• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.4
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• pp.245-253
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• 2020

In this study, natural frequencies are compared using several pile-soil interaction (PSI) models to evaluate the effects of each model on resonance safety checks for a monopile type of wind turbine structure. Base spring, distributed spring, and three-dimensional brick-shell models represented the PSIs in the finite element model. To analyze the effects of the PSI models on a natural frequency, after a stiffness matrix calculation and Winkler-based beam model for base spring and distributed spring models were presented, respectively; natural frequencies from these models were investigated for monopiles with different geometries and soil properties. These results were compared with those from the brick-shell model. The results show that differences in the first natural frequency of the monopiles from each model are small when the small diameter of monopile penetrates hard soil and rock, while the distributed spring model can over-estimate the natural frequency for large monopiles installed in weak soil. Thus, an appropriate PSI model for natural frequency analyses should be adopted by considering soil conditions and structure scale.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.197-205
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• 2020

Compared to aerial photogrammetry, UAV photogrammetry has advantages in acquiring and utilizing high-resolution images more quickly. The production of 3D models using UAV photogrammetry has become an important issue at a time when the applications of 3D spatial information are proliferating. Therefore, this study assessed the feasibility of utilizing 3D models produced by UAV photogrammetry through quantitative and qualitative analyses. The qualitative analysis was performed in accordance with the LODs (Level of Details) specified in the 3D Land Spatial Information Construction Regulation. The results showed that the features on planes have a high LoD while features with elevation differences have a low LoD due to the occlusion area and parallax. Quantitative analysis was performed using the 3D coordinates obtained from the CPs (Checkpoints) and edges of nearby structures. The mean errors for residuals at CPs were 0.042 m to 0.059 m in the horizontal and 0.050 m to 0.161 m in the vertical coordinates while the mean errors in the structure's edges were 0.068 m and 0.071 m in horizontal and vertical coordinates, respectively. Therefore, this study confirmed the potential of 3D models from UAV photogrammetry for analyzing the digital twin and slope as well as BIM (Building Information Modeling).

• International conference on construction engineering and project management
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• 2024.07a
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• pp.479-486
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• 2024

This study reviews the recently conducted case studies to explore the innovative integration of Artificial Intelligence (AI) and Machine Learning (ML) in the domain of building facility management and predictive maintenance. It systematically examines recent developments and applications of advanced computational methods, emphasizing their role in enhancing asset management accuracy, energy efficiency, and occupant comfort. The study investigates the implementation of various AI and ML techniques, such as regression methods, Artificial Neural Networks (ANNs), and deep learning models, demonstrating their utility in asset management. It also discusses the synergistic use of ML with domain-specific technologies such as Geographic Building Information Modeling (BIM), Information Systems (GIS), and Digital Twin (DT) technologies. Through a critical analysis of current trends and methodologies, the paper highlights the importance of algorithm selection based on data attributes and operational challenges in deploying sophisticated AI models. The findings underscore the transformative potential of AI and ML in facility management, offering insights into future research directions and the development of more effective, data-driven management strategies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.391-399
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• 2020

In this study, the behaviors of a suction cap shaped as an arch were analyzed using finite element models. The fundamental structural behaviors of both flat- and arch-type caps were compared, and the advantages of the arch-shaped cap were explained. Moreover, the effects of geometric parameters and stiffener arrangements on the stress and deformation of the arch-shaped caps were investigated by comparisons of the changes in the behaviors of the caps. Additionally, the effects of boundary condition at the edge of the cap were analyzed to study the interactions between the cap and cofferdam walls; these results were used to derive the fundamental structural design of stiffened arch caps. Unlike flat caps, the results showed that ring stiffeners could improve the structural behaviors of arch caps remarkably, while the contributions of the radial stiffeners to the structural behaviors of the caps are dependent on constraints at the edges of the caps.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1093-1099
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• 2022

The number of buildings is increasing day by day. The next logical footstep is tackling challenges regarding scarcity of resources and sustainability, as well as shifting focus on existing building structures to renovate and retrofit. Many existing old and heritage buildings lack documentation, such as building models, despite their necessity. Technological advances allow us to use virtual reality, augmented reality, and mixed reality on mobile platforms in various aspects of the construction industry. For these purposes, having a BIM model or high detail 3D model is not always necessary, as a simpler model can serve the purpose within many mobile platforms. This paper streamlines a framework for generating a lightweight 3D model for mobile platforms. In doing so, we use an existing structure's site survey data for the foundation data, followed by mobile VR implementation. This research conducted a pilot study on an existing building. The study provides a process of swiftly generating a lightweight 3D model of a building with relative accuracy and cost savings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.370-378
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• 2020

The Construction Operations Building Information Exchange (COBie) collects and documents a large amount of information from different paths in one place during design and construction projects. This documented information is readily available as a means of continuously transferring data to the facility management systems used by building engineers. In this process, the COBie Worksheet, an open standard form, is used to input the information by simplifying the list required by the user. As a result, COBie was developed to improve dramatically how relevant information is obtained and updated to support operations, maintenance, and asset management at the design and construction stages. On the other hand, to use COBie, a great deal of manual work is required for information linkage and quality inspection with heterogeneous data models. These issues become obstacles to COBie-based facility management system development. This study analyzed the COBie information system and defined the framework for simpler operating maintenance information from BIM (Building Information Modeling). Moreover, the rules for facility management information submission, quality inspection, role definition of framework components, and information linkage were derived. COBie DB schema and support data linkages could be generated effectively based on the proposed framework in prototype development.

• Journal of the Korean housing association
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• v.23 no.6
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• pp.49-59
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• 2012

This study suggested a new model in consideration of long life and constructability of apartment house suggested in the former part. New model suggested the possibility of cost saving based on the idea that people trend to reject because of the recognition that the new model would cost a lot of expense which work as the barrier for the expansion and distribution at the local market so as to prepare the ground for its activation. The Study was aimed at verifying the possibility of cost saving through comparing it with the existing standard apartment house system centered on the skeleton and cladding system among the new structural design models suggested in the former part. Assuming that these existing standard both models should be changed structural design into new model system, the quantity volume, cost and construction period along with the alteration of finished materials between two models were compared altogether. Simultaneously BIM library was built for easy taking-off bill of quantity and consideration of working methodology for construction working cycle, which was translated into construction cost so as to derive the cost of the two subject systems to be counted. Through the analysis, it was concluded that new model would secure variability in the future and constructability along with shortening the construction period (29%) and achieve cost saving (13%) of construction against the those of existing model.

• Journal of the Korean Geotechnical Society
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• v.36 no.8
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• pp.7-15
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• 2020

Constructing the maximum displacement and shear force database for the seismic performance of building with soil-structure interaction under varied earthquake scenarios and geotechnical conditions is critical in developing the neural network-based prediction models. However, using the available 3D FEM-based computer simulation techniques causes high computation costs in developing the database. This study introduces the framework of developing the artificial neural network (ANN) model to predict the seismic performance of building at given Poisson's ratio and shear wave velocity of soil. The simple Single-Degree-Of-Freedom system was used to develop the database and the performance of the developed neural network model is discussed through the evaluated coefficient of determination (R²). In addition, ANN models were developed for 90~100% percentile of the database to assess the accuracy of the developed ANN models in each percentile.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.920-927
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• 2009

Many schedule delays and cost overruns in interior construction are caused by a lack of understanding in detailed and complicated interior works. To minimize these potential impacts in interior construction, a systematic approach for project managers to detect discrepancies at early stages and take corrective action through use of visualized data is required. This systematic implementation is still challenging: monitoring is time-consuming due to the significant amount of as-built data that needs to be collected and evaluated; and current interior construction progress reports have visual limitations in providing spatial context and in representing the complexities of interior components. To overcome these issues, this research focuses on visualization and computer vision techniques representing interior construction progress with photographs. The as-planned 3D models and as-built photographs are visualized in a 3D walk-through model. Within such an environment, the as-built interior construction elements are detected through computer vision techniques to automatically extract the progress data linked with Building Information Modeling (BIM). This allows a comparison between the as-planned model and as-built elements to be used for the representation of interior construction progress by superimposing over a 3D environment. This paper presents the process of representing and detecting interior construction components and the results for an ongoing construction project. This paper discusses implementation and future potential enhancement of these techniques in construction.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1073-1080
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• 2024

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.