FYPs/Thesis/Journal from Higher Education Institutions in Hong Kong | Resources | BIM

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

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 of steel reinforcement in RC building frames using building information modeling and hybrid genetic algorithm	Journal	02/2018	Mangal, M., and Cheng, J.C.P.	Design of steel reinforcement is an important and necessary task for designing reinforced concrete (RC) building structures. Currently, steel reinforcement design is performed manually or semi-automatically using computer software such as ETABS, with reference to building codes. These approaches are time consuming and sometimes error-prone. Recent advances in building information modeling (BIM) technology allow digital 3D BIM models to be leveraged for supporting different types of engineering analyses such as structural engineering design. With the aid of BIM technology, steel reinforcement design could be automated for fast, economical and error-free procedures. This paper presents a BIM-based framework using the developed three-stage hybrid genetic algorithm (GA) for automated optimization of steel reinforcement in RC frames. The methodology framework determines the selection and alignment of steel reinforcement bars in an RC building frame for the minimum steel reinforcement area, considering longitudinal tensile, longitudinal compressive and shear steel reinforcement. The first two stages optimize the longitudinal tensile and longitudinal compressive steel reinforcement while the third stage optimizes the shear steel reinforcement. International design code (BS8110) and buildability constraints are considered in the developed optimization framework. A BIM model in Industry Foundation Classes (IFC) is then automatically created to visualize the optimized steel reinforcement design results in 3D thereby facilitating design communication and generation of construction detailing drawings. A three-storey RC building frame is analyzed to check the applicability of the developed framework and its improvement over current design approaches. The results show that the developed methodology framework can minimize the steel reinforcement area quickly and accurately.	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.	N.A.
HKUST	Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM	Journal	09/2016	Kim, M.-K., Wang, Q., Park, J.-W., Cheng, J.C.P., Chang, C.-C., and Sohn, H.	This study presents a quality inspection technique for full-scale precast concrete elements using laser scanning and building information modeling (BIM). In today's construction industry, there is an increasing demand for modularization of prefabricated components and control of their dimensional quality during the fabrication and assembly stages. To meet these needs, this study develops a non-contact dimensional quality assurance (DQA) technique that automatically and precisely assesses the key quality criteria of full-scale precast concrete elements. First, a new coordinate transformation algorithm is developed taking into account the scales and complexities of real precast slabs so that the DQA technique can be fully automated. Second, a geometry matching method based on the Principal Component Analysis (PCA), which relates the as-built model constructed from the point cloud data to the corresponding as-designed BIM model, is utilized for precise dimension estimations of the actual precast slab. Third, an edge and corner extraction algorithm is advanced to tackle issues encountered in unexpected conditions, i.e. large incident angles and external steel bars being located near the edge of precast concrete elements. Lastly, a BIM-assisted storage and delivery approach for the obtained DQA data is proposed so that all relevant project stakeholders can share and update DQA data through the manufacture and assembly stages of the project. The applicability of the proposed DQA technique is validated through field tests on two full-scale precast slabs, and the associated implementation issues are discussed. Field test results reveal that the proposed DQA technique can achieve a measurement accuracy of around 3.0 mm for dimension and position estimations.	Link
HKUST	Automated Clash-free Steel Reinforcement Design in RC Structures Using BIM and GA	Report	06/2018	Tommy Yuen	Currently, building information modelling (BIM) technology has been increasingly popular in the architecture, engineering and construction (AEC) industry for some years, but it is not widely adopted in structural design. The objective of this project is to develop a framework for automated rebar design in RC beams using the building information modelling (BIM) technology. This project presents an automated rebar design program, based on the latest BIM technology. Design constraints for the optimization are considered according to the Hong Kong Code of Practice. The developed program will make use of the analysis result from structural software to design RC beams, and then the tailor-made genetic algorithm will optimize the final rebar design. Finally, generate the rebars to BIM model in 3D environment. The result shows that BIM can carry out repetitive works and complicated calculations automatically and accurately. Unlike human, they seldom make mistakes through over-tiredness, do not require rest breaks and can carry out in seconds what may take hours to do by manual methods. The overall design process is fully automated, smooth and without error. Therefore, it is anticipated that the time and manpower resource required for structural design and management could be reduced significantly.	N.A.
HKUST	As-built BIM Model Verification Through Field Inspection and Laser Scanning: A Comparative Study	Report	06/2020	HUANG, Cong ZHOU Haoran LIU Hao	BIM model is a prerequisite for the Operation and Maintenance (O&M) of sustainable buildings. Only after having a reliable BIM model can start O&M related work, such as space management and energy management, all these works needs to confirm the accuracy of the BIM model. In this project, the author conducted a verify of the BIM model to ensure that the model was correct before the O&M work started. This project first compared various survey methods, and based on their advantages and disadvantages, chose the laser scanning method and manual survey method for experiments. Then using two selected methods to do site surveying for HUKUST parking lot and LTK to SENG Commons. And the end, given survey recommendations for different types of as-built model verification based on the survey result.	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.