Resources
FYPs/Thesis/Journal from Higher Education Institutions in Hong Kong
Institution | Title | Type | Date | Author(s) | Abstract | Link |
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HKUST | A BIM-based system for demolition and renovation waste estimation and planning | Journal | 03/2013 | Cheng, J.C.P., and Ma, L.Y.H. | Due to the rising worldwide awareness of green environment, both government and contractors have to consider effective construction and demolition (C&D) waste management practices. The last two decades have witnessed the growing importance of demolition and renovation (D&R) works and the growing amount of D&R waste disposed to landfills every day, especially in developed cities like Hong Kong. Quantitative waste prediction is crucial for waste management. It can enable contractors to pinpoint critical waste generation processes and to plan waste control strategies. In addition, waste estimation could also facilitate some government waste management policies, such as the waste disposal charging scheme in Hong Kong. Currently, tools that can accurately and conveniently estimate the amount of waste from construction, renovation, and demolition projects are lacking. In the light of this research gap, this paper presents a building information modeling (BIM) based system that we have developed for estimation and planning of D&R waste. BIM allows multi-disciplinary information to be superimposed within one digital building model. Our system can extract material and volume information through the BIM model and integrate the information for detailed waste estimation and planning. Waste recycling and reuse are also considered in our system. Extracted material information can be provided to recyclers before demolition or renovation to make recycling stage more cooperative and more efficient. Pick-up truck requirements and waste disposal charging fee for different waste facilities will also be predicted through our system. The results could provide alerts to contractors ahead of time at project planning stage. This paper also presents an example scenario with a 47-floor residential building in Hong Kong to demonstrate our D&R waste estimation and planning system. As the BIM technology has been increasingly adopted in the architectural, engineering and construction industry and digital building information models will likely to be available for most buildings (including historical buildings) in the future, our system can be used in various demolition and renovation projects and be extended to facilitate project control. |
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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. | Link |
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. | Link |
HKUST | Mapping BIM schema and 3D GIS schema semi-automatically utilizing linguistic and text mining techniques | Journal | 01/2015 | Cheng, J.C.P., Deng, Y.C., and Anumba, C. | The interoperability between BIM (Building Information Modeling) and 3D GIS (Geographic Information System) can enhance the functionality of both domains. BIM can serve as an information source for 3D GIS, while 3D GIS could provide neighboring information for BIM to perform view analysis, sustainable design and simulations. Data mapping is critical for seamless information sharing between BIM and GIS models. However, given the complexity of todayÕs BIM schemas and GIS schemas, the manual mapping between them is always time consuming and error prone. This paper presents a semi-automatic framework that we have developed to facilitate schema mapping between BIM schemas and GIS schemas using linguistic and text-mining techniques. Industry Foundation Classes (IFC) in the BIM domain and City Geography Markup Language (CityGML) in the GIS domain were used in this paper. Entity names and definitions from both schemas were used as the knowledge corpus, and text-mining techniques such as Cosine Similarity, Market Basket Model, Jaccard Coefficient, term frequency and inverse document frequency were applied to generate mapping candidates. Instance-based manual mapping between IFC and CityGML were used to evaluate the results from the linguistic-based mapping. The results show that our proposed name-to-definition comparison could achieve a high precision and recall. Results using different similarity measures were also compared and discussed. The framework proposed in this paper could serve as a semi-automatic way for schema mapping of other schemas and domains. | Link |
HKUST | Analytical review and evaluation of civil information modelling (CIM) | Journal | 04/2016 | Cheng, J.C.P., Lu, Q., and Deng, Y. | Building information modeling (BIM) has been widely adopted in the building industry. However, the use of BIM in civil infrastructure facilities, sometimes referred to as civil information modeling (CIM) has been slow in its application. Industry and academia are increasingly putting effort into CIM study and implementation, but so far there has been no comprehensive review of their effort in this regard. This paper presents a framework to evaluate the current practices of CIM adoption for various civil infrastructure facilities. In this study, civil infrastructure facilities were divided into nine categories for evaluation and the effort with regard to CIM adoption for each civil infrastructure category was evaluated in six aspects. Based on the evaluation and comparison results of 171 case studies and 62 academic papers on CIM, research gaps were identified and recommendations were made. For example, the findings show that data schema development for civil infrastructure facilities other than bridges, roads, and tunnels are lacking. The results and research gaps revealed by this study are useful for both researchers and practitioners. | Link |
HKUST | A semi-automated approach to generate 4D/5D BIM models for evaluating different offshore oil and gas platform decommissioning options | Journal | 07/2017 | Cheng, J.C.P., Tan, Y., Song, Y., Liu, X., and Wang, X. | Background Offshore oil and gas platforms generally have a lifetime of 30 to 40 years, and platform decommissioning is a major issue because many of the existing offshore oil and gas platforms are reaching the end of their service life. There are many possible options for decommissioning offshore oil and gas platforms, and each decommissioning option can be implemented using different methods and technologies. Therefore, it is necessary to have a clear understanding and in-depth evaluation of each decommissioning option before commencing platform decommissioning. 4D and 5D building information modeling (BIM) has been commonly used in the building industry to analyze constructability and to evaluate different construction or demolition plans. However, application of BIM in the oil and gas industry, especially for the platform decommissioning process, is still limited. Methods This paper suggests and demonstrates the application of 4D and 5D BIM technology to simulate various methodologies to realize various selected offshore platform decommissioning options, thereby visualizing and evaluating different options, considering both the time and resources required for decommissioning process. One hundred and seventy-seven offshore platform decommissioning options are summarized in this paper. A new approach to create multiple 4D/5D BIM models in a semi-automated manner for evaluating various scenario options of OOGP decommissioning was proposed to reduce the model creation time as current way of 4D/5D BIM model creation for each OOGP decommissioning option is time consuming. Results In the proposed approach, an OOGP BIM model relationship database that contains possible 4D/5D BIM model relationships (i.e. schedules for different decommissioning methods) for different parts of an OOGP was generated. Different OOGP decommissioning options can be simulated and visualized with 4D/5D BIM models created by automatically matching schedules, resources, cost information and 3D BIM models. This paper also presents an illustrative example of the proposed approach, which simulates and evaluates two decommissioning options of a fixed jacket platform, namely Rig-to-Reef and Removal-to-Shore. As compared to the traditional approach of 4D/5D BIM model generation, the proposed semi-automated approach reduces the model generation time by 58.8% in the illustrative example. Conclusions The proposed approach of semi-automated 4D/5D BIM model creation can help understand the implication of different decommissioning options as well as applied methods, detecting potential lifting clashes, and reducing 4D/5D BIM model creation time, leading to better planning and execution for the decommissioning of offshore oil and gas platforms. In addition, with the proposed semi-automated approach, the 4D/5D BIM model can be generated in a more efficient manner. |
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