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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 | 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. | 連結 |
| HKUST | Development of BIM-assisted Access Point Placement Optimization and Deep Learning based Multi-floor Identification Algorithms for Enhancing Indoor Positioning to Support Construction Applications | Thesis | 08/2019 | Kenneth Chun Ting LI | Over the past decades, indoor positioning has been drawing wide attention in different fields of engineering. Indoor positioning technologies are complementary to the mature outdoor positioning technology such that the indoor positioning technologies can provide a real-time positioning service in any environment where there is a blockage of GNSS signals. In the fields of construction and facility management, indoor positioning technologies enable promising applications that can considerably enhance the productivity, efficiency and safety on construction sites, supporting five major applications, which are (1) construction safety management, (2) construction process monitoring and control, (3) inspection of construction structures and materials, (4) construction automation with robotics, and (5) the use of building information modelling (BIM) technology for construction progress management. Currently, there is no single perfect indoor positioning system that can perform optimally under any circumstances. In addition, due to the large variety of indoor positioning technologies and principles, as well as the complex and dynamic environment on construction sites, developing suitable indoor positioning systems on construction sites is a challenging task. Applying indoor positioning systems is essentially user-oriented and environment-specific. This thesis thus analyses the challenges to apply indoor positioning systems on construction sites, and then proposes six indoor positioning performance metrics, namely APP-CAT, for evaluating suitable on-site indoor positioning systems. Subsequently, the top 10 indoor positioning technologies, which are selected according to their evaluation results using APP-CAT and their popularity amongst the indoor positioning literature studies, are thoroughly discussed and compared. The promising recent trends of developing on-site indoor positioning systems, such as infrastructure-free positioning, collaborative positioning, game theory positioning, and device-free positioning, as well as integration of indoor positioning technologies with BIM models, are also highlighted. In this research work, the comprehensive discussion of current development in indoor positioning from different aspects is intended to help academics, researchers, and industry practitioners develop high-performing and suitable on-site indoor positioning systems for supporting various engineering and construction applications. Among various positioning technologies, Wi-Fi fingerprinting has emerged as a popular technique due to the wide coverage of Wi-Fi signals and its high compatibility with smartphones. Wi-Fi fingerprinting utilizes the patterns of the Wi-Fi signal strengths, which are measured by the Received Signal Strength Index (RSSI), for position estimation. Normally, Wi-Fi access points are placed arbitrarily, which causes a poor positioning accuracy. In fact, positioning accuracy can be considerably enhanced by optimizing the access point (AP) placement strategy. In light of the high popularity of Wi-Fi fingerprinting and the liberty to design AP placemnent strategies on construction sites, this thesis aims to conduct AP placement optimization is by finding the optimal AP placement strategy that maximizes the distinctiveness between individual Wi-Fi fingerprints in a 3D virtual environment. The use of BIM technology provides 3D geometric and semantic information to accurately reproduce the virtual environment for realistic simulation of Wi-Fi signal propagation. Wi-Fi signal propagation is usually modelled by a modified indoor radio wave path loss model, but such models cannot easily consider the multipath effect in an indoor environment. Therefore, in this thesis, an accurate Deep Belief Network (DBN) based path loss model, which considers the multipath effect emulated by the ray-tracing method using particle swarm optimization (PSO), is proposed and implemented to predict the indoor Wi-Fi signal strengths. Based on the results of the simulation, the optimal AP placement strategy as well as the geometrically-constrained optimal AP placement strategy can be obtained by using the genetic algorithm (GA). The test results in a university library have shown that the developed AP placement optimization algorithm could consistently enhance the accuracy of 3D indoor positioning under the circumstances of different numbers of APs and the presence of geometric constraints. Facility management is often performed in a multi-floor indoor environment such as shopping malls and airports. However, one of the major challenges facing the received signal strength indicator (RSSI) based fingerprinting is the inability to perform accurate indoor positioning in a multi-floor environment, despite their popularity. The multi-floor environment poses a large challenge to RSSI fingerprint-based indoor positioning because the uniqueness of RSSI fingerprints is largely lost in a multi-floor environment, especially when ring structure exists in the building. Such a ring structure is commonly found in large airports and shopping malls. In this thesis, in light of the analogy between visual images and a radio map, a novel twofold multi-floor localization algorithm based on convolutional neural network (CNN) is developed to perform robust and accurate multi-floor localization. To support the twofold CNN model and to improve the localization accuracy, the similar selective search algorithm and data augmentation algorithm are proposed. Lastly, with the support of inertial measuring units (IMUs), the snapping algorithm is proposed to convert a random trajectory to a grid shape for the purpose of localization. Per the validation results, the proposed multi-floor localization algorithm is capable of identifying on which floor the user is located such that the “floor jumping” problem is mitigated, and thus the overall indoor positioning accuracy on RSSI fingerprint-based indoor positioning is substantially improved during indoor navigation. To summarise, this thesis provides a comprehsive review of the top 10 indoor positioning technologies for their usage on construction sites, and aims to develop a BIM-assisted access point placement optimization and deep learning based multi-floor identification algorithms for enhancing indoor positioning to support construction applications for both construction management and facility managment. |
N.A. |
| HKUST | Evaluation and Development of Automated Detailing Design Optimization Framework for RC Slabs Using BIM and Metaheuristics | Thesis | 08/2019 | Muhammad AFZAL | Reinforced concrete (RC) structural design optimization has been undertaken for several decades and plays an important role in maximizing the reliability, cost efficiency, and environmental sustainability of RC structures. However, optimization of RC structural design is challenging and requires advanced strategies during different life cycle phases of RC structures. Over the past few decades, substantial fundamental research efforts in RC structural design optimization have been undertaken, but there is a lack of a comprehensive review of these efforts that can provide academic and industry practitioners with sufficient detailed insights. Therefore, this research introduces a critical evaluation of previous research related to the optimization of RC structures for minimizing the amount of construction materials, the material cost, and the environmental effects, with more emphasis on detailing design (such as steel reinforcement), aiming to identify the common research themes and highlight the future directions. Based on the critical evaluation, the portfolio of 348 available research articles presents the identified research gaps and potential future research directions. For example, the adoption of clash-free rebar design optimization, detailing design optimization of complex and irregular RC components, and the concentration of design for manufacture and assembly (DfMA) aspects, are seldom conducted and studied. Moreover, steel reinforcement detailing design of RC structures is one of the common and important tasks in building construction. Currently, despite having introduced advanced computing technologies in the architecture, engineering, and construction (AEC) industry, the rebar detailing design process is still predominantly performed by manual or at least semi-manual approaches, with the aid of computer software packages following the regional design codes. Manual or semi-manual perspectives often result in conservative, uncertain, and sometimes unacceptable outcomes. Additionally, the simple design of RC structural elements can potentially face constructability issues such as congestion, collision, and complexity which may cause complications during the procurement of rebars and other elements all along the construction phase. These issues also hinder concrete pouring and as a result, generate improper compounding of concrete with the rebars which disturb the integrity of the RC structure. All these concerns substantially increase the construction cost, time and quality and thus are uneconomical for AEC industry stakeholders. Although a few previous studies have conducted detailing design optimization of RC structures, very little attention has been given to the above-mentioned issues. Therefore, this research also aims to develop a holistic BIM-based framework utilizing the different meta-heuristic algorithms (such as SGA, SGA-SQP, and PSO-SQP, etc.) for the optimal detailing design of RC solid slabs, considering the minimization of overall construction cost. The main objective function determines the overall minimized construction cost of the RC solid slab, including the cost of steel reinforcement bars in all reinforcing layers, the cost of concrete, and the cost of labor for installing the steel reinforcement bars and pouring the concrete in the RC solid slab. The optimization process is handled in such a way that the first stage optimizes the steel reinforcement present in all four reinforcing layers (two layers each at the bottom and top of solid slab), while the second stage optimizes the solid slab thickness based on the characteristic concrete strength. For the optimum design to be directly constructible without any further alterations, aspects such as available standard rebar diameters, spacing requirements of the rebars, relevant regional design provisions (i.e. British Standards), and the above-mentioned constructability (more specifically clash-avoidance) concerns, are also incorporated into the development of optimization model. In this research, a case study of a typical RC solid slab containing one-way and two-way spanning slab panels is analyzed to investigate the capabilities of the proposed framework. The results demonstrate the potential of the developed model in producing optimum and realistic design solutions. The developed model can be utilized as a design tool to retrieve economical design solutions at the early-stage structural detailing design. |
N.A. |
| HKUST | Application of Mixed Reality Technology for Operations and Maintenance of Building Facilities | Thesis | 08/2019 | Keyu CHEN | The architecture, engineering, construction and operation (AECO) industry has been widely regarded as a highly resource consuming industry. Among different stages of the AECO industry, the operations and maintenance (O&M) lasts the longest in the lifecycle of a building and incurs more than 85% of the total costs, indicating the importance of optimizing management and improving efficiency during O&M. However, it was indicated that two-thirds of the estimated cost of facility management is lost due to inefficiencies during the O&M stage. With current approaches for O&M activities, it is difficult for people to directly visualize and update information of building facilities and many¬ facilities are hidden (e.g. ventilation ducts above ceilings and water pipes under floors). Therefore, this research aims to apply innovations to improve efficiency during the O&M stage. In recent years, professionals begin to realize the practical value of mixed reality (MR) technology, which can aid in various tasks during O&M. Through integrating virtual information with the real world, MR makes the information of users surrounding facilities readable and manipulable. However, there are two major limitations while implementing MR in O&M: (1) All existing methods for MR spatial registration have their own limitations in either accuracy or practicality. (2) There is a lack of efficient methods for data transfer from BIM to MR, which limits the functionality and complexity of MR applications. To tackle these limitations, this research develops an MR engine that can achieve accurate and robust MR spatial registration and efficient data transfer from BIM to MR. For the development of the MR engine, an indoor localization approach is proposed for MR spatial registration. A transfer learning technique named transferable CNN-LSTM is proposed for improving the accuracy of localization and reducing Wi-Fi fingerprinting’s vulnerability to environmental dynamics. A deep learning approach that combines convolutional neural network (CNN) with long short term memory (LSTM) networks is first proposed to predict the locations of unlabeled fingerprints based on labeled fingerprints. Then the transferable CNN-LSTM model is derived from the CNN-LSTM networks based on transfer learning to improve the robustness against time and devices. The proposed transferable CNN-LSTM model is tested and compared with some conventional approaches and even some transfer learning approaches. Another part of the engine focuses on efficient mechanisms for BIM-to-MR data transfer. An ontology-based approach is proposed for transfer of semantic data. For geometric models, building components are classified into four types according to their different features and different model simplification algorithms are proposed accordingly. The algorithms were first tested with single components, and then a whole building was used to evaluate the overall performance of the developed mechanisms. As illustrated in the tests, the developed mechanisms can efficiently transfer both semantic information and geometric information of BIM models into MR applications, thus reducing the time for model transfer and improving the fluency of corresponding MR applications. The developed MR engine is then applied to facility maintenance management (FMM) and emergency evacuation. To improve the efficiency of FMM, a BIM-based location aware MR collaborative framework is developed, with BIM as the data source, MR for interaction between users and facilities, and Wi-Fi fingerprinting for providing real-time location information. An experiment is designed to evaluate the effectiveness of the developed system framework. For emergency evacuation, a graph-based network is formed by integrating medial axis transform (MAT) with visibility graph (VG), with the addition of buffer zones. Closed-circuit television (CCTV) processing techniques are also developed to monitor the flow of people so that evacuees can avoid congested areas. An Internet of things (IoT) sensor network is established as well to detect the presence of hazardous areas. With the constructed graph-based network, congestion analysis and environment index of each area, an optimal evacuation path can be obtained and augmented with MR devices. This research develops an MR engine that can improve the accuracy and robustness of conventional Wi-Fi fingerprinting based MR spatial registration and efficiency of BIM-to-MR data transfer. The developed MR engine has been implemented in FMM and emergency evacuation, illustrating the potential of the proposed approaches in improving the efficiency of O&M activities. |
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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. | 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 |