Infrastructure Engineering - Theses
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ItemMulti-objective optimisation of a prefabricated house in Australian climate zones( 2020)Quantitative evaluations of indoor environmental quality (IEQ) along with energy efficiency are amongst the key features of environmentally sustainable buildings. Buildings are responsible for a significant portion (nearly 40%) of the world’s total energy consumption and GHG emissions. On the other hand, operational phase contributes to the greater part of the building’s energy consumption. Along with energy efficiency, IEQ is another aspect that has been attracting significant attention in the field of sustainable building design. People spend about 90% of their time indoors. Therefore, the comfort levels and satisfaction of indoor environment can easily affect the quality of their daily life. The IEQ parameters considered in this thesis include thermal comfort, visual comfort, auditory comfort and indoor air quality (IAQ). While prefabrication offers substantial benefits to the construction industry through quality assurance, time savings and waste reduction, it tends to transform the construction process and components which can affect the buildings’ performance in both positive and negative ways. Understanding the effects of the prefabricated building components on energy performance and IEQ will inform the design decisions which can lead to the creation of more sustainable buildings with high quality. Although previous research has focused on the benefits and limitations of prefabrication in housing, there has been little quantitative analysis on how various envelope components may affect several performance parameters including energy consumption and IEQ of residential buildings. Along with that, there is still a lack of systematic design methods and decision support which can lead to design solutions to improve both sustainability and affordability aspects. These issues constitute the main knowledge gaps, leading to the identification of the research aim as: ‘The aim of this thesis is to optimise the envelope components of a prefabricated house to minimise thermal discomfort hours (TDH), daylight unsatisfied hours (DUH) and life cycle costs (LCC) while meeting the requirement of Australian National Construction Code (NCC) on energy efficiency and IEQ performance’. The focus of this study is on a prefabricated house in various Australian climates. Building performance optimisations with multi-objectives in early stages of design have been conducted to minimise LCC while maintaining the satisfactory indoor environment. A framework was developed to conduct multi-objective optimisation of a selected residential building. The framework is the structure developed for conducting multi-objective optimisation in early design stages through a number of sequential rational steps. The steps include model development, model validation, sensitivity analysis, development of component’s library and multi-objective optimisation. As the result of optimisations, the optimal combinations of envelope components were presented in the form of Pareto optimal solutions. The optimal solutions achieved 27-31% savings in LCC compared to the baseline while the reductions for TDH varied between 6% and 55%. As a result of trade-offs, the selected compromised solutions in each climate could achieve better reductions for either TDH, LCC or both.The optimal solutions, as well as recommended best compromised solutions, provided useful insight and decision support towards the design solutions that minimise LCC while providing satisfactory indoor environment. The developed framework for building optimisation in early stages can be used by designers and building performance simulation practitioners across any types of buildings.
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ItemImproving building energy efficiency: biomimetic adaptive façade and computational data-driven approach( 2020)The urbanisation and population growth are resulting in a significant increase in energy consumption in buildings, leading to a substantial increase in greenhouse gas (GHG) emissions. During the operation of buildings, a massive amount of GHG emissions are released due to the process of building heating, cooling, and lighting, which accounts for the most significant proportion in building energy consumption. Therefore, energy-efficiency design and operation will play an essential role in reducing GHG emissions in buildings. Facade systems are one of the most critical aspects regarding the efficient management of heating, cooling, and lighting energy in buildings. A facade system is a barrier and exchanger (simultaneously) for temperature, light, and air between the building indoor environment and the outside environment. Therefore, the proper design and operation of the facade can effectively save substantial energy. For decades, engineers and researchers from all over the world have been in search for the intelligent design and operation of the facade systems to improve energy efficiency and sustainability in buildings, and to not compromise a pleasant indoor environment for building occupants. Subsequently, they have found that many natural systems have developed a highly efficient biological structure to adapt to dynamic and extreme environments over millions of years. These natural systems now have become great inspirations for the research community in the quest for building energy efficiency solutions, and the biomimetic adaptive facade (BAF) system is one of those remarkable examples of adopting bioinspiration in buildings. The BAF system is considered as a potential solution to improve the performance of conventional facade systems. The BAF system has an ability to adapt its functions, features, or behaviour for dynamically varying climatic conditions, providing buildings with the operational flexibility to act in response to different climate scenarios. Nonetheless, the practical application of a BAF in buildings remains limited due to the absence of a comprehensive design platform that can facilitate the widespread adoption of BAF systems. Most studies on BAFs remain at a conceptual stage of development, and an effective platform that can effectively assist the design and operation of BAF is still lacking. This thesis proposes and develops a methodology for enhancing building energy efficiency using the design of BAF systems, and thereby supports the transition to next-generation facades. Specifically, the objective of this thesis is to develop, test, and evaluate a computational data-driven optimisation approach in assisting the BAF design. The thesis presents a multidisciplinary approach that combines building energy modelling, metaheuristic optimisation, and data-driven methods. The goal of the proposed approach is to minimise the total energy consumption in buildings, including heating, cooling, and lighting energy, but still maintain the indoor environmental quality in terms of thermal and visual performance. A comprehensive analysis of the proposed computational data-driven optimisation approach is provided in the thesis. In summary, this study has proposed a computational data-driven approach based on building energy simulations, optimisation processes, and machine learning algorithms. The proposed approach is used to assist the design and operation of BAFs for building energy efficiency and analyse the interactions between energy-saving and indoor environmental quality. These significant findings demonstrate the potential of BAFs to enhance the energy efficiency of buildings, and the developed platform can be used as an effective tool to support BAFs in both design and product development.