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院校 題目 類型 日期 作者 摘要 網頁
HKU Application of information technology in materials logistics in the Hong Kong construction industry Thesis 04/2000 YAN Kwok Wing -- N.A.
HKUST A state-of-the-art review on mixed reality (MR) applications in the AECO industry Journal 11/2019 Cheng, J.C.P., Chen, K., and Chen, W. The ability to combine digital information with the real world enables mixed reality (MR) technology to provide a better display of information, resulting in its increasing popularity in various fields. The architecture, engineering, construction, and operation (AECO) industry is no exception. However, existing reviews on the use of MR technology can hardly keep up with the rapid development of MR applications. Therefore, a state-of-the-art review focusing on MR technology applications in the AECO industry is needed to reflect the current status of MR implementation in the AECO industry. This review is based on articles retrieved from well-acknowledged academic journals within the domain of the AECO industry. In this paper, 87 journal papers on MR applications are identified and classified into four categories: (1) applications in architecture and engineering, (2) applications in construction, (3) applications in operation, and (4) applications in multiple stages. Five basic components of MR, including spatial registration, display, user interaction, data storage, and multiuser collaboration, in each of the aforementioned 87 journal papers are identified and discussed. After reviewing the selected applications and corresponding MR components, this paper summarizes the challenges of MR development and provides insights into future trends of the MR technology in four aspects, namely: (1) accuracy of spatial registration, (2) user interface (UI), (3) data storage and transfer, and (4) multiuser collaboration. 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 Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data Journal 01/2020 Yang, L., Cheng, J.C.P., and Wang, Q. As-built building information models (BIMs) are increasingly needed for construction project handover and facility management. To create as-built BIMs, laser scanning technology has gained popularity in the recent decades due to its high measurement accuracy and high measurement speed. However, most existing methods for creating as-built BIMs from laser scanning data involve plenty of manual work, thus becoming labor intensive and time consuming. To address the problems, this study presents a semi-automated approach that can obtain required parameters to create as-built BIMs for steel structures with complex connections from terrestrial laser scanning data. An algorithm based on principal component analysis (PCA) and cross-section fitting techniques is developed to retrieve the position and direction of each circular structural component from scanning data. An image-assisted edge point extraction algorithm is developed to effectively extract the boundaries of planar structural components. Normal-based region growing algorithm and random sample consensus (RANSAC) algorithm are adopted to model the connections between structural components. The proposed approach was validated on a bridge-like steel structure with four different types of structural components. The extracted as-built geometry was compared with the as-designed geometry to validate the accuracy of the proposed approach. The results showed that the proposed approach could efficiently and accurately extract the geometry information and generate parametric BIMs of steel structures. 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
HKUST Transfer learning enhanced AR spatial registration for facility maintenance management Journal 02/2020 Chen, K., Yang, J., Cheng, J.C.P., Chen, W., and Li, C.T. Augmented reality (AR), which requires a spatial registration technique, has proved to greatly improve the efficiency of facility maintenance management (FMM) activities. Being one of the most promising techniques for indoor localization, Wi-Fi fingerprinting has been widely used for AR spatial registration. However, localization accuracy of Wi-Fi fingerprinting decreases over time due to dynamics of environmental factors. Readings from different mobile devices can also affect the accuracy negatively. In this paper, a transfer learning technique named transferable CNN-LSTM is proposed for improving the robustness of Wi-Fi fingerprinting while implementing AR in FMM activities. Convolutional neural network (CNN), embedded with long short term memory (LSTM) networks, is utilized to predict the location of unlabeled fingerprints. Multiple kernel variant of maximum mean discrepancy (MK-MMD) is adopted to reduce the distribution difference between the source domain and the target domain, so that the location of the newly collected unlabeled fingerprints can be predicted accurately. As shown in the experimental validation, the transferable CNN-LSTM can achieve an accuracy of 97.1% in short-term (without significant environmental changes) spatial registration, 87.8% in long-term (with significant environmental changes) spatial registration, and around 90% in multi-device spatial registration, indicating a higher accuracy and better robustness over other conventional approaches. 連結