Namini, Seyed Saeed Banihashemi (2017) Active BIM with artifical intelligence for energy optimisation in buildings. PhD thesis, University of Technology Sydney, Australia.
Abstract
Using Building Information Modelling (BIM) can expedite the Energy Efficient Design (EED) process and provide the opportunity of testing and assessing different design alternatives and materials selection that may impact on energy performance of buildings. However, the lacks of; intelligent decision making platforms, ideal interoperability and inbuilt practices of optimisation methods in BIM hinder the full diffusion of BIM into EED. This premise triggered a new research direction known as the integration of Artificial Intelligence (AI) into BIM-EED. AI can develop and optimise EED in an integrated platform of BIM to represent an alternative solution for building design. But, very little is known about achieving it. Hence, an exhaustive literature review was conducted on BIM, EED and AI and the relevant gaps, potentials and challenges were identified. Accordingly, the main goal for this study was set to optimise the energy efficiency at an early design stage through developing an AI-based active BIM in order to obtain an initial estimate of energy consumption of residential buildings and optimise the estimated value through recommending changes in design elements and variables. Therefore, a sequential mixed method approach was designated in which it entailed conducting a preliminary qualitative method to serve the subsequent quantitative phase. This approach was started with a comprehensive literature review to identify variables applicable to EED and the application of a three-round Delphi to further identify and prioritise the significant variables in the energy consumption of residential buildings. A total of 13 significant variables was achieved and factualised with simulation method to first; generate the building energy datasets and second; simulate AI algorithms to investigate their functionality for energy optimisation. The research was followed with developing the integration framework of AI and BIM; namely AI-enabled BIM-inherited EED to optimise the interdisciplinary data of EED in the integration of BIM with AI algorithm packages. Finally, the functionality of the developed framework was verified using a real residential building and via running comparative energy simulation pre and post-framework application (baseline and optimized case). The outcomes indicated around 50% reduction in the electricity energy consumption and 66% saving in the annual fuel consumption of the case study. Enhancing BIM applicability in terms of EED optimisation, shifting the current practice of post-design energy analysis, mitigating the less integrated platform and lower levels of interoperability are the main significant outcomes of this research. Ultimately, this research heads toward the higher diffusion levels of BIM and AI into EED which contributes significantly to the current body of knowledge and its research and development effects on the industry.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Ding, Grace |
| Uncontrolled Keywords: | artificial intelligence; building design; building information modelling; case study; decision making; energy analysis; energy consumption; energy efficiency; energy performance; integration; interoperability; optimisation; residential; simulation |
| Index terms: | dataset, body of knowledge, research and development, integration, decision-making, functionality, literature review, energy-efficient design, qualitative method, energy simulation, fuel consumption, energy performance, estimate, testing, interoperability, artificial intelligence, early design stage, energy consumption, energy analysis, building design, building information modelling, energy efficiency, residential building, mixed method, platform, package, case study |
| Subjects: | data collection methods, research design and methodology, modelling and simulation, sustainability and energy, artificial intelligence, data analysis and analytics, energy systems, financial and cost management, organizational analysis, data management, decision analysis, information systems, systems and processes, construction type, design features, professional practice, knowledge management, contractual arrangements, architectural design, sustainable design, design process, digital design, research management, engineering analysis |
| Topics: | Engineering Principles, Risk Management, Procurement, Sustainability, Design Practice, Digital Applications, Organizational Design, Cost Management, Research Practice, Information Management, Construction Technology |
| Descriptive scope: | 4 PCEA |
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