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院校 題目 類型 日期 作者 摘要 網頁
HKUST Parametric modeling and evolutionary optimization for cost-optimal and low-carbon design of high-rise reinforced concrete buildings Journal 07/2019 Gan, V.J.L., Wong, C.L., Tse, K.T., Cheng, J.C.P., Lo, I.M.C., and Chan, C.M. Design optimization of reinforced concrete structures helps reducing the global carbon emissions and the construction cost in buildings. Previous studies mainly targeted at the optimization of individual structural elements in low-rise buildings. High-rise reinforced concrete buildings have complicated structural designs and consume tremendous amounts of resources, but the corresponding optimization techniques were not fully explored in literature. Furthermore, the relationship between the optimization of individual structural elements and the topological arrangement of the entire structure is highly interactive, which calls for new optimization methods. Therefore, this study aims to develop a novel optimization approach for cost-optimal and low-carbon design of high-rise reinforced concrete structures, considering both the structural topology and individual element optimizations. Parametric modelling is applied to define the relationship between individual structural members and the behavior of the entire building structure. A novel evolutionary optimization technique using the genetic algorithm is proposed to optimize concrete building structures, by first establishing the optimal structural topology and then optimizing individual member sizes. In an illustrative example, a high-rise reinforced concrete building is used to examine the proposed optimization approach, which can systematically explore alternative structural designs and identify the optimal solution. It is shown that the carbon emissions and material cost are both reduced by 18–24% after performing optimization. The proposed approach can be extended to optimize other types of buildings (such as steel framework) with a similar problem nature, thereby improving the cost efficiency and environmental sustainability of the built environment. 連結
HKUST Multi-zone indoor CFD under limited information: An approach coupling solar analysis and BIM for improved accuracy Journal 10/2020 Kwok, H.H.L., Cheng, J.C.P., Li, A.T.Y., Tong, J.C.K., and Lau, A.K.H. It is important to monitor the indoor air quality and thermal comfort of an office environment for the wellbeing of its occupants, and, to do so, computational fluid dynamics simulation is more cost-effective than measuring an entire floor. Computational fluid dynamics simulation has been used by previous studies for single rooms and partitioned spaces, but not for office floors with multi-zone ventilation systems, and air infiltrations between different zones through closed doors have been neglected. Also, since it is often not possible to take measurements across an entire floor due to concerns of tenant privacy, few studies have used the limited obtainable field measurements to validate multi-zone computational fluid dynamics simulations. This study describes a methodology to conduct indoor multi-zone steady-state computational fluid dynamics simulation, with improved accuracy, on a typical office floor where there is limited information on carbon dioxide concentrations and temperatures. Heat and mass conservation equations were used to compensate for the lack of information. The mechanical ventilation and air conditioning layout was considered along with the sources of heat and carbon dioxide emissions. To improve the accuracy of the simulation on temperature, a solar analysis, based on building geometry, orientation, materials, location, and weather, was conducted to estimate any solar heat gain and distribution through curtain walls. Building information modeling supported the solar analysis and provided geometric information for the computational fluid dynamics simulation. The methodology was validated by a real case of a commercial building, where the accuracy of the temperature simulation improved by 9.9%. 連結
HKUST Simulation-based evolutionary optimization for energy-efficient layout plan design of high-rise residential buildings Journal -- Gan, V.J.L., Wong, H.K., Tse, K.T., Cheng, J.C.P., Lo, I.M.C., and Chan, C.M. Buildings consume 40% of global energy, in which residential buildings account for a significant proportion of the total energy used. Previous studies have attempted to optimize the layout plan of residential buildings for minimizing the total energy usage, mainly focusing on low-rise houses of a regular shape and having a limited number of design variables. However, layout design for high-rise residential buildings involves the complicated interaction among a large number of design variables (e.g., different types of flats with varying configurations) under practical design constraints. The number of possible solutions may increase exponentially which calls for new optimization strategies. Therefore, this study aims to develop an energy performance-based optimization approach to identify the most energy-efficient layout plan design for high-rise residential buildings. A simulation-based optimization method applying the evolutionary genetic algorithm (GA) is developed to systematically explore the best layout design for maximizing the building energy efficiency. In an illustrative example, the proposed optimization approach is applied to generate the layout plan for a 40-storey public housing in Hong Kong. The results indicate that GA attempts to maximize the use of natural-occurring energy sources (e.g., wind-driven natural ventilation and sunlight) for minimizing 30–40% of the total energy consumption associated with air-conditioning and lighting. The optimization approach provides a decision support basis for achieving substantial energy conservation in high-rise residential buildings, thereby contributing to a sustainable built environment. 連結
HKUST Holistic BIM framework for sustainable low carbon design of high-rise buildings Journal 06/2018 Gan, V.J.L., Deng, M., Tse, K.T., Chan, C.M., Lo, I.M.C., and Cheng, J.C.P. In high-density, high-rise cities such as Hong Kong, buildings account for nearly 90% of energy consumption and 61% of the carbon emissions. Therefore, it is important to study the design of buildings, especially high-rise buildings, so as to achieve lower carbon emissions. The carbon emissions of a building consist of embodied carbon from the production of construction materials and operational carbon from energy consumption during daily operation (e.g., air-conditioning and lighting). While most of the previous studies concentrated mainly on either embodied or operational carbon, an integrated analysis of both types of carbon emissions can improve the sustainable design of buildings. Therefore, this paper presents a holistic framework using building information modeling (BIM) technology in order to enhance the sustainable low carbon design of high-rise buildings. BIM provides detailed physical and functional characteristics of buildings that can be integrated with various environmental modeling approaches to achieve a holistic design and assessment of low carbon buildings. In a case study, the proposed framework is examined to evaluate the embodied and operational carbon in a high-rise residential building due to various envelope designs. The results demonstrate how the BIM framework provides a decision support basis for evaluating the key carbon emission sources throughout a building's life cycle and exploring more environmentally sustainable measures to improve the built environment. 連結
HKUST An integrated underground utility management and decision support based on BIM and GIS Journal 08/2019 Wang, M., Deng, Y., Won, J., and Cheng, J.C.P. This study aims to improve the underground utility management efficiency from the perspective of utility component and urban utility network, as well as to facilitate the decision-making for utility maintenance work. The main reasons for the inefficient information sharing, poor utility management and reactive decision-making are investigated, after which potential solutions are explored. An integrated utility management framework is proposed based on the integration of Building Information Modeling (BIM) and Geographic Information System (GIS), for which a common utility data model representing utility information in five aspects is developed to facilitate the mapping of Industry Foundation Classes (IFC) and City Geography Markup Language (CityGML). The verification of the proposed framework indicates that the developed data model can represent utility information comprehensively, based on which functions of the integrated BIM-GIS platform are developed to support underground utility management in terms of individual utility components and the utility spatial networks. With the proposed utility management framework, the information sharing process, utility management efficiency and decision-making can be improved and facilitated. In the future, more functions of the framework will be developed according to practical requirements and more maintenance data will be utilized to validate and enhance the framework. 連結
HKUST Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms Journal 01/2020 Cheng, J.C.P., Chen, W., Chen, K., and Wang, Q. Facility managers usually conduct reactive maintenance or preventive maintenance strategies in building maintenance management. However, there are some limitations that reactive maintenance cannot prevent failure, and preventive maintenance cannot predict the future condition of MEP components and repair in advance to extend the lifetime of facilities. Therefore, this study aims to apply a predictive maintenance strategy with advanced technologies to overcome these limitations. Building information modeling (BIM) and Internet of Things (IoT) have the potential to improve the efficiency of facility maintenance management (FMM). Despite the significant efforts that have been made to apply BIM and IoT to the architecture, engineering, construction, and facility management (AEC/FM) industry, BIM and IoT integration for FMM is still at an initial stage. In order to provide a better maintenance strategy for building facilities, a data-driven predictive maintenance planning framework based on BIM and IoT technologies for FMM was developed, consisting of an information layer and an application layer. Data collection and data integration among the BIM models, FM system, and IoT network are undertaken in the information layer, while the application layer contains four modules to achieve predictive maintenance, namely: (1) condition monitoring and fault alarming module, (2) condition assessment module, (3) condition prediction module, and (4) maintenance planning module. Machine learning algorithms, ANN and SVM, are used to predict the future condition of MEP components. Furthermore, the developed framework was applied in an illustrative example to validate the feasibility of the approach. The results show that the constantly updated data obtained from the information layer together with the machine learning algorithms in the application layer can efficiently predict the future condition of MEP components for maintenance planning. 連結