• Journal of Construction Engineering and Project Management
• /
• v.4 no.2
• /
• pp.1-7
• /
• 2014

Radio Frequency Identification (RFID) has been applied to the construction industry for improving the efficiency of material and process management. Most RFID-related studies have focused on building or plant construction. The application of RFID has been limited in pipeline construction projects where materials are stored and stacked across a large construction site. This paper investigates practical issues in pipeline construction, improves the read rates of RFID tags, and tests their utility by putting them into practice. This paper demonstrates the benefits that may be expected with the use of improved RFID tags and the development of an automated pipeline construction management system. As a result, pipeline construction management time decreased by 28 hours per month compared to the conventional method. Cost decreased by about 26%.

• 한국생물공학회:학술대회논문집
• /
• 2005.10a
• /
• pp.833-833
• /
• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2011.06a
• /
• pp.57-58
• /
• 2011

• Journal of Drive and Control
• /
• v.17 no.4
• /
• pp.181-185
• /
• 2020

• Korean Journal of Construction Engineering and Management
• /
• v.21 no.5
• /
• pp.11-19
• /
• 2020

A daily progress monitoring and further schedule management of a construction project have a significant impact on the construction manager's decision making in schedule change and controlling field operation. However, a current site monitoring method highly relies on the manually recorded daily-log book by the person in charge of the work. For this reason, it is difficult to take a detached view and sometimes human error such as omission of contents may occur. In order to resolve these problems, previous researches have developed automated site monitoring method with the object recognition-based visualization or BIM data creation. Despite of the research results along with the related technology development, there are limitations in application targeting the practical construction projects due to the constraints in the experimental methods that assume the fixed equipment at a specific location. To overcome these limitations, some smart devices carried by the field workers can be employed as a medium for data creation. Specifically, the extracted information from the site picture by object recognition technology of CNN model, and positional information by GIPS are applied to update 4D BIM data. A standard CNN model is developed and BIM data modification experiments are conducted with the collected data to validate the research suggestion. Based on the experimental results, it is confirmed that the methods and performance are applicable to the construction site management and further it is expected to contribute speedy and precise data creation with the application of automated progress monitoring methods.

• KIISE Transactions on Computing Practices
• /
• v.22 no.11
• /
• pp.547-558
• /
• 2016

Construction of multi-site Cloud Infrastructure with OpenStack, the most popular cloud open source project with various ICT infrastructure technologies, has operational inefficiency due to installation complexity and geographical limitation. To address this inefficiency, we gradually developed automated installation/configuration tools to automate installation/configuration of Linux and OpenStack by complying with DevOps methodology over a multi-site OpenStack testbed called OF@KOREN SmartX Playground. However, the pre-developed installation tools can be used only for limited cases. Therefore, we designed and developed a prototype of SmartX Provisioning Framework which could conducts Playground-wide provisioning flexibly by following three steps of resource management in Software-defined Infrastructure. We verified the efficiency of the provisioning functionality of the framework by demonstrating an example of automated multi-site cloud construction.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.1
• /
• pp.679-684
• /
• 2023

Currently, large-scale and deep-depth excavation construction is being carried out in the vicinity of structures due to overdensity in urban areas in Korea. It is very important to secure the safety of retaining structures and underground structures for adjacent excavation work in urban areas. The safety of facilities is managed by introducing an automated measurement system. However, the utilization of the results of the automated measurement system is very low. Conventional evaluation techniques rely only on the maximum value of the measured data, and can overestimate abnormal behavior. In this study, we intend to improve the analysis technique for the automation measurement results. In order to identify abnormal behavior of facilities, a time-series analysis method for automated measurement data was presented. By applying a probability statistical analysis technique to a vast amount of data, highly reliable results were derived. In this study, the analysis method and evaluation method that can process the vast amount of data of facilities have been improved.

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.279-286
• /
• 2013

This paper introduces a new method for identification of building energy performance problems. The presented method is based on automated analysis and visualization of deviations between actual and expected energy performance of the building using EPAR (Energy Performance Augmented Reality) models. For generating EPAR models, during building inspections, energy auditors collect a large number of digital and thermal imagery using a consumer-level single thermal camera that has a built-in digital lens. Based on a pipeline of image-based 3D reconstruction algorithms built on GPU and multi-core CPU architecture, 3D geometrical and thermal point cloud models of the building under inspection are automatically generated and integrated. Then, the resulting actual 3D spatio-thermal model and the expected energy performance model simulated using computational fluid dynamics (CFD) analysis are superimposed within an augmented reality environment. Based on the resulting EPAR models which jointly visualize the actual and expected energy performance of the building under inspection, two new algorithms are introduced for quick and reliable identification of potential performance problems: 1) 3D thermal mesh modeling using k-d trees and nearest neighbor searching to automate calculation of temperature deviations; and 2) automated visualization of performance deviations using a metaphor based on traffic light colors. The proposed EPAR v2.0 modeling method is validated on several interior locations of a residential building and an instructional facility. Our empirical observations show that the automated energy performance analysis using EPAR models enables performance deviations to be rapidly and accurately identified. The visualization of performance deviations in 3D enables auditors to easily identify potential building performance problems. Rather than manually analyzing thermal imagery, auditors can focus on other important tasks such as evaluating possible remedial alternatives.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2009.05b
• /
• pp.15-19
• /
• 2009

As robotic technologies have become more actively utilized to automate many construction tasks, they have been able to improve the construction productivity, quality, and workers safety on site. A new system, of which Robot-based Construction Automation (RCA), is currently being developed, and RCA systems consist of Construction Factory(CF), Automated Bolting Robots, and Climbing Hydraulic Robot. Especially. Climbing Hydraulic robot system is very important to RCA systems because of function as lifting the Construction Factory. In this paper, We validate safety of Climbing Hydraulic Robot system before application for real building construction.

• Journal of Drive and Control
• /
• v.17 no.1
• /
• pp.37-43
• /
• 2020

An intelligent excavator can be divided into Machine Guidance (MG), semi-automatic, and unmanned by technology. The MG technology excavator is equipped with a tilt sensor on each link of the excavator and a GPS is installed on the excavator body to inform the user of the position of the excavator bucket end. Machine control (MC) technology that assists the user's work can be divided into semi-automatic and fully automatic technology. The semi-automatic MC equipment has already been commercialized by Komatsu and Caterpillar. The MC excavator is equipped with an electro-hydraulic system, sensors and controllers to control the excavator bucket end according to the user's needs. In this study, the semi-automated excavator modified based on manual excavator, is equipped with an electro-hydraulic system, a controller system, multi-sensors and a control algorithm is developed to assist in excavation work such as leveling and grading. By applying the developed technology, it was possible to confirm productivity improvement compared to manual digging and leveling work. In the future, further research to improve the accuracy of the hydraulic precision control and collaborative work with heterogeneous construction equipment such as dump truck and automated collaboration tasks technology could be developed.