Christopher Johnson

Bio: Christopher Johnson is an academic researcher from Manchester Metropolitan University. The author has contributed to research in topics: Building information modeling & Sustainable design. The author has an hindex of 3, co-authored 8 publications receiving 46 citations.

Sustainability-led design: Feasibility of incorporating whole-life cycle energy assessment into BIM for refurbishment projects

Abstract: 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.

Object Detection at Level Crossing Using Deep Learning.

Abstract: 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.

Digitisation of existing buildings to support building assessment schemes: viability of automated sustainability-led design scan-to-BIM process

Abstract: 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 pr...

A review of the technological developments for interlocking at level crossing

Abstract: 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 throug...

Image processing techniques for the detection and characterisation of features and defects in railway tracks

Abstract: 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.

Thermodynamics and statistical mechanics

Abstract: Thermodynamics and Statistical Mechanics. By P.T. Landsberg (Oxford University Press: Oxford, 1979.) £9.75.