A Level Crossing remains as one of the highest risk assets within the railway system often depending on the unpredictable behaviour of road and footpath users. For this purpose, interlocking through automated safety systems remains a key area for investigation. Within Europe, 2015–2016, 469 accidents at crossings were recorded of which 288 lead to fatalities and 264 lead to injuries. The European Union’s Agency for Railways has reported that Level Crossing fatalities account for just under 28% of all railway fatalities. This paper identifies suitable obstacle detection technologies and their associated algorithms that can be used to support risk reduction and management of Level Crossings. Furthermore, assessment and decision methods are presented to support their application. Finally state of the art and synergistic opportunity of which a combination of obstacle detection sensors with intelligent decisions layers such as Deep Learning are discussed which can provide robust interlocking decisions for rail applications. The sensor fusion of video camera and RADAR is a promising solution for Level Crossings. By applying additional sensing techniques such as RADAR imaging, further capabilities are added to the system, which can lead to a more robust approach.

Multiple projects within the rail industry across different regions have been initiated to address the issue of over-population. These expansion plans and upgrade of technologies increases the number of intersections, junctions, and level crossings. A level crossing is where a railway line is crossed by a road or right of way on the level without the use of a tunnel or bridge. Level crossings still pose a significant risk to the public, which often leads to serious accidents between rail, road, and footpath users and the risk is dependent on their unpredictable behavior. For Great Britain, there were three fatalities and 385 near misses at level crossings in 2015–2016. Furthermore, in its annual safety report, the Rail Safety and Standards Board (RSSB) highlighted the risk of incidents at level crossings during 2016/17 with a further six fatalities at level crossings including four pedestrians and two road vehicles. The relevant authorities have suggested an upgrade of the existing sensing system and the integration of new novel technology at level crossings. The present work addresses this key issue and discusses the current sensing systems along with the relevant algorithms used for post-processing the information. The given information is adequate for a manual operator to make a decision or start an automated operational cycle. Traditional sensors have certain limitations and are often installed as a “single sensor”. The single sensor does not provide sufficient information; hence another sensor is required. The algorithms integrated with these sensing systems rely on the traditional approach, where background pixels are compared with new pixels. Such an approach is not effective in a dynamic and complex environment. The proposed model integrates deep learning technology with the current Vision system (e.g., CCTV to detect and localize an object at a level crossing). The proposed sensing system should be able to detect and localize particular objects (e.g., pedestrians, bicycles, and vehicles at level crossing areas.) The radar system is also discussed for a “two out of two” logic interlocking system in case of fail-mechanism. Different techniques to train a deep learning model are discussed along with their respective results. The model achieved an accuracy of about 88% from the MobileNet model for classification and a loss metric of 0.092 for object detection. Some related future work is also discussed.

Millimetre-wave thermography is used to image through the soles of shoes as proof of principle study into the application of such an approach for security inspection. Current airport security screening practice necessitates the removal of shoes prior to x-ray screening for potential threats or other concealments, for example explosive or explosive precursor materials; narcotic substances or small weapons. The authors demonstrate that thermography at ∼ 250 GHz is able to reveal a variety of objects concealed within the soles of typical shoes, and that such an approach might be applied to rapidly screen passengers without necessitating the removal of their footwear.

Conventional track circuit condition monitoring systems are fixed at the wayside, with each installation reporting on a single track circuit. In this work, we present a custom-built, sensitive, magnetic field detection system, which can be fitted to the underside of a rail vehicle. With this system installed, some characteristics of an operating track circuit can be monitored from the vehicle whilst it is in motion. By using appropriate analysis techniques, it is possible to identify the signatures of equipment relating to audio frequency track circuits, the topic of this work. Analysing the signatures of track circuit equipment demonstrated that there were clear differences between track circuit assets. By building on other research into the behaviours of failing track circuits, and continuing to conduct this research, the authors believe that it is possible, and beneficial, to perform condition monitoring of track circuits from low-cost equipment mounted on the train. Coupling this with advanced analysis techniques will allow predictive maintenance of track circuits with very little capital outlay.

Buildings? functional and physical characteristics can be digitally represented through Building Information Modelling (BIM) which creates a sharing platform for all stakeholders involved in the project lifecycle. The application of BIM has been studied throughout the years mainly in new buildings with less emphasis in refurbishing existing ones due to the limited availability of accurate data of the original project. However, maintaining and refurbishing existing buildings often contributes to higher risk and cost. In addition, more buildings are required to undertake building assessment schemes such as Building Research Establishment Environmental Assessment Method (BREEAM), Leadership in Energy and Environmental Design (LEED), Green Star, Green Mark, and Green Building Index (GBI) ? most were not designed to assess existing buildings. In this sense, laser scanning can be used to collect existing parametric building data as point clouds, which can be developed and used for modelling of energy consumption, comfort and costing. This can be then translated back into assessment schemes. The aim of this paper is to demonstrate the process flow in the usage of laser scanning for existing buildings to support sustainability-led design by a new scan-to-BIM process.

Growing climate change challenges and increasingly strict sustainability standards have led to a significant growth in the need for building refurbishment projects which are essentially focused on retrofitting in order to make them low carbon, energy efficient and environmentally friendly. The Waste and Resources Action Programme (WRAP) suggested that Building Information Modelling (BIM) should be used to achieve sustainability requirements during refurbishment projects as a correspondence to the National Audit Office (NAO) sustainability report. BIM is now widely advocated as the preferred tool for the management and co-ordination of design and construction data using object- oriented principles. The successful integration of environmental assessment into BIM for the whole of the construction lifecycle has not yet been achieved. The potential for using BIM in refurbishment projects specifically for achieving and managing sustainability requirements has not been yet critically reviewed or put into practice. This paper focuses on the use of BIM sustainability design tools in refurbishment projects, to achieve energy efficient buildings and achieve sustainability criteria for refurbishing non-domestic buildings. A critical lens is cast on the current literature in the domains of sustainable designs and the associated implications of the sustainability decision-support tools in BIM. The research also reviews the practicality of the existing sustainability decision-support tools that are currently used to assist with achieving environmental scheme certifications such as BREEAM and LEED for refurbishment projects.

This thesis describes the research that led to the development of a machine vision system in collaboration with TATA, UK and Sheffield Supertram. This was part of a European initiative for Predictive Maintenance employing non-intrusive inspection and data analysis known as PM’n’Idea. The hardware and software design, construction, and evaluation of a prototype for predictive maintenance are presented. The prototype was tested on Sheffield and Warsaw’s tram systems. The prototype has been designed with due account of a specified set of environmental constraints such as a high level of vibrations and space restrictions of the target trams. Special computer vision techniques have been specifically developed to be used with the prototype. Various image processing techniques and algorithms have been evaluated for the purpose of detection and characterisation of a series of rail abnormalities and faults. The system described in this thesis makes use of a number of standard and modified image processing techniques, not only to alleviate the requirements for manual inspections, but also to allow continuous monitoring and tracking of any defects or abnormalities in a rail track. Currently, detecting defects in their earlier stages can only be achieved by using close visual inspection i.e. line walking. Extensive testing and evaluation of the performance of the prototype inspection system at Sheffield Supertram indicated that the system was able to detect abnormalities with a resolution down to 0.1 mm. Evidence of the classification rates for the standard and modified algorithms that are implemented in the system are presented in this thesis. The algorithms developed show an average success rate of 88.9% in detecting surface bound abnormalities.

Aim: This study set out to explore whether a teaching programme developed and delivered by medical students yielded an improvement of attendees' examination performance or their experience of preparing for the target assessment.Methods: Over 4 years all students were invited to consent to use of their official examination data. Students were ranked for baseline performance and again for performance in the target assessment. Change in rank was compared for attendees and non-attenders. Additionally, a questionnaire was distributed to students before and after the peer-led programme. Attendees' responses were compared to those of non-attenders.Results: No statistically significant difference in change in rank was observed between the two groups on evaluation of quantitative performance data. The majority of students (81.0%) scored the programme four or five in terms of perceived usefulness on a five-point Likert scale. Attendees reported statistically significant increases in preparedness for the examination (p=0.001) and in familiarity with the style of examination questions (p=0.004) compared to students who did not attend.Conclusion: This study suggests that teaching from peers may improve students' perception of their preparedness for official assessments. However, such interventions may be limited in their ability to produce a demonstrable benefit in terms of examination performance.

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