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


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Institution Title Type Date Author(s) Abstract Link
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 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 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 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 Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning Journal 04/2016 Wang, Q., Kim, M.-K., Cheng, J.C.P., and Sohn, H. Precast concrete elements are popularly used and it is important to ensure that the dimensions of individual elements conforms to design codes. However, the current quality assessment of precast concrete elements is inaccurate and time-consuming. To address the problems, this study presents an automated quality assessment technique which estimates the dimensions of precast concrete elements with geometry irregularities using terrestrial laser scanners (TLS). While the scan data obtained from TLS represent the as-built condition of an element, a Building Information Modeling (BIM) model stores the as-design condition of the element. Taking the BIM model as a reference, the scan data are processed to estimate the as-built dimensions of the element. Experiments on a specimen demonstrated that the proposed technique can estimate the dimensions of elements effectively and accurately. Furthermore, a mirror-aided scanning approach, which aims to achieve reduced incident angles in real scanning environments, is proposed and validated by experiments. Link
HKUST Automatic as-built BIM creation of precast concrete bridge deck panels using laser scan data Journal 02/2018 Wang, Q., Sohn, H., and Cheng, J.C.P. Precast concrete bridge deck panels are commonly used for bridge constructions because they enable faster construction and have less impact on traffic flow. The quality of connections between adjacent precast elements must be ensured to guarantee the overall structural integrity of precast systems. Therefore, the dimensional quality of precast concrete panels should be inspected before they are shipped to construction sites for installation. However, current quality inspection of precast concrete elements primarily relies on manual inspection. Furthermore, the as-built dimensions of precast elements are usually stored in paper sheets or Microsoft Excel spreadsheets, making it difficult to visualize and manage the as-built dimensions. This study develops a technique to automatically estimate the dimensions of precast concrete bridge deck panels and create as-built building information modeling (BIM) models to store the real dimensions of the panels. First, the proposed technique conducts scan planning to find the optimal scanner locations for scan data acquisition. Then, the scan data of the target panel are acquired and preprocessed to remove noise data and to register multiple scans in a global coordinate system. From the registered scan data, the as-built geometries of the target panel are estimated. In the last step, an as-built BIM model is created on the basis of the previously estimated geometries. The proposed technique is validated on a laboratory-scale specimen and a full-scale precast concrete bridge deck panel. The experimental results show that the proposed technique can accurately and efficiently estimate the dimensions of full-scale precast concrete bridge deck panels with an accuracy of 3 mm and automatically create as-built BIM models of the panels. Link