Data driven engineering to improve the performance of asset maintenance

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The main objective of the research would be to realise the full potential of data in asset management. Using Building Information Modelling (BIM) to start the data driven improvements to asset management bringing performance efficiencies. Currently available data and technologies are used during the design and construction phases but there are not processes in place to manage a handover of this to the client to facilitate ongoing asset management. The research would look into the barriers within the industry to understand any cultural, process and knowledge constraints, which mean that innovations are not carried through to the asset handover stage of a project. Research in this area has the potential to bring a number of industry efficiencies for stakeholders including; improved understanding of whole life asset management, maintenance efficiencies, greater level of service delivered to clients, development of bespoke maintenance information, harnessing additional data to inform the development of maintenance scenarios and planning and a movement towards proactive asset management. BIM has been heavily adopted during the design and construction phases but currently there is a disconnect with the asset handover and maintenance phases and my research would seek to close this gap. I would research the industry adoption of BIM within the construction of the asset to understand how it has been largely successful and to harness any learnings, consider any challenges and link this to the development of data driven asset management. This, alongside the implementation of industry guidance PAS1193-3:2014 and work underway to comply with the requirements set out in the UK Government Construction Standard 2025, would provide a framework from which to build my research and drive my eventual output. Research would then include building a picture of current asset management approaches undertaken by clients, future planned developments to comply with industry requirements and also a view of potential issues with the current methodologies and the clients view of the future shape of asset management. This knowledge of gaps, shortfalls and future requirements could then be used to build a view of the required solutions, which would then lead to an exploration of technologies and technical solutions which could be implemented to bring considerable improvements in data driven asset management.