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


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Date: From


Institution Title Type Date Author(s) Abstract Link
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 Developing efficient mechanisms for BIM-to-AR/VR data transfer Journal 06/2020 Chen, K., Chen, W., Wang, Q., and Cheng, J.C.P. Augmented reality/virtual reality (AR/VR) has been increasingly adopted to enhance visualization of building information modeling (BIM) models. However, there is a lack of mechanisms for efficient data transfer from BIM to AR/VR. On one hand, most semantic information is lost while importing BIM models into AR/VR engines. On the other hand, huge and complicated BIM models can increase the time for model transfer, increase the computation work load while rendering, and reduce the fluency when using AR/VR applications. Therefore, this paper aims to develop efficient mechanisms for BIM-to-AR/VR data transfer to better utilize the information of BIM. In this paper, an ontology-based approach is proposed to transfer semantic information of BIM. Building components in geometric models are classified according to their features and simplified with different polygon reduction methods. As shown in the experimental validation, the proposed mechanisms have the capability to efficiently transfer semantic information of BIM to AR/VR, greatly reduce the number of triangles for geometric models while maximizing the consistency of the overall shape, and improve the framerate in corresponding AR/VR applications. N.A.
HKUST Transfer learning enhanced AR spatial registration for facility maintenance management Journal 02/2020 Chen, K., Yang, J., Cheng, J.C.P., Chen, W., and Li, C.T. Augmented reality (AR), which requires a spatial registration technique, has proved to greatly improve the efficiency of facility maintenance management (FMM) activities. Being one of the most promising techniques for indoor localization, Wi-Fi fingerprinting has been widely used for AR spatial registration. However, localization accuracy of Wi-Fi fingerprinting decreases over time due to dynamics of environmental factors. Readings from different mobile devices can also affect the accuracy negatively. In this paper, a transfer learning technique named transferable CNN-LSTM is proposed for improving the robustness of Wi-Fi fingerprinting while implementing AR in FMM activities. Convolutional neural network (CNN), embedded with long short term memory (LSTM) networks, is utilized to predict the location of unlabeled fingerprints. Multiple kernel variant of maximum mean discrepancy (MK-MMD) is adopted to reduce the distribution difference between the source domain and the target domain, so that the location of the newly collected unlabeled fingerprints can be predicted accurately. As shown in the experimental validation, the transferable CNN-LSTM can achieve an accuracy of 97.1% in short-term (without significant environmental changes) spatial registration, 87.8% in long-term (with significant environmental changes) spatial registration, and around 90% in multi-device spatial registration, indicating a higher accuracy and better robustness over other conventional approaches. Link
HKUST Developing an BIM and Augmented Reality-based Framework for Construction Monitoring and Facility Management FYP 06/2018 CHIU, San Fung
KWOK, Wai Shing
Augmented reality (AR) is an innovative technology, which allows the real-world environment to be augmented by virtual information. In construction industry, the mobile accessibility of building information through building information modelling (BIM) is still limited, a practical AR system with the integration of building information modelling (BIM) to realize real-time collaboration is yet to be developed. In addressing this gap, this project developed an integrated Augmented Reality (AR) and Building Information Modeling (BIM) framework to achieve the real-time collaboration in construction monitoring and facility management. The function of the developed framework is shown in two scenarios about pipe repairing tutorial and real-time collaboration on remoting computer and mobile device. N.A.
HKUST Automatic Generation of BIM Models Based on Photogrammetry and Laser Scanning Point Cloud Data FYP 06/2019 LEUNG, Chi Ching
SONG, Changhao
As-built drawings are essential to provide information about the most updated configuration of a facility or a structure for project delivery and facility management. Yet, it is stated that approximately 55% of the as-built drawings was found mismatching with the updated configuration of the building, incurring an additional cost of $4.8 billion for verification of the as-built drawings. This paper aims to develop a more advanced method towards automated generation of BIM model using point cloud data from laser scanning based on that developed previously by our research team, reducing labour, cost and time consumed in modelling processes. Geometry information extraction was conducted to each category of the point cloud data with the aim to obtain parameters for automated parametric modelling using Dynamo command networks. The proposed approach was validated by successfully generating as-built Revit models for 3 different sites. N.A.
HKUST Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data Journal 01/2020 Yang, L., Cheng, J.C.P., and Wang, Q. As-built building information models (BIMs) are increasingly needed for construction project handover and facility management. To create as-built BIMs, laser scanning technology has gained popularity in the recent decades due to its high measurement accuracy and high measurement speed. However, most existing methods for creating as-built BIMs from laser scanning data involve plenty of manual work, thus becoming labor intensive and time consuming. To address the problems, this study presents a semi-automated approach that can obtain required parameters to create as-built BIMs for steel structures with complex connections from terrestrial laser scanning data. An algorithm based on principal component analysis (PCA) and cross-section fitting techniques is developed to retrieve the position and direction of each circular structural component from scanning data. An image-assisted edge point extraction algorithm is developed to effectively extract the boundaries of planar structural components. Normal-based region growing algorithm and random sample consensus (RANSAC) algorithm are adopted to model the connections between structural components. The proposed approach was validated on a bridge-like steel structure with four different types of structural components. The extracted as-built geometry was compared with the as-designed geometry to validate the accuracy of the proposed approach. The results showed that the proposed approach could efficiently and accurately extract the geometry information and generate parametric BIMs of steel structures. Link