• Proceedings of the Korean Institute of Building Construction Conference
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• 2010.05a
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• pp.135-138
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• 2010

In the construction industry, construction management system using information technology has been applied diversely to increase productivity. Although IT device such as PDA, RFID, Barcode, wireless network and web camera has been introduced in construction site, the usage of these device is restricted, because these cause engineer to do additional work. The suggested process using Digital Pen in this study, which can lessen engineer's additional work, get defect data promptly. Also accumulated data is utilized effectively for analyzing construction site. The purpose of this study is to introduce Digital Pen System as a means of construction data acquisition for improving productivity.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.545-548
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• 2003

In the past, there were problems in managing construction due to the lack of effective communication and sharing information between a constructor and an owner during construction works. To solve those problems, several major construction companies made great efforts to get a higher productivity through the systematic management, cumulating construction know-how. As a result of the endeavor, some of those companies developed the foundation of PMIS(Project Management Information System). However, it is thought that PMIS on the construction site was not effectively used in performing construction. Therefore, through the case of contractor's management system which is being used in 1-4 out field construction site in Incheon International Airport Rail of P construction company, this paper deals with the development of system supporting to the practical work of construction site and it'll provide to be the foundation of making effective PMIS.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.499-505
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• 2009

Along with the changing pattern that the construction project is getting complicated, large-scaled and up-to-dated, necessity of efficient management of manpower and resources of the construction site is being increased. In particular, considering that the construction site requires a lot of labor force and in terms of the construction cost, labor cost shares 30-40%, labor management has become a very important factor. In this study, in order to support a stable and successful construction project implementation and supplement the existing system being utilized at the current site, construction site access management automation system based on hand vascular pattern, a biometrics system, is intended to be developed. Relevant theory for the system development and the problem of existing RFID system were evaluated and based on this findings, a system design and DB composition chart were envisaged. In addition, by applying this developed system to a number of sites, its result was analyzed

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.170-175
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• 2003

Ground and rock mass considered in tunnelling have characteristics such as uncertainty, heterogeneity and structural complexity because they have been formed undergoing various geological events for a long period. So, it is difficult for engineers to predict behaviors of rock mass in tunneling. In the paper the authors describe the development of an integrated expert system prototype for site investigation, design and construction in tunnelling and introduce the case applying this system to the tunnel construction site under construction. Geostructure Research Group in Korea Institute of Construction Technology (KICT) has developed the system during the past 4 years. The system mainly consists of several modules which is related to the design, construction and management of tunnelling. The test site, Neung-dong tunnel is located in Ulsan, Korea. The geology map shows it may confront big fault zone whose width is over kilometres. With the networking system of ITIS, various information of face mapping, monitoring and other construction task can be transmitted into the database and GIS Server at real time. And necessary analyses can be carried out with the modules equipped in the system.

• Journal of the Korea Institute of Building Construction
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• v.15 no.1
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• pp.115-121
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• 2015

As recent buildings have been enlarged and free-formed, it is hard to represent three-dimensional buildings with two-dimensional drawings and identify design errors, construction errors, difference of construction quantity and construction cost, clash with columns, beams, pipe machinery, and so on. In order to overcome the limitations, BIM(Building Information Modeling) has been used in construction industry. However, BIM in the construction site is not fully facilitated after practical implementation in process control including training, evaluation and analysis of the process. Therefore, in this study, the effect of BIM applied in the construction site is analyzed empirically throughout the survey at the construction site in accordance with the training, evaluation, and implementation of BIM.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.202-203
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• 2020

Unlike price calculation by cost accounting, which categorizes costs into material costs, labor costs, and miscellaneous expenses to determine the construction budget price, construction cost calculation based on Construction Standard Unit Prices utilizes unit prices extracted from market prices of items from projects already completed to estimate costs of similar construction projects. Although unit price information is collected through construction site surveys to revise these construction standard unit prices every year, but due to the limitations of the site survey method, it is difficult to quickly implement the rapid changes in the construction methods and market prices. As such, an important issue that arose was the identification of work items whose prices need urgent revision. This study conducted research on factors that need to be considered when developing online survey system for monitoring construction site market prices. This study is expected to enhance convenience for users, and provide an efficient data collection and management system for administrators.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korean Institute of Rural Architecture
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• v.26 no.2
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• pp.65-72
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• 2024

In order to analyze the impact of fine dust generated from a construction waste intermediate processing site on the surrounding areas, diverse types of samples were collected from inside the site and surrounding areas. The impact analysis results of samples are as follows. (1) Compared to the air quality management standards by the Ministry of Environment, the concentration of fine dust within the site was 30 to 46% for PM10 and 14 to 42% for PM2.5, which was not much different from the general air quality level. (2) It was found that PM10 within the site may have a partial effect on the air quality, but when the blocking facilities in the site, wheel washing facilities at vehicle entry and exit route, and sprinkler during working were maintained, the impact on the nearby area was not high. (3) In the case of PM2.5, its concentration was influenced more by the exhaust fumes from work vehicles than fine dust generated during construction waste processing. Since the PM2.5 concentrations in the site and surrounding area were not much different from the general air quality, there was little correlation with the work impact of construction waste intermediate processing sites. (4) Pb, an indicator of heavy metal components, was within 50ng/m³ in all three sites, which was 10% of the domestic management standard and equivalent to the general air quality level. The complaints from residents in nearby areas were filed using indicators based on visual and experiential information in their daily lives, so even if the survey results of environmental impact by the construction intermediate waste processing site are lower than the standard, nearby residents can feel it better than such numerical information. Therefore, specific activities to reduce find dusts should be continuously continued.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.136-144
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• 2022

Construction sites are characterized by dangerous situations and environments that cause fatal accidents. Potential risk detection needs to be improved by continuously monitoring site conditions. However, the current labor-intensive inspection practice has many limitations in monitoring dangerous conditions at construction sites. Computer vision technology that can quickly analyze and collect site conditions from images has been in the spotlight as a solution. Nonetheless, inspection results obtained via computer vision are still stored and managed in centralized systems vulnerable to tampering with information by the central node. Blockchain has been used as a reliable and efficient decentralized information management system. Despite its potential, only limited research has been conducted integrating computer vision and blockchain. Therefore, to solve the current safety management problems, the authors propose a framework for construction site inspection that integrates object detection and blockchain network, enabling efficient and reliable remote inspection. Object detection is applied to enable the automatic analysis of site safety conditions. As a result, the workload of safety managers can be reduced with inspection results stored and distributed reliably through the blockchain network. In addition, errors or forgery in the inspection process can be automatically prevented and verified through a smart contract. As site safety conditions are reliably shared with project participants, project participants can remotely inspect site conditions and make safety-related decisions in trust.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1138-1145
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• 2022

While pursuing digitalization and paperless projects, the construction industry needs to settle on how to make the most of digitized data and information. On-site workers, who currently rely on paper documents to check and review design and construction plans, will need alternative ways to efficiently access the information without using any paper. Augmented Reality is a potential solution where the information customized to a user is aligned with the physical world. This paper proposes the Augmented Reality framework to deliver the information on on-site resources (e.g., workers and equipment) using head-mounted devices. The proposed framework was developed by interoperating Augmented Reality-supported devices and a digital twin platform in which all information related to ongoing tasks is accumulated in real-time. On-site resources appearing in the user's field of view are automatically detected by an object detection algorithm and then assigned to the corresponding information by matching the data in the digital twin platform. Preliminary experiments show the feasibility of the proposed framework. Worker detection results can be visualized on HoloLens 2 in near real-time, and the matching process obtained the accuracy greater than 88%.