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


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Institution Title Type Date Author(s) Abstract Link
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 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 A semi-automated approach to generate 4D/5D BIM models for evaluating different offshore oil and gas platform decommissioning options Journal 07/2017 Cheng, J.C.P., Tan, Y., Song, Y., Liu, X., and Wang, X. Background
Offshore oil and gas platforms generally have a lifetime of 30 to 40 years, and platform decommissioning is a major issue because many of the existing offshore oil and gas platforms are reaching the end of their service life. There are many possible options for decommissioning offshore oil and gas platforms, and each decommissioning option can be implemented using different methods and technologies. Therefore, it is necessary to have a clear understanding and in-depth evaluation of each decommissioning option before commencing platform decommissioning. 4D and 5D building information modeling (BIM) has been commonly used in the building industry to analyze constructability and to evaluate different construction or demolition plans. However, application of BIM in the oil and gas industry, especially for the platform decommissioning process, is still limited.
This paper suggests and demonstrates the application of 4D and 5D BIM technology to simulate various methodologies to realize various selected offshore platform decommissioning options, thereby visualizing and evaluating different options, considering both the time and resources required for decommissioning process. One hundred and seventy-seven offshore platform decommissioning options are summarized in this paper. A new approach to create multiple 4D/5D BIM models in a semi-automated manner for evaluating various scenario options of OOGP decommissioning was proposed to reduce the model creation time as current way of 4D/5D BIM model creation for each OOGP decommissioning option is time consuming.

In the proposed approach, an OOGP BIM model relationship database that contains possible 4D/5D BIM model relationships (i.e. schedules for different decommissioning methods) for different parts of an OOGP was generated. Different OOGP decommissioning options can be simulated and visualized with 4D/5D BIM models created by automatically matching schedules, resources, cost information and 3D BIM models. This paper also presents an illustrative example of the proposed approach, which simulates and evaluates two decommissioning options of a fixed jacket platform, namely Rig-to-Reef and Removal-to-Shore. As compared to the traditional approach of 4D/5D BIM model generation, the proposed semi-automated approach reduces the model generation time by 58.8% in the illustrative example.

The proposed approach of semi-automated 4D/5D BIM model creation can help understand the implication of different decommissioning options as well as applied methods, detecting potential lifting clashes, and reducing 4D/5D BIM model creation time, leading to better planning and execution for the decommissioning of offshore oil and gas platforms. In addition, with the proposed semi-automated approach, the 4D/5D BIM model can be generated in a more efficient manner.
HKUST Analysis and Evaluation of Indoor Ventilation and Energy Consumption Using Building Information Modeling FYP 06/2017 TSANG, Wing Sum
WONG, Long Yee Mary
YIP, Shing
This project used Building Information Modeling (BIM) and BIM compatible software, Computational Fluid Dynamic (CFD), to analyze the indoor environmental quality of current UG Hall VII building in HKUST under mechanical and natural ventilation. The results obtained from the software analysis were used for evaluating the indoor environment with green building standard BEAM Plus EB Ver. 2 Selective Scheme. Indoor environmental quality analysis and energy analysis on different air-conditioner usage scenarios and modified air-conditioning system were also conducted to investigate whether any modifications could give rise to indoor environment that able to reach BEAM Plus standard while reducing energy consumption. We founded that opening one air-conditioner, with temperature set as 24℃, in only one bedroom in a suite could achieve favourable indoor environment while reducing half of the energy usage on cooling. Also, changing the air-conditioning system from window-type air-conditioners to centralized system could also lower energy consumption on cooling while keeping a comfortable indoor environment. N.A.
HKUST Construction Lift Planning for Prefabricated Units Based on Building Information Modelng and Optimization Techniques FYP 06/2017 LEE, Hoi Yin
LO, Kwong Ching
In recent years, prefabricated construction has been increasingly employed in building projects, especially in vertical extension of existing building. However, current lift planning mainly relies on experience and instinct of site manager, leading to potentially poor lifting schedule that may incur extra time and costs on lifting operations. This project presents a BIM-based lift planning framework for prefabricated modules in vertical extension project that aims to optimize the lifting schedule of prefabricated modules and provide visualization for actual lifting path of the modules. The framework considers three main models: (1) information extraction and geometry simplification model to obtain the module information and simplify the shape of modules, (2) analysis model to calculate the actual lifting path distance of each prefabricated module, and (3) optimization model for the selection of ideal lifting schedule using genetic algorithm (GA). An illustrative example is presented to illustrate and evaluate the proposed framework. The results show that the proposed framework can generate the shortest lifting path for each prefabricated module automatically. The lift planning for prefabricated modules in vertical extension project can be significantly improved by the developed framework. N.A.
HKUST Developing a Building Information Modeling Framework for Facility Management FYP 06/2017 LUK, Ka Yui
TING, Hok Lam
The sustainability of an infrastructure is of paramount importance to protect the benefits of both clients, engineers and its end-users. Building Information Modelling (BIM) therefore has become a vital tool for facility management (FM) to monitor the lifecycle of all building elements. Numerous of frameworks in the industry, however, are unable to locate and trace the asset information details of the building elements automatically for the asset management(AM) in the building lifecycle, especially the operation and maintenance stage. These existing frameworks highly rely on facility managers to locate the building elements and filter the information from a humongous database and carry out further data analysis for asset management strategies plan. Therefore, developing an integrated BIM framework to integrate the use of Radio Frequency Identification (RFID) technology and a FM software is essential for a more advanced facility management, especially the asset management performance of an infrastructure.

In this research, AM is focused and a BIM model of the HKUST library is established as our targeted infrastructure for framework scenario establishment. Numbers of RFID tags have been installed on various library assets to collect respective RFID elements data. A Structured Query Language (SQL) database has been created to store in MySQL and integrate the data of the RFID tags with a FM software, Archibus. A RFID Asset Management website has been established to filter and visualize the required data. Finally, a BIM-based framework for asset management has been attained. The research framework has been applied to a HKUST Library-based AM scenario and the results have proved its AM functions and reliability in enhancing the AM performance of an infrastructure.