| HKUST |
Study on Legal Aspects of BIM Projects |
Report |
06/2020 |
Ka Cheong TANG
Tsz Yin CHOW |
Building Information Modeling (BIM) is an emerging technology applied in Architecture, Engineering and Construction (AEC) industry. With the increase in the BIM application, some legal uncertainties have appeared and led to a high risk in legal aspects when adopting BIM in design and construction projects. It is vital that BIM users should be aware of the potential legal issues and develop suitable legal documents and contracts to prevent these issues from occurring. Within this context, a critical review on different cases associated with BIM is carried out in order to provide an overview of potential legal issues. Model copyright, right of BIM common data environment control and responsible control were discussed. Furthermore, three protocols and guidelines commissioned by the United Kingdom, the United States and Singapore are compared and analyzed. BIM Protocol published by Construction Industry Council of the United Kingdom is suggested as the most comprehensive and structured protocol in the analysis. Recommendations on the aspects of BIM cyber security, practical completion and contractors’ perspective are made to Hong Kong AEC professional institutes to commission a suitable and comprehensive protocol for the local industry. |
N.A. |
| HKUST |
Study on BIM Project Execution Plan and BIM Uses in Comparison with PMBOK |
Report |
06/2020 |
Ka Wing Ngan
HUANG Li |
Project successful strongly relies on PMBOK. Besides that, BIM is important because it is a powerful tool in delivery of BIM-based project. To implement BIM, BIM uses are defined based on project goals. To effective implement BIM as planned, BIM project execution plan (PXP) is necessary to control BIM. In the first section, this paper compares supporting infrastructure from BIM project execution plan (PXP) to PMBOK to find out the relationship. The categories of supporting infrastructure are BIM PXP overview, project information, key project contacts, project goals / BIM uses, organizational roles / staffing, BIM process design, BIM information exchanges, BIM and facility data requirement, collaboration procedures, quality control, technological infrastructure needs, model structure, project deliverables and delivery strategy / contract whereas PMBOK are integration, scope, time, cost, quality, human resources, communication, risk, procurement and stakeholder management. From the investigation, it is found that risk and cost management is not obviously applied from the categories of supporting infrastructure. In the second section, this paper investigate the relationship of various BIM uses in terms of PMBOK. The considerable BIM uses are design authoring, design review, 3D coordination, cost estimation, phase planning (4D Modelling), digital fabrication and site utilization planning. It is also found that scope, communication and human resources management is not obviously applied from the selected BIM uses. In the third section, we recommend that for BIM PXP additional section including project cost management and BIM risk management should be included; and for BIM uses attention should be paid in drafting BIM PXP to support BIM uses and other BIM uses maybe considered. Manager may benefit from the relationship developed and recommendation in BIM implementation. |
N.A. |
| HKU |
The Determinants of Information Technology Applications in Retail under the Context of Shopping Malls in Hong Kong |
Thesis |
04/2018 |
KAM Oi Man |
-- |
N.A. |
| 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 |
Analysis and Evaluation of Green Building Features Using Building Information Modeling |
FYP |
06/2016 |
KEUNG, Wun Ting Iris
WONG, Wing Man |
There is a global trend of green buildings in recent years. As of 2011, there are over 10,000 green building projects certified by the LEED (Leadership in Energy and Environmental Design) standard in the United States alone. In Hong Kong, the BEAM Plus green building standard developed by the Hong Kong Green Building Council (HKGBC) in 2009 has certified over 200 projects in Hong Kong. Green buildings utilize various design features and operation technologies to reduce energy and water consumption, improve indoor environmental quality and increase building performance. This project aims to study the common green building features and evaluate them using building information modeling (BIM) and computer simulation techniques. In a BIM model, each building component has its properties, information and semantics, which support sophisticated simulation and analysis under different conditions. In this project, commonly adopted energy saving and indoor environmental quality improvement green building features will be modeled, evaluated, and compared. |
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. |
N.A. |
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