FYPs/Thesis/Journal from Higher Education Institutions in Hong Kong


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Institution Title Type Date Author(s) Abstract Link
HKUST A BIM-based framework for lift planning in topsides disassembly of offshore oil and gas platforms Journal 03/2017 Tan, Y., Song, Y., Liu, X., Wang, X., and Cheng, J.C.P. Offshore oil and gas platforms (OOGPs) usually have a lifetime of 30–40 years. An increasing number of OOGPs across the world will be retired and decommissioned in the coming decade. Therefore, a safe and efficient approach in planning the disassembly of the topsides of OOGPs is required. One commonly applied disassembly method is reverse installation, which moves the OOGP modules from the platform deck to a heavy lift vessel (HLV) in reverse order of their installation. Considering the high risk and cost of working offshore, shortening the lift time is crucial. In contrast to the traditional experience-driven lift operations, this paper describes minimizing the lift path for each OOGP module during disassembly, leveraging building information modeling (BIM) technology and an improved A* algorithm. BIM models provide accurate component-based geometric and semantic information that can be used for planning and optimization. However, there has been no previous study on the use of BIM for offshore disassembly. Industry Foundation Classes (IFC), which is a neutral data model of BIM, is used in this study to represent OOGP models. In particular, the IfcBuildingElementProxy entity is used to represent the OOGP components, and the information in IfcBuildingElementProxy is automatically extracted to obtain the location and dimension information of each OOGP module. Then, for a given layout of modules on the removal vessel, the lift path and removal sequence of different modules, with the shortest lift path distance, are obtained. The lift path distance is calculated using the A* algorithm, which has been widely applied in 2D environments and is modified in this study to suit the 3D environment. Finally, the genetic algorithm (GA) technique is applied to optimize the layout plan on the removal vessel by minimizing the total lift path distance. The developed BIM-based framework is illustrated and evaluated through an illustrative example. The results show that the proposed framework can generate and visualize the shortest lift path for each OOGP module directly and automatically, and significantly improve the efficiency of OOGP disassembly. 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.
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.
HKUST BIM-based Automatic Piping Layout Design and Schedule Optimization Thesis 08/2020 Jyoti SINGH Piping system is one crucial component in civil infrastructure that is designed to collect and transport fluid from the various sources to the point of distribution. The design, manufacture, coordination, scheduling, and installation of pipe systems is an important and necessary task and is one of the most time-consuming and complicated jobs in any piping project. Therefore, it is important and necessary to perform pipe systems design and scheduling efficiently. Better understanding of the complex design logic and installation options of a pipe system can enhance the reliability of designing and scheduling, which is crucial to achieve smooth and steady design and schedule flow. An efficient designing and scheduling of piping systems become more and more challenging due to various constraints such as physical, design, economical, and installation constraint. Current practice in the architecture, engineering and construction (AEC) industry involves pipe system design and installation as per enforced design codes, either by manual calculations, or by partial automation using computer-aided design software. Manual calculations are based on the experience of consultants and design codes, which is labor intensive, time consuming, and unadaptable to changes, and often leads to mistakes due to tedious nature of pipe design and coordination problems and the numerous calculations and decision-making involved. Therefore, complete automation with design and schedule optimization are required to economically plan pipe system design layout and generation of installation schedule.

Nowadays, Building Information Modelling (BIM) has been increasingly applied for architectural and structural design in civil engineering, especially in the building sector, since BIM have advantages for digital representation and information management. BIM technology is used to capture the 3D geometric and semantic information of the ceiling space, building components and pipe system information and parameters. BIM technology is used to capture the valuable information from 3D models to assist time based 4D modeling. However, existing research of BIM application for piping system design in building sector is lacking. To tackle the limitation of existing research, this thesis aims to develop an automated BIM-based approach for pipe systems design and schedule optimization.

For the design of pipe system layout, various factors such as building space geometry, system requirements, design code specifications, and locations and configurations of relevant equipments are considered. A framework based on building information modeling (BIM) for automatic pipe system design optimization in 3D environment. Heuristic algorithms are modified and used in a directed weighted graph to obtain the optimal feasible route for pipe system layout. Clashes among pipes and with building components are considered and subsequently avoided in the design optimization. The developed framework considers one-to-one, one-to-many, many-to-one connections of the pipe network routing. Comparison between heuristic routing algorithms is also presented in this research.

For installation schedule generation, this research proposes a new approach to automate pipe installation coordination and schedule optimization using 4D BIM. Category-based matching rules are used to automate the pairing and integration between 3D BIM models and installation activities. Constraint based analysis by sequence rule is developed to generate favorable sequence and coordination between pipe systems. Heuristic algorithm is adopted to optimize the generated practical schedules based on formulated objective function. All developed BIM-based framework and approaches are illustrated with related examples. Compared to current practices, these proposed approaches significantly reduce the time and cost for pipe system design layout and generating installation schedule.

This research has three parts. The first part is background study and literature review on pipe systems design and scheduling. The second part applies BIM-based framework to design piping system, including the following three studies: (1) an automated single pipe system design using modular approach, (2) multiple pipe system layout design optimization, and (3) comparison of developed approach with other optimization methods. The third part applies BIM-based framework for piping coordination and scheduling optimization
HKUST Automatic as-built BIM creation of precast concrete bridge deck panels using laser scan data Journal 02/2018 Wang, Q., Sohn, H., and Cheng, J.C.P. Precast concrete bridge deck panels are commonly used for bridge constructions because they enable faster construction and have less impact on traffic flow. The quality of connections between adjacent precast elements must be ensured to guarantee the overall structural integrity of precast systems. Therefore, the dimensional quality of precast concrete panels should be inspected before they are shipped to construction sites for installation. However, current quality inspection of precast concrete elements primarily relies on manual inspection. Furthermore, the as-built dimensions of precast elements are usually stored in paper sheets or Microsoft Excel spreadsheets, making it difficult to visualize and manage the as-built dimensions. This study develops a technique to automatically estimate the dimensions of precast concrete bridge deck panels and create as-built building information modeling (BIM) models to store the real dimensions of the panels. First, the proposed technique conducts scan planning to find the optimal scanner locations for scan data acquisition. Then, the scan data of the target panel are acquired and preprocessed to remove noise data and to register multiple scans in a global coordinate system. From the registered scan data, the as-built geometries of the target panel are estimated. In the last step, an as-built BIM model is created on the basis of the previously estimated geometries. The proposed technique is validated on a laboratory-scale specimen and a full-scale precast concrete bridge deck panel. The experimental results show that the proposed technique can accurately and efficiently estimate the dimensions of full-scale precast concrete bridge deck panels with an accuracy of 3 mm and automatically create as-built BIM models of the panels. Link
HKUST Automated Quality Assessment of Precast Concrete Elements Using 3D Laser Scan Data Thesis 08/2017 Qian WANG Precast concrete elements are popularly adopted in buildings and civil infrastructures like bridges because they provide well-controlled quality, reduced construction time, and less environmental impact. To ensure the performance of complete precast concrete structures, individual precast concrete elements must be cast according to the as-designed blueprints. Any inconsistency between the as-built and as-designed dimensions can result in assembly difficulty or structure failure, causing delay and additional cost. Therefore, it is essential to conduct geometry quality assessment for precast concrete elements before they are shipped to the construction sites. Currently, the quality assessment of precast concrete elements is still relying on manual inspection, which is time-consuming and labor-intensive. Besides, due to tedious work, manual inspection is also error-prone and unreliable. Thus, automated, efficient, and accurate approaches for geometry quality assessment of precast concrete elements are desired. Nowadays, 3D laser scanning has been widely applied to the quality assessment of buildings and civil infrastructures because it can acquire 3D range measurement data at a high speed and high accuracy. However, existing research of laser scanning based quality assessment is mainly focused on simple-geometry elements, such as straight columns and rectangular concrete surfaces. There has been limited research on the quality assessment of precast concrete elements with complex shapes. To tackle the limitations of existing research, this research aims to develop automated, efficient, and accurate techniques for the geometry quality assessment of precast concrete elements using 3D laser scan data. The geometry quality assessment includes dimensional quality assessment, surface flatness and distortion assessment, and rebar position assessment.

For dimensional quality assessment, a dimensional quality assessment technique focusing on the side surfaces of precast concrete panels is developed. This technique aligns the laser scan data with the as-designed building information model (BIM), and extracts the as-built dimensions of the elements. Furthermore, an improved dimensional quality assessment and as-built BIM creation technique is developed to inspect the entire precast concrete element, rather than a surface only, and to automatically create a BIM model for storing the as-built dimensions for better visualization and management. As a supporting study, a novel mixed pixel filter is developed to remove noise data namely mixed pixels from raw laser scan data and to improve the dimension estimation accuracy. The proposed mixed pixel filter formulates the locations of mixed pixels, based on which the optimal threshold value is obtained to classify scan data into mixed pixels and valid points. Another supporting study is to investigate the influence factors for edge line estimation accuracy. Four influence factors are identified and the effect of each factor is analyzed based on numerical simulations. Implications are eventually suggested based on the analysis.

For surface flatness and distortion assessment, the developed technique identifies a few measures for both surface flatness and distortion. These measures are then automatically calculated from the laser scan data of the precast concrete surface for surface quality assessment. Furthermore, an automated rebar position estimation technique is developed to estimate the rebar positions for rebar positioning quality assessment. The technique can recognize individual rebars from the laser scan data of reinforced precast concrete elements and accurately estimate the rebar positions.

This research provides automated approaches for the quality assessment of precast concrete elements, which are able to greatly save the labor cost and time for quality assessment. In addition, the quality of precast concrete structures can be improved due to the faster and more economical quality assessment, thereby further promoting the adoption of precast concrete elements in the construction industry.