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


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Date: From


Institution Title Type Date Author(s) Abstract Link
HKU Application of information technology in materials logistics in the Hong Kong construction industry Thesis 04/2000 YAN Kwok Wing -- 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.
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.
HKUST BIM-based Automatic Generation of Fabrication Drawings for Building Facades Thesis 08/2018 Min DENG Many modern commercial buildings involve complex shaped façades, resulting in increasing complexity as well as challenges in façade fabrication and assembly processes. Currently, fabrication drawings are essential for fabrication, design evaluation and inspection of building components. Computer-aided automation, which can significantly improve the efficiency and accuracy of the fabrication and assembly process, is thus essential for the generation of façade fabrication drawings, thereby supporting the fabrication and assembly of the building façade components. Among current computer-aided technologies, building information modeling (BIM) has been widely applied to many sophisticated building projects due to its comprehensive ability in digital representation of building models. BIM has demonstrated its advantages over generating different types of drawings. However, generating fabrication drawings for façade panels using conventional approaches is time-consuming and error prone, especially when the number of façade components become huge. Therefore, this thesis aims to develop BIM-based methodologies to automate the generation of fabrication drawings for façade components, thereby facilitating the whole construction process.

For façade panels, a BIM-based framework is proposed for the automatic generation of fabrication drawings for façade panels. The framework integrates both graphical and non-graphical information from BIM models and other external data sources. Specific algorithms are applied to automatically generate the graphical information on the drawing templates based on the BIM geometric models. Title blocks of the drawing templates are also automatically filled in with corresponding non-graphical information. Complete fabrication drawings as well as a tabulated file with essential graphical information on similar components are then generated automatically.

For structural components such as mullions and transoms, it is important to represent their physical characteristics clearly, thus a large number of section views need to be produced, which is a time-consuming process and very labor intensive. Therefore, automatic generation of fabrication drawings for building façade components (such as mullions and transoms) is of paramount importance. In this thesis, attempts have been made to develop an efficient framework in order to automatically generate fabrication drawings for building façade structural components, including mullions and transoms. To represent the complex physical characteristics (such as holes and notches) on mullions and transoms using minimum number of drawing views, a computational algorithm based on graph theory is developed to eliminate duplicated section views. Another methodology regarding the generation of breaks for front views is also proposed to further improve the quality of drawing layouts. The obtained drawing views are then automatically arranged using a developed approach. In addition, primary dimensions of the drawing views focusing on the physical features are also generated. Furthermore, in order to maintain the consistency of the drawing formats, a methodology is proposed to simulate the scales of the drawings by using clustering technique.

With the adoption of the proposed BIM-based methodologies, time and human effort in the generation of fabrication drawings for façade components can be significantly reduced, and all the fabrication drawings for similar components will follow a consistent drawing format with explicit layout, thereby enhancing their readability.
HKUST Automated Optimization and Clash Resolution of Steel Reinforcement in RC Frames Using Building Information Modeling and Hybrid Genetic Algorithm Thesis 08/2017 Mohit MANGAL Reinforced concrete (RC) is widely used in building construction. Steel reinforcement design for RC frames is a necessary and important task for designing RC building structures. Currently, steel reinforcement design is performed manually or semi-automatically with the aid of computer software. These methods are error-prone, time-consuming, and sometimes resulting in over-design or under-design. In addition, clashes of steel reinforcement bars are rarely considered during the design stage and they often occur in beam-column joints on site nowadays. Additional time and manpower are often needed to resolve these clashes in an ad-hoc manner. Sometimes, it is impossible to resolve clashes without moving the steel reinforcement bars and redesigning steel reinforcement layout. Therefore, this research aims to develop a framework for automating the steel reinforcement design process for RC frames using the building information modelling (BIM) technology. BIM has been increasingly popular in the architecture, engineering and construction (AEC) industry for some years, but its use in structural design is still limited to extracting construction design and clash detection. However, BIM models provide much geometric and functional information and can be used for steel reinforcement optimization and clash resolution as well.

This research presents an automated steel reinforcement optimization framework with modified version (considering clash resolution) based on the BIM technology. The first framework uses information from a BIM model to intelligently suggest the number, size and arrangement of three types of steel reinforcement (i.e., tensile, compressive, and shear) with minimum steel reinforcement area. The framework uses the developed hybrid Genetic Algorithm-Hooke and Jeeves (GA-HJ) approach to optimize the steel reinforcement according to the loading conditions, end-support conditions and geometry of the RC member (RC beam or RC column). The developed GA-HJ approach increases the efficiency as well as the quality of the optimum solutions. The modified version of the framework is then developed to utilize and integrate the 3D spatial information of RC frame from a BIM model to provide clash-free and optimized steel reinforcement design. The modified framework uses a two-stage GA approach to provide clash-free, optimized, constructable, and design code compliant steel reinforcement design. Overall, the developed frameworks provide fast and error-free steel reinforcement design with the minimum area of steel reinforcement when compared with currently available steel reinforcement design approaches. In addition, the developed GA-HJ approach can be modified and used to support other building design optimization problems in future.
HKUST Evaluation of the BIM Adoption for Civil Infrastructure and Development of a 5D BIM Financial Decision Making Framework Thesis 08/2015 Qiqi LU Building Information Modeling (BIM) has been widely adopted in the building industry. However, the application of BIM in civil infrastructure facilities, sometimes referred to Civil Information Modeling (CIM), is relatively lacking and slow. Researchers and practitioners are increasingly putting efforts into CIM study and implementation, but so far there is no comprehensive review of their efforts in this regard. Such study can help the academia and industry find the gaps and identify future research direction. Therefore, this work firstly presents a framework to evaluate the current practices of CIM adoption for various civil infrastructure facilities. In this study, civil infrastructure facilities were divided into nine categories for evaluation and the efforts with regard to CIM adoption for each infrastructure category were evaluated in six aspects. This study summarizes the results of 171 case studies and 62 academic papers on CIM. Based on the evaluation and comparison results, research gaps and future direction are identified. For example, CIM uses for detailed design and documentation phase and O&M phase like 5D cost estimation, are seldom conducted and studied.

5D BIM has been studied in academic research and implemented in industry. However, existing studies on 5D BIM focus on cash outflow estimation rather than cash inflow analysis and project financing. This thesis proposes a 5D BIM-based framework for cash flow analysis and project financing. This framework considers contract types and retainage to estimate cash inflow, and cash outflow patterns for equipment, manpower and materials to accurately estimate cash outflow. Project financing scenarios can also be evaluated using the framework. One building case and one bridge case are demonstrated to validate the proposed framework by considering various what-if scenarios. The framework can help contractors analyze the cash flow and make appropriate decisions for different design and payment scheme alternatives in various types of construction projects.