• International conference on construction engineering and project management
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• 2015.10a
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• pp.674-675
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• 2015

A 3D indoor model becomes an indiscernible component of BIM (Building Information Modeling) and GIS (Geographic Information System). However, a huge amount of time and human resources are inevitable for collecting spatial measurements and creating such a 3D indoor model. Also, a varied forms of 3D indoor models exist depending on their purpose of use. Thus, in this study, three different 3D indoor models are defined as 1) omnidirectional images, 2) a 3D realistic model, and 3) 3D indoor as-built model. A series of reconstruction methods is then introduced to construct each type of 3D indoor models: they are an omnidirectional image acquisition method, a hybrid surveying method, and a terrestrial LiDAR-based method. The reconstruction methods are applied to a large and complex atrium, and their 3D modeling results are compared and analyzed.

• 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 Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7902-7909
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• 2015

The needs for smart city management technology has been increased for efficiently managing huge and complex social infrastructures. Thus, this research introduces smart bridge facility management system based on sensor and BIM technologies so that the inadequacy of current facility maintenance system and the limitations of data objectivity, consistency and expandability can be improved. For which, current trends on sensor and BIM technologies and standard information system were investigated to derive system develop requirements, and a scenario was suggested to discuss how to utilize the suggested system. Research outcomes are expected to be utilized as a preliminary research for developing the real application framework for bridge facility management.

• IE interfaces
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• v.25 no.2
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• pp.216-228
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• 2012

BIM-based 4D simulation makes people easily understand complex construction process using 3D graphics model and helps them review and identify the construction schedule in each phase of the construction process. Moreover, 4D simulation can be used as reference data to determine the validity of the process in the design phase and will be utilized as a measure for checking the construction process. Therefore 4D simulation of construction improves efficiency of project management. However, current commercial applications available for 4D simulation do not provide sufficient functions for connection of 3D models and process information. In this paper, we propose an automated generation method through the definition of the process based on a work method template and developed the template based schedule generation system (TSGS).

• Journal of KIBIM
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• v.11 no.2
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• Journal of the Korea Institute of Building Construction
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• v.14 no.6
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• pp.561-569
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• 2014

In construction site, BIM is used in part of construct-ability review and design mistake checking. However, in a current domestic construction site, except public order project, most of work in private order project has been processing based on 2D work. As an expert resists to certain point in introduction of new technique, it may be shown in the introduction of BIM. For this, We need to analysis construct-ability of introducing BIM, work field of introducing and operational capability about present domestic construction engineers. Therefore, this study solves the problem of existing construct-ability analysis, and it visualizes the various information and objects for an effective job performance. Furthermore, construct-ability analysis by BIM that can use an intergrated management is theoretically examined and practical field-application priority among conclusions is proposed through a survey targeting on the hand-on worker. Therefore, this study suggests a factor supplying for a business-centric introduction plan and support condition.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.125-129
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• 2023

In this paper, we propose a method to design and implement a smart pipeline information management system that can provide visualization information linked to GIS and roadmap based on the construction of precise pipeline buried information. The smart pipeline information management system consists of a positioning device for high-precision pipeline location measurement and surround view image data recording, a database for data storage and management, and a mobile app for remote monitoring and management. It connects surrounding image data and location data with GIS and roadmap. Convenience and accessibility of management can be improved.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.111-120
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• 2011

High-rise buildings have recently increased over the residential, commercial and office facilities, thus an understanding of construction cost for high-rise building projects has been a fundamental issue due to enormous construction cost as well as unpredictable market conditions and fluctuations in the rate of inflation by long-term construction periods of high-rise projects. Especially, recent violent fluctuations of construction material prices add to problems in construction cost forecasting. This research, therefore, develops a time-series model with the Box-Jenkins methodologies and material prices time-series data in Korea in order to forecast future trends of unit prices of required materials. BIM (Building Information Modeling) approaches are also used to analyze injection time of construction resources and to conduct quantity takeoff so that total material price can be forecasted. Comparative analysis of Predictability of tentative ARIMA (Autoregressive Integrated Moving Average) models was conducted to determine optimal time-series model for forecasting future price trends. Proposed BIM based time series forecasting model can help to deal with sudden changes in economic conditions by estimating future material prices.

• Korean Journal of Construction Engineering and Management
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• v.16 no.3
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• pp.113-122
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• 2015

VE workshop is performed based on VE expert' experiences without retrieving VE data of similar previous projects. Moreover, it usually omitted or applied for the sake of formality due to insufficiently understanding VE function, limited time, space and budget. Even though many studies have established VE databases for retrieving and reusing VE data, VE workshop is still inefficient and ineffective to improve projects' values. With this regard, this study proposes a preliminary prototype for improving VE workshop, which utilizes the state-of-the-art information communication technologies(ICTs) including Building Information Modeling(BIM), Mobile Computing(MC), Network Service System(NSS), and Database Management System(DBMS) for better managing, storing and reusing VE data. The prototype was developed to evaluate advantages and limitations. The results show that the proposed prototype can support visual VE data retrieval from similar previous projects, enhance communication among VE team and save much time and cost comparing to traditional VE. Through this, the productivity of VE workshop can improve efficiently and effectively.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.