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 thesis investigates approaches to classify and anticipate the energy consumption of commercial office buildings using external and performance benchmarking to reduce the energy consumption. External benchmarking in the context of building energy consumption considers the influence of climate zones that significantly impact a building's energy needs. Performance benchmarking recognizes that different types of commercial buildings have distinct energy consumption patterns. Benchmarks are established separately for each building type to provide relevant comparisons.</p><p dir="ltr">The first part of this thesis is about providing a benchmarking baseline for buildings to show their consumption levels. This involves simulating the buildings based on standards and developing a model based on real-time results. Software tools like Open Studio and Energy Plus were utilized to simulate buildings representative of different-sized structures to organize the benchmark energy consumption baseline. These simulations accounted for two opposing climate zones—one cool and humid and one hot and dry. To ensure the authenticity of the simulation, details, which are the building envelope, operational hours, and HVAC systems, were matched with ASHRAE standards.</p><p dir="ltr">Secondly, the neural network machine learning model is needed to predict the consumption of the buildings based on the trend data came out of simulation part, by training a comprehensive set of environmental characteristics, including ambient temperature, relative humidity, solar radiation, wind speed, and the specific HVAC (Heating, Ventilation, and Air Conditioning) load data for both heating and cooling of the building. The model's exceptional accuracy rating of 99.54% attained across all, which comes from the accuracy of training, validation, and test about 99.6%, 99.12%, and 99.42%, respectively, and shows the accuracy of the predicted energy consumption of the building. The validation check test confirms that the achieved accuracy represents the optimal performance of the model. A parametric study is done to show the dependency of energy consumption on the input, including the weather data and size of the building, which comes from the output data of machine learning, revealing the reliability of the trained model. Establishing a Graphic User Interface (GUI) enhances accessibility and interaction for users. In this thesis, we have successfully developed a tool that predicts the energy consumption of office buildings with an impressive accuracy of 99.54%. Our investigation shows that temperature, humidity, solar radiation, wind speed, and the building's size have varying impacts on energy use. Wind speed is the least influential component for low-rise buildings but can have a more substantial effect on high-rise structures.</p>

<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>