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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 | A BIM-based web service framework for green building energy simulation and code checking | Journal | 06/2014 | Cheng, J.C.P., and Das, M. | Green building design has been a major trend in the last decade which has largely affected the AEC industry. As of 2013, for example, there were over 13,000 green buildings certified with LEED (Leadership in Energy and Environment Design) in the United States alone. Building Information Modeling (BIM) technolo- gy and computer simulations are adopted largely for green building design. However, while information sharing and automated, collaborative design review are important for the design of green buildings, the current way of BIM-based green building design relies mainly on individual file transfer and does not support collaboration in the distributed environment of construction projects. On the other hand, as the Internet becomes ubiquitous, the web provides convenient and cost-efficient means for multi-location cross-organizational collaboration. Energy analysis and validation against standard building codes are two major processes in green building design evaluation. This paper presents a modular web service based framework which integrates the information necessary for green building design, automates the building design evaluation processes, and facilitates simple updates on the building model on a common but distributed platform. This framework is based on BIM data models like gbXML (Green Building XML) which contain information for green building design like geometry of the building, material, and sensor information from more than one source. The BIM data models act as a single source of building information for all processes. Building design evaluation and updating are iterative in green building design and require information and inputs dispersed among various project participants. Since our framework follows a distributed architecture and is easily accessible from the Internet, it makes the information required to facilitate the iterative process and its results conveniently available to a multi-participant environment. The paper also presents an example scenario demonstrating the developed framework. | 連結 |
| HKUST | Development of Approaches in Embodied Carbon of Buildings: From Construction Materials to Building Structural Design | Thesis | 08/2016 | Jielong GAN | Global warming has been considered as a major environmental challenge nowadays. Among various sources of anthropogenic greenhouse gas (GHG) emissions, the building sector is one of the major contributors to global warming, in which a substantial amount of the GHG emissions are embodied carbon from construction material production and transportation. Embodied carbon can account for 50% of the life cycle GHG emissions in buildings, and this percentage can become more significant for those buildings with shorter service life or higher energy efficiency. Therefore, reducing the embodied carbon in buildings is critically important and can help decrease the life cycle GHG emissions in buildings, thereby pushing human’s living environment towards a sustainable and low carbon future. This thesis uses two approaches to reducing the embodied carbon in buildings. The first approach focuses on the construction material aspect and aims to reduce the embodied carbon from the manufacturing processes and transportations of construction materials. In this thesis, only the cement-based material (i.e., concrete) and quarried material (i.e., aggregate) are studied using the construction materials approach, as they account for more than 60% of the embodied carbon in a reinforced concrete (RC) building. Methods to the reduction of embodied carbon of aggregate and concrete are proposed, considering the feature of each material. Aggregate is very heavy and generates a large amount of emissions during transportation, therefore the aggregate study presents a mathematical model based on life cycle assessment (LCA) and multi-objective optimization (MOO) in order to plan the optimal amount of aggregate from different supply sources. The model can help stakeholders formulate sustainable material supply strategies that minimize the embodied carbon and material cost. For the concrete study, embodied carbon from concrete mix proportions is more important. Thus, a systematic embodied carbon quantification and mitigation framework is proposed for low carbon concrete mix design. The parameters that significantly affect the mix design and embodied carbon of concrete, namely the compressive strength class, the cement type, the supplementary cementitious materials (SCMs) and the maximum aggregate size, are considered. The proposed framework can be used to identify the low carbon mix design for concrete, and the results serves as a basis for reducing the embodied carbon emissions in buildings. Another approach to reducing the embodied carbon in buildings considers different kinds of construction materials together, and focuses on building design aspect in order to minimize the total amounts of construction materials and embodied carbon in buildings. While the previous studies in this particular stream concentrated on low-rise building, they overlooked the analysis on high-rise buildings. However, the structural forms, construction materials and component designs in high-rise buildings are different from those in low-rise buildings, which can cause a large variability in the embodied carbon estimates. Therefore, an embodied carbon accounting methodology based on building information modeling (BIM) for high-rise buildings is proposed in this thesis, and relationships between embodied carbon and the critical parameters in high-rise building design are evaluated through BIM and CFD technologies. A 60-story composite core-outrigger building is designed based on the structure of a typical high-rise building in Hong Kong (i.e., Cheung Kong Center), and then used as a reference for the comparative studies. The results of embodied carbon are expressed in terms of carbon dioxide equivalent (CO2-e). The first comparative study focuses on the material procurement strategies. The embodied carbon in the reference building is evaluated with different assumptions for the material manufacturing processes, the amounts of recycled scrap and cement substitutes, and the transportation distance. It is found that structural steel and rebar from traditional blast furnace account for 76% of the embodied carbon in high-rise buildings. If a contractor chooses to use steel from electric arc furnace (with 100% recycled scrap as the feedstock), the embodied carbon of a high-rise building can be decreased by 60%. As for concrete, 10-20% embodied carbon reduction is achieved by using 35% fly ash (FA) or 75% ground granulated blast-furnace slag (GGBS) in mix design. Comparative studies are also carried out to determine the embodied carbon associated with different construction materials, building heights and structural forms. The 60-story composite core-outrigger reference building has a unitary embodied carbon of 557 kg CO2-e/m2 gross floor area (GFA). If the construction material changes to structural steel, the unitary embodied carbon increases to 759 kg CO2-e/m2 GFA, while the value of embodied carbon decreases to 537 kg CO2-e/m2 GFA if RC is used in construction. Core-frame structures are suitable for buildings of 40 stories or below, with the minimum embodied carbon at 525 kg CO2-e/m2 GFA. The optimal height range for core-outrigger structures is from 50-story to 70-story with 530 kg CO2-e/m2 GFA, whereas tubular structures are in the range between 70-story and 90-story at 540 kg CO2-e/m2 GFA. The results serve as a basis for more environmentally friendly building design, thereby improving our built environment towards a sustainable and low carbon future. |
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| HKUST | Mapping of 3D GIS Digital Building Models in CityGML Across Levels of Details (LoD) | Report | 06/2013 | DU Qianru | GIS, a traditional technology used in many fields in the past hundreds years, now develops to a new height. With the fast development of 3D GIS technology, many new data formats established based on this kind of technology. Being a new format, CityGML is mainly used to represent the city models. It is really convenient due to the fact that different levels of detail exist in this kind of model format. Different LoDs have different attributes and used in diverse situations. Now, the models are often built in different LoDs. Therefore, to achieve one model which is in different LoDs, a translator needs to be published. However, until now neither OGC standard nor previous researchers provide an efficient translator for the transformation between different LoDs. Furthermore, the detailed definition for different LoDs was not provided either. Based on these motivations, this project decided to focus on these two goals. The first part of this project focuses on the differences among different LoDs. Based on the differences, a translator is published and its methodology is also shown in the later part of this report. By using the translator established according to the method in this report, a 3D model sample is provided at the end of the report. This project not only provides a tool to realize the translation between different LoDs, but also offers a convenient method for further research. |
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| 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 | 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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| HKUST | A BIM-based location aware AR collaborative framework for facility maintenance management | Journal | 07/2019 | Chen, K., Chen, W., Li, C.T., and Cheng, J.C.P. | Facility maintenance management (FMM) accounts for a large amount of the total cost of facilities’ lifecycle, illustrating the importance of improving FMM efficiency. Many mechanical facilities, like ventilation ducts above ceilings, are normally hidden, indicating the necessity of applying certain technology that can enable users to visualize and update the information of hidden facilities. Real-time location information is also needed so that users can be aware of their current location and the surrounding facility can be displayed accordingly. Therefore, this paper aims to develop location aware augmented reality (AR) framework for FMM, with building information modeling (BIM) as the data source, AR for the interaction between users and facilities, and Wi-Fi fingerprinting for providing real-time location information. The developed framework has the following features: (1) a proposed softmax-based weighted K nearest neighbour (S-WKNN) algorithm is used for Wi-Fi fingerprinting to obtain the current location of users; (2) a room identification method, based on BIM, the obtained location, and ray casting algorithm, is proposed to identify which room the user is currently in; (3) according to the obtained location and the identified room, users can visualize and interact with their surrounding facilities through the AR devices; and (4) users in a remote location can visualize site situation and interact with site facilities in real time through video streaming and the shared database. At the end of the paper, an experiment was designed to evaluate the effectiveness of the developed system. As shown by the experiment, the developed AR collaborative system can reduce the completion time of the designed task by around 65% compared with traditional 2D drawing-based method, and can provide a localization accuracy of around 1m |
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