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香港專上院校所提供之論文/研究刊物
| 院校 | 題目 | 類型 | 日期 | 作者 | 摘要 | 網頁 |
|---|---|---|---|---|---|---|
| HKUST | Construction Lift Planning for Prefabricated Units Based on Building Information Modelng and Optimization Techniques | FYP | 06/2017 | LEE, Hoi Yin LO, Kwong Ching |
In recent years, prefabricated construction has been increasingly employed in building projects, especially in vertical extension of existing building. However, current lift planning mainly relies on experience and instinct of site manager, leading to potentially poor lifting schedule that may incur extra time and costs on lifting operations. This project presents a BIM-based lift planning framework for prefabricated modules in vertical extension project that aims to optimize the lifting schedule of prefabricated modules and provide visualization for actual lifting path of the modules. The framework considers three main models: (1) information extraction and geometry simplification model to obtain the module information and simplify the shape of modules, (2) analysis model to calculate the actual lifting path distance of each prefabricated module, and (3) optimization model for the selection of ideal lifting schedule using genetic algorithm (GA). An illustrative example is presented to illustrate and evaluate the proposed framework. The results show that the proposed framework can generate the shortest lifting path for each prefabricated module automatically. The lift planning for prefabricated modules in vertical extension project can be significantly improved by the developed framework. | N.A. |
| HKUST | Construction Planning of Prefabricated Units Leveraging BIM and Resource Leveling Techniques | FYP | 06/2018 | WONG, Kok Yiu YEUNG, Ching Hei |
As a compact city with limited amount of available land and vast population, Hong Kong is currently facing the massive demand for housing. This phenomenon has been driving the construction industry to enhance the productivity of construction projects, particularly for residential buildings. In recent years, the Hong Kong government has been investigating the feasibility of Modular Integrated Construction (MIC). MIC refers to a construction method where volumetric modules are prefabricated in factories and then assembled at a construction site to form a building. The productivity of this method has been demonstrated by numerous projects in foreign countries, such as Singapore and China. In view of the proven benefits of MIC, the Hong Kong government has proposed three pilot projects recently, which will adopt MIC to construct resident buildings. However, the local industry possesses insufficient experience in managing this kind of construction projects. This report presents an optimization framework, which integrates Genetic Algorithm (GA) and Building Information Modeling (BIM) to perform resource leveling based on constraints of a MIC project. An illustrative case demonstrates the functionalities of GA and BIM in optimizing the schedule of a MIC project. The proposed framework aims to provide the industry practitioners with a general guideline for scheduling a MIC project. | N.A. |
| HKUST | Creating a Connected Digital Twin of HKUST Campus for Smart Campus Facility Management | FYP | 06/2020 | FONG, Tsz Yan KONG, Yu Hin |
Experts in engineering defines BIM as a representation of a digital twin which is a virtual replica of a physical system (Marr 2017). A digital twin provides rich semantic and geometric information for facilitating construction and FM processes. Through Facility Management Systems (FMSs) and Building Management Systems (BMSs) linked with sensors, information can be garnered to support building FM. FMS or BMS is a computer-based system installed in offices or buildings ensuring that all buildings are structurally sound and serviceable. In this research, we initially plan to incorporate two common FM software, namely ArchiBUS and Maximo with the HKUST FM system for the sake of maximizing the FM effectiveness and facilitating FM process. However, we did not get either one of the licenses of both software, so it turns out that we have to use other machine learning set of tools to do predictions for our library. The specific goals were (1) to build a machine learning model to perform temperature forecasting; (2) to make suggestion on the operative temperature of AC in library to ensure thermal comfort; (3) to provide common campus FM capabilities by setting up and demonstrating tailor-made user interfaces by using Power BI. |
N.A. |
| 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 |
| HKU | Defining QS-BIM in Hong Kong | Thesis | 04/2019 | LEE Curtise | -- | N.A. |
| HKUST | Developing a BIM- and GIS-based Facility Management Framework for Underground Utilities | Report | 06/2017 | Starry Xing LI Liu YANG |
Nowadays there is a trend of integrating Building Information Modeling (BIM) and Geographic Information System (GIS) to develop the construction projects, including the projects of underground utilities. Compared with BIM and GIS, traditional utility management has plenty of limitations. Traditional utility management keeps 2D CAD drawings, which are separated by utility type and lack of surrounding information. Besides, it is difficult to find the specific utility pipe in 2D drawings under special situation. The working sequence arrangement for those pipes are sometimes not effective. This study aims to improve underground utility management in Hong Kong by using ArcGIS. The improvements consist of 3D visualization, querying and working sequence arrangement. 3D visualization of underground pipes and geological layers is created with reference to relevant Hong Kong standards and researches. Three cases are described to demonstrate the practical application of querying function. Working sequence of project in case 3 is analyzed through Excel. |
N.A. |