Faculty of Engineering. 

There could be several variations in delivering a medical treatment for a specific patient condition even in a single healthcare institution. These variations have consequences on patient safety and clinical process efficiency. In this research, I try to develop a methodology to model a clinical practice considering its variations in order to develop an understanding on its effect on patient’s related outcome and operational performance.

In the beginning, this research aims to find a method that can properly map or capture the variations, which may come from the patient condition, medical staff knowledge and skill, resource availability, medical team interaction, decision-making process, and so on. The challenge of this research would also come from the effort to develop or find a method that can accurately measure the sources of variations and gather the data without much interference to medical staff activities. Hence, automatic measurement method would be considered.

In the end, the model and its related data input will be used to get an insight about how a certain clinical practice behaves and furthermore highlight the critical aspect of certain clinical practice that should be taken care of to assure an acceptable quality of outcome.  

Email: alfian.tan@nottingham.ac.uk

It is obviously desirable to maximise performance of the Hydrogen Refuelling Stations not just for economic reasons, but to deliver the best customer experience. This project seeks to optimise the plant operation, where planning preventative maintenance can help reduce disruption to service and improve the commercial case of a plant.

Salim graduated from The University of Huddersfield where he studied Electrical/Electronic Engineering. He completed his MSc in Powertrain Engineering from IFP School (École Nationale Supérieure du Pétrole et des Moteurs) in 2020. His interest is on hydrogen technology infrastructure and vehicle applications.

High-speed railway infrastructure is a complex arrangement of systems and structures, which includes: track, switches, drainage, signalling, power supply and communications, in addition to the civil structures comprising earthworks, tunnels, bridges and stations. As new high-speed railways are built, it is important that plans are in place to ensure they are sustainable and affordable so that elements can be renewed as they wear out or become obsolete due to new technologies. The funding for renewals is generated through a usage charge levied by the infrastructure owners on the train operating companies. However, should this charge be too little then necessary replacements will not take place, or, in the event that it be too much, the consequence is higher than necessary fares for passengers.

To be able to fix and justify the right charge requires advances in areas of engineering. It is necessary to understand how all the infrastructure elements degrade due to either the passage of time or use. This enables the estimation of when replacement is necessary, and can be achieved using modelling methods which include artificial intelligence (AI). For systems and structures made of many components, aging at different rates, there is the additional challenge of combining the component performance predictions to give the performance of the entity.

Having established the degradation mechanisms for each of the asset types it is then necessary to plan when the renewals will be performed. This decision will be based on the costs incurred when selecting the alternative decisions and can be optimised. Some assets will degrade more slowly than others and so have more flexibility in fixing the exact renewal date. There are also practical considerations which will minimise any consequential service disruption.

Utilising the asset degradation models enables the asset renewal schedule to be produced in such a way as to minimise the costs to the infrastructure owner. This will be achieved through the definition of an optimisation problem which minimises the whole system costs and satisfies constraints which account for the practicalities of performing the renewals to have minimal impact on the service provision. The costs included in the objective function will include the costs of the renewals, the costs of the penalties for service disruption and the costs associated with additional maintenance to keep aging assets in an acceptable condition beyond the predicted renewal time. The modelling will account for the uncertainty in both the costs and the degradation profiles. Once the model is created it would enable the new schedules to be determined in the event that track utilisation (volumes of freight and passenger traffic) changed. 

The UK's Rail Technical Strategy (RTS) 2020 sets a 20-year vision for railway service quality with a focus on safety, reliability, and resilience, as well as meeting capacity and service requirements through innovation and technology. This is a major challenge when, in addition to component or system failures and human errors, modern railways also experience threats such as cyber-attacks, natural hazards and climate change. Current rail industry practice for choosing recovery actions after a disruption is based on pre-written contingency plans and, to a degree, relies on controller judgement, but the various options cannot be explored automatically. To ensure quick, effective recovery and minimise disruption, there is a need for an optimization-based decision-making procedure to be developed.

During my PhD, my research focuses on developing innovative solutions to build asset management models of wind turbine blades integrating SHM process and the SHM reliability. The details are described below:

(1) Guided wave based SHM: Develop guided wave-damage interaction models. Explore physics-based Bayesian inference models to produce damage characterisation frameworks.

(2) Reliability of SHM systems: Explore a novel method of evaluating SHM system reliability using Petri nets, information theory            and Bayesian inference.

(3) Asset management of wind turbines: Develop a wind turbine blade asset management Petri net model incorporating risk-based maintenance and an SHM process.

This work presents a probabilistic mathematical model to analyse and predict water distribution network resilience, aiding managers in optimal repair decisions during disruptions. The model evaluates the network's ability to withstand and recover from disruptions, considering parameters like pipe diameter and probabilistic factors such as demand fluctuations, failure rates, and mean time to respond. The authors underscore that integrating a probabilistic model, considering real-world uncertainties, is crucial for optimizing decision-making in management and repairs and enhancing water network resilience.

I am currently conducting research aimed at improving the locomotive fleet management system. The locomotive/train fleet management system is complex, requiring innovative approaches and methodologies to maintain the high productivity of the system. In the research, a model of the system will be developed to predict the effectiveness of different train maintenance strategies. The model will be applied to the fleet of trains used to provide service on a railway network. The prediction will account for practical limitations in the financial and equipment resources of the system aiming to optimise service provision.

The condition of bridges and their components is the result of two key processes: deterioration and maintenance. To effectively maintain any network, asset managers typically use life-cycle models to forecast the condition of bridges and bridge components, enabling effective allocation of resources to maximise condition and safety and minimise risk. Accurate modelling requires detailed understanding of both processes. However, data on interventions and their impact is often sparse and incomplete, leading to inaccurate estimations of the impact of maintenance work. This makes it difficult to separate the contribution of the two processes to the condition record, leaving artefacts of undetected interventions and causing slower predictions for deterioration rates.  

In this research, a novel approach is being undertaken to model the behaviour of defects on bridge components through progression of deterioration; inspection; decisions on the application of maintenance work; and repair and replacement activities. Using Coloured Petri Nets to model defects on a portfolio of bridges, the model simulates real-world behaviour and practices taken by Network Rail, who own and manage over 26 000 railway bridges in the UK.   

This work uses extensive real condition data and limited intervention records to calibrate deterioration rates, and draws upon detailed industry expert knowledge to replicate the processes and logic taken by Network Rail in applying maintenance work, to create a comprehensive forecasting model. The project aims to output deterioration rates with improved accuracy than the current approach, and a detailed understanding of intervention processes and effectiveness