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FYPs/Thesis/Journal from Higher Education Institutions in Hong Kong
| Institution | Title | Type | Date | Author(s) | Abstract | Link |
|---|---|---|---|---|---|---|
| HKUST | Developing an evacuation evaluation model for offshore oil and gas platforms using BIM and agent-based model | Journal | 02/2018 | Cheng, J.C.P., Tan, Y., Song, Y., Mei, Z., Gan, V.J.L., and Wang, X. | Accidents on offshore oil and gas platforms (OOGPs) usually cause serious fatalities and financial losses considering the demanding environment where such platforms are located and the complicated topsides structure that the platforms have. Conducting evacuation planning on OOGPs is challenging. Computational tools are considered as a good way to plan evacuation by emergency simulation. However, the complex structure of OOGPs and various evacuation behaviors can weaken the advantages of computational simulation. Therefore, this study develops a simulation model for OOGPs to evaluate different evacuation plans to improve evacuation performance by integrating building information modeling (BIM) technology and agent-based model (ABM). The developed model consists of four parts: evacuation model input, simulation environment modeling, agent definition, and simulation and comparison. Necessary platform information is extracted from BIM and then used to model the simulation environment by integrating matrix model and network model. In addition to essential attributes, environment sensing and dynamic escape path planning functions are developed and assigned to agents in order to improve simulation performance. Total evacuation time for all agents on an offshore platform is used to evaluate the evacuation performance of each simulation. An example OOGP BIM topsides with different emergency scenarios is used to illustrate the developed evacuation evaluation model. The results show that the developed model can accurately simulate evacuation and improve evacuation performance on OOGPs. The developed model is also applicable to other industries such as the architecture, engineering, and construction industry, where there is an increasing demand for evacuation planning and simulation. | Link |
| HKUST | Optimizing lift operations and vessel transport schedules for disassembly of multiple offshore platforms using BIM and GIS | Journal | 06/2018 | Tan, Y., Song, Y., Zhu, J., Long, Q., Wang, X., and Cheng, J.C.P. | As the coming decades will witness a big trend in the decommissioning of offshore platforms, simultaneously disassembling topsides of multiple offshore platforms is getting increasingly common. Considering high risk and cost of offshore operations, module lift planning among multiple offshore platforms with transport vessels is required to be carefully conducted. The lift planning usually contains two main parts: module layout on vessels planning and vessel transport schedules arrangement. In contrast to the current experience-driven module lift planning, this paper formulates the lift planning optimization problem and develops a web system integrating building information modeling (BIM) and geographical information system (GIS) to efficiently disassemble topsides for multiple offshore platforms. BIM provides detailed information required for planning module layout on vessels and GIS contains the management and analysis of geospatial information for the vessel transport schedule arrangement. As for module layout optimization, three heuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FA) are implemented and compared to obtain the module layout with the minimum total lift time. While for vessel transport schedule, graph search technique is integrated with a developed schedule clash detection function to obtain the transport schedule with the minimum sailing time. The proposed optimization algorithms and techniques are integrated into a developed BIM/GIS-based web system. An example of three offshore platforms with eighteen modules in total is used to illustrate the developed system. Results show that the developed system can significantly improve the efficiency of lift planning in multiple topsides disassembly. The developed BIM/GIS-based web system is also effective and practical in the resource allocation and task assignment among multiple locations, such as construction sites, buildings, and even cities. | Link |
| HKUST | BIM-supported 4D acoustics simulation approach to mitigating noise impact on maintenance workers on offshore oil and gas platforms | Journal | 12/2018 | Tan, Y., Fang, Y., Zhou, T., Gan, V.J.L., and Cheng, J.C.P. | Maintenance workers on offshore platforms are usually exposed to a high level of noise from the working environment as most of the daily operations of oil and gas process machines generate noise over 85 dBA, causing substantial health and safety issues. Avoiding exposure of workers to the modules that generate high sound power during maintenance activities can significantly mitigate the noise impact on human health and safety. Noise simulation and noise mapping methodologies can be used to evaluate and quantify the noise impact on offshore platforms. However, limited digital information of offshore platforms makes noise simulation setup challenging as modules on topsides have a high level of details. In addition, current noise mapping studies are usually conducted in a 3D static manner, which only reflects noise impact at a certain time. Building information modeling (BIM) provides detailed physical and functional characteristics of a facility that can be applied to support the noise simulation on offshore platforms. In this study, attempts have been made to develop a BIM-supported 4D acoustics simulation approach to mitigating the noise impact on maintenance workers of offshore platforms. BIM is utilized to automatically provide required information to facilitate noise simulation setup. 4D acoustics simulation approach is used to obtain the spatio-temporary sound pressure level (SPL) distribution of the noise generated by the functional modules on offshore platforms. Acoustic diffusion equation (ADE) is selected as noise SPL prediction model. To evaluate noise impact on maintenance workers, an equation based on daily noise dose is then newly derived to quantify the noise impact. Optimization algorithm is used to determine the maintenance schedule with the minimum daily noise dose. Finally, optimized maintenance schedule that has considered noise impact is used to update the daily maintenance plan on offshore platforms. An example of a fixed offshore platform with maintenance daily activity information is used to illustrate the proposed BIM-supported 4D acoustics simulation approach. The results show that the developed approach can well mitigate noise impact on maintenance workers on offshore platforms, resulting in health and safety management improvement. | 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 | 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. | Link |
| 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. | Link |