Engineering asset management (EAM) is a new concept about inter-disciplinary field that combines technique issues of asset reliability, safety and performance with financial and managerial requirements. However, there are few literatures in research and application cases from industries. This thesis takes the Five hundred meters Aperture Spherical Telescope (FAST) project as a case to explore how EAM was processed in large engineering projects. The aim of this study is to figure out the key elements of EAM in the projects like FAST and to develop an EAM model that is suitable for this kind of projects. FAST will be the biggest single radio telescope in the world, that being built in a natural Karst basin in Guizhou, China. In this study, qualitative research and case study were adopted. The related knowledge of EAM was collected from the scientific literature, which helped access the initial theoretical framework. The details of FAST project, which includes the fundamental data and the first-hand information, are from the interviews and surveys. By applying EAM to the project management of FAST, the shortcoming of existing EAM model has been noticed. The existing model mainly focuses on cost-saving and profit-achieving, while ignoring environment and risk management. In order to make EAM model more efficient and practical, this thesis provides a tailored EAM model that could be suitable for large engineering projects like FAST.

Engineering asset management (EAM) organisations are increasingly motivated to implement business intelligence (BI) systems in response to dispersed information environments and regulatory requirements. However, the implementation of a BI system is a complex undertaking requiring considerable resources. To date, there has been only a limited authoritative set of critical success factors (CSFs) for management reference because the BI market is a relatively new area that has been driven mainly by IT industry and vendors. There is an imperative to explicitly focus on, and rigorously specify, the CSFs that impact on the implementation of BI systems. Consequently, this research seeks to bridge the gap that exists between academia and practitioners. It addresses the challenging problems in implementing BI systems by investigating the CSFs and their associated contextual issues with EAM organisations.

<p dir="ltr">This research aims to address these critical challenges in uncertainty quantification. The objective is to employ data-driven approaches for UQ with physics models.</p>