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FYPs/Thesis/Journal from Higher Education Institutions in Hong Kong
| Institution | Title | Type | Date | Author(s) | Abstract | Link |
|---|---|---|---|---|---|---|
| HKUST | Application of Building Information Modeling Technology for Safe Operations and Decommissioning of Offshore Oil and Gas Platforms | Thesis | 08/2018 | Yi TAN | Offshore oil and gas platforms (OOGPs) usually have a lifetime of 30-40 years. The operation and maintenance stage takes up the most percentage of the whole lifetime of OOGPs. During the operations and maintenance, there are several safety issues. Emergent accidents and exposure to high level of noise are two main issues. Traditional emergency responses include 2D escape plan guidance and real drill exercises. 2D escape plan usually causes different understanding, while real drill exercises require extra time and workforce. As for current noise controls, only personal protective equipment has been commonly employed, which is the least effective noise control. In addition, as increasing number of OOGPs will be retired and decommissioned in the coming decade, disassembling offshore platforms is an unavoidable activity. During OOGP decommissioning stage, there are also several safety issues such as potential clashes when conducting heavy lift operations and lift vessel capsize. Besides, when multiple lift vessels are working together to disassemble multiple offshore platforms, more than one vessel working at the same platform, which can significantly increase lift clashes, is another safety issues. Current approaches to addressing these safety issues at the decommissioning stage are usually based on experience, and manually planned. Considering all these safety issues mentioned above, automated, efficient, and accurate approaches to improving safety management of OOGPs at both operation and decommissioning stages are desired. However, limited researches have been conducted to tackle these safety issues. Therefore, this research aims to develop automated, efficient, and accurate techniques and approaches for safer operations and decommissioning of OOGPs. Building information modeling (BIM) technology is widely used in the building and infrastructure industries for the past decade considering the rich geometric and semantic information BIM contains. Therefore, this research applies BIM technology to efficiently provide required information of OOGPs when developing new approaches to addressing safety issues. For the operation and maintenance stage of an offshore platform, to better respond to emergent accidents, a BIM-based evacuation evaluation model is developed to efficiently simulate and evaluate different emergency scenarios, and improve evacuation performance on offshore platforms. As for the noise control, this research proposes a BIM-supported 4D acoustics simulation approach. The proposed approach can automatically conduct noise simulation for offshore platforms using the information extracted from BIM models. Maintenance schedules can then be optimized based on simulated results. By minimizing the time of exposing to a high level of noise, the noise impact on maintenance workers is well mitigated. For the decommissioning stage, first, a semi-automated approach to generate 4D/5D BIM models to evaluate different OOGP decommissioning option is developed. Second, automated topsides disassembly planning approach based on BIM is developed. Clash-free lift paths can be generated to avoid clashes during heavy lifts. Module layouts on vessels are optimized to minimize the total heavy lift time and to guarantee the stability of lift vessels. Besides, a schedule clash detection method is also developed to make sure that no more than one vessel is working at one offshore platform simultaneously. All developed BIM-based approaches are illustrated with related examples. Compared to current practices, these proposed approaches improve the safety management performance of offshore platforms. |
N.A. |
| 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 | Integrating 4D BIM and GIS for construction supply chain management | Journal | 02/2019 | Deng, Y., Gan, V.J.L., Das, M., Cheng, J.C.P., and Anumba, C.J. | Construction supply chain management (CSCM) requires the tracking of material logistics and construction activities, an integrated platform, and certain coordination mechanisms among CSCM participants. Researchers have suggested the use of building information modeling (BIM) technology to monitor construction activities and manage construction supply chains. However, because material warehousing and deliveries are mostly performed outside construction project sites, project information from a single BIM model is insufficient in meeting the needs of construction supply chain management. In this research, an integrated framework was developed based on four-dimensional (4D) BIM and a geographical information system (GIS) for coordination of construction supply chains between the construction project sites and other project related locations, such as supplier sites and material consolidation centers. The proposed integration was used to solve three common tasks in CSCM, namely (1) supplier selection, (2) determination of number of material deliveries, and (3) allocation of consolidation centers, using information from 4D BIM and GIS. The proposed 4D BIM-GIS framework was demonstrated via case studies. The results of the case studies indicated that determinations of supplier and number of deliveries need to take into account both the transportation distance and material unit price. Mathematical solutions were also generated to support decision making for the allocation of consolidation centers in congested regions with long transportation distances. The outcomes of this paper serve as a decision support base for a more efficient CSCM in the future. | Link |
| HKUST | Natural-language-based intelligent retrieval engine for BIM object database | Journal | 03/2019 | Wu, S., Shen, Q., Deng, Y., and Cheng, J.C.P. | Rapid growth of building components in the BIM object database increases the difficulty of the efficient query of components that users require. Retrieval technology such as Autodesk Seek in America and BIMobject in Europe, which are widely used in BIM databases, are unable to understand what the search field truly means, causing a lack of completion and a low accuracy rate for results incapable of meeting the demands of users. To tackle such a problem, this paper puts forward a natural-language-based intelligent retrieval engine for the BIM object database and Revit modeling. First, a domain ontology is constructed for semantic understanding, and the BIM object database framework is established for testing our search engine. Second, “target keyword” and “restriction sequence” proposed are extracted from the natural sentences of users. Then, a final query is formed, combining concepts of “keyword” and “restriction sequence”, and its concepts are expanded through the semantic relationship in ontology. Finally, the results are presented after mapping from the final query to the BIM object database and ranking of results. Compared with traditional keyword-based methods, the experimental results demonstrate that our method outperforms the traditional methods. | Link |
| HKUST | Integration of Building Information Modeling and Internet of Things for Facility Maintenance Management | Thesis | 03/2019 | Weiwei CHEN | Facility management (FM) accounts for more than two thirds of the total cost of the whole life cycle of a building. FM staff do have inadequate visualization and often have difficulty in querying information using 2D drawings and traditional facility management systems. Currently, building information modeling (BIM) is increasingly applied to FM in the operations and maintenance (O&M) stage. BIM represents the geometric and semantic information of building facilities in 3D object-based digital models and enables facility managers to manage building facilities better in the O&M stage. At the same time, the Internet of Things (IoT) technology can be used to acquire operational data of building facilities and real-time environmental data to support FM. However, few studies have used BIM and IoT technologies together for automated management and maintenance of building facilities. Around 65%~80% of the FM comes from facility maintenance management (FMM). However, there is a lack of efficient maintenance strategies and appropriate decision making approaches that can reduce FMM costs. Facility managers usually undertake reactive maintenance or preventive maintenance strategies in the O&M stage. However, reactive maintenance cannot prevent failures and preventive maintenance cannot predict the future condition of building components, which leads to maintenance actions being performed after failure has occurred and it cannot keep the functionality of a building consistent. This study aims to apply a predictive maintenance strategy with BIM and IoT technologies to overcome these limitations. In addition, there is an information interoperability problem among BIM, IoT and the FM system. Therefore, this study aims to leverage the BIM and IoT technologies to improve the efficiency of FMM and to address the information interoperability problem of integrating BIM, IoT and the FM system. In order to improve the efficiency of FMM, an FMM framework is proposed based on BIM and facility management systems (FMSs), which can provide automatic scheduling of maintenance work orders (MWOs) to enhance good decision making in FMM. In this framework, data are mapped between BIM and FMSs according to the developed Industry Foundation Classes (IFC) extension of maintenance tasks and MWO information in order to achieve data integration. Geometric and semantic information of the failure components is extracted from the BIM models in order to calculate the optimal maintenance path in the BIM environment. Moreover, the MWO schedule is automatically generated using a modified Dijkstra algorithm that considers four factors, namely, problem type, emergency level, distance among components, and location. In order to provide a better maintenance strategy for building facilities, a data-driven predictive maintenance framework based on BIM and IoT technologies for FMM has been developed. The framework consists of an information layer and an application layer. Data collection and data integration among the BIM models, FM system, and IoT system are undertaken in the information layer, while the application layer contains four modules to achieve predictive maintenance, namely: (1) condition monitoring and sensor data acquisition, (2) condition assessment module, (3) condition prediction module, and (4) maintenance planning module. In addition, machine learning algorithms, i.e. artificial neural network (ANN) and support vector machine (SVM), are used to predict the future condition of building components. For the information interoperability problem among BIM, IoT and FM system, an ontology-based methodology framework is proposed for data integration among the BIM, IoT and FM domains. The ontology-based approach is developed as a tool to facilitate knowledge management in BIM- and IoT-based FMM and improve the data integration process. First, three ontologies are developed for BIM, IoT, and FMM respectively according to the ontology development process and facility information requirement. Second, an ontology mapping method is designed to integrate the three developed ontologies based on mapping rules. Moreover, ontology reasoning rules are developed based on description logics to infer implicit facts from the integrated ontology and support quick information querying on FMM. The developed framework is validated through an illustrative example. This research provides an automatic work order scheduling approach in FMM and predictive maintenance strategy for building facilities, thereby enabling great saving in time and labor costs for facility staff. In addition, the proposed ontology-based methodology can address the information interoperability problem and integrate data from BIM, IoT and FM system for facility maintenance activities. In the future, the ontology-based methodology will be applied for the operation management of building facilities. |
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| HKU | Defining QS-BIM in Hong Kong | Thesis | 04/2019 | LEE Curtise | -- | N.A. |