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


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Institution Title Type Date Author(s) Abstract Link
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 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.
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.
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 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.
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.