• International conference on construction engineering and project management
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• 2007.03a
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• pp.433-439
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• 2007

A ubiquitous environment in construction requires integrating hardware and software systems. Currently the Construction System Integration Lab (CSIL) at Pusan National University is currently studying the application of ubiquitous technology for better communication in the construction process. In this paper, a pilot of Ubiquitous Concrete Pour System (u-CPS) has been presented to demonstrate the effectiveness of data exchange in the concrete pour process. The u-CPS environment takes advantage of the RFID technology for collecting construction data. The pilot can automatically generate the data for concrete pour work such as departure time, arrival time, concrete pour time. Construction managers can keep track of the progress of concrete pour work using the information. A case study was done for a building construction using the pilot system, the result of which demonstrated that the RFID-base system can help improve the effectiveness of data communication during the concrete pour process.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.323-324
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• 2021

This study is to develop a non-contact construction project management solution that can reduce cost and construction period through on-site information sharing, minimizing contact with others by COVID19, and improving the productivity of the construction industry. Decisions, checklists, and execution rates of construction costs can be checked with smart devices through sharing on-site photos and videos, exchanging opinions. Details and checklist data stored on cloud servers of sites that apply non-face-to-face construction project management solutions will be used as data for analyzing amounts and construction periods depending on the size of the construction. Real-time field information sharing will reduce expected problems and waste factors, expand communication channels with users to prevent or minimize construction disputes and claims, and contribute to the expansion and growth of new research industry markets in construction technology.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.329-333
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• 2008

As construction projects become getting bigger and complicated, the importance of resource management in construction sites is to be emphasized all the more than ever, but the reality is now gone far away leaving data filing and management dependent on the hands of in-field managers, which gives rise to a variety of problems such as decreased productivity. The manually-dependent data management makes it hard to acquire data and information needed for projects both quickly and correctly with abundant cost and endeavor by accumulating the related data, and efficient project management, being exemplified by such practical applications as Bar-code, RFID(Radio Frequency Identification), GPS(Global Positioning System), PDA(Personal Digital Assistants), USN(Ubiquitous Sensor Network) and DAT(Data Acquisition Technology). In this context literature survey is made theoretically, taken into account 14 classification of construction works, 31 types of work classification in CIS, Masterformat as follows;

• International conference on construction engineering and project management
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• 2020.12a
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.721-727
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• 2022

Construction safety remains an ongoing concern, and project managers have been increasingly forced to cope with myriad uncertainties related to human operations on construction sites and the lack of a skilled workforce in hazardous circumstances. Various construction fatality monitoring systems have been widely proposed as alternatives to overcome these difficulties and to improve safety management performance. In this study, we propose an intelligent, automatic control system that can proactively protect workers using both the analysis of big data of past safety accidents, as well as the real-time detection of worker non-compliance in using personal protective equipment (PPE) on a construction site. These data are obtained using computer vision technology and data analytics, which are integrated and reinforced by lessons learned from the analysis of big data of safety accidents that occurred in the last 10 years. The system offers data-informed recommendations for high-risk workers, and proactively eliminates the possibility of safety accidents. As an illustrative case, we selected a pilot project and applied the proposed system to workers in uncontrolled environments. Decreases in workers PPE non-compliance rates, improvements in variable compliance rates, reductions in severe fatalities through guidelines that are customized according to the worker, and accelerations in safety performance achievements are expected.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.267-268
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• 2023

Unmanned patrol robots are currently being developed for autonomous data survey in construction sites. As the amount of data acquired by robots increases, it is important to utilize proper metadata and system to manage data flow. In this study, we developed three materials, metadata design, robot system and web system, in the purpose of automating construction site data survey using unmanned patrol robots. The metadata was mainly designed to represent when and where raw data was acquired. To identify the location of data acquired, localization data from SLAM algorithm was converted to suit the construction drawings. The robot system and web system were developed to generate, store and parse the raw data and metadata automatically. The materials developed in this study was adopted to Boston Dynamics SPOT, a quadruped robot. Autonomous data survey of 360-picture and environment sensor was tested in two construction sites and the robot worked as intended. As a further study, development on the autonomous data survey to improve the convenience and productivity will be continued.

• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.319-325
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• 2011

During a construction project, construction managers must be provided with material information to help them to make decisions more efficiently without delaying the delivery of material. Construction work can be smoothly performed with the proper material supply. Construction duration depends on several material-related decisions, including the order, delivery, and allocation of material to the correct work location. Hence, it is worthwhile to introduce data warehouse techniques that generate subject-oriented and integrated data to construction material management. The data warehouse for construction material management can perform multidimensional analysis and then define KPIs (Key Performance Index) in order to provide construction managers with construction material information such as lead time, material delivery rate, material installation rate and so on. This research proposes a method of effectively facilitating large amounts of data in the operating systems during the construction management process. In other words, the proposed method can supply structured and multi-perspective material-related information using data warehouse techniques.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.291-292
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• 2012

Progress in the construction project, the estimated cost of the measure is very important. Use preliminary estimate cost data for the prediction of construction cost. But, preliminary estimate cost data a clear calculation, there is no way. it is rely on the historical data and the past construction data. As a result, a significant difference in the actual construction cost and the predicted cost of the problem occurs. In this study, taking advantage of BIM Cost Prediction for efficient and rapid preliminary estimate BIM for building database through the study preliminary estimate cost data.

• Korean Journal of Computational Design and Engineering
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• v.6 no.3
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• pp.147-156
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• 2001

In the construction industry, construction process includes a variety of design stages with cooperation and regulation by many related disciplines. In addition, the construction market is growing and becoming more complex. With this characteristic, design information has become more and more complicated and difficult to manage by the simple and traditional data management methodology. In the practical field, although CAD has been widely available for the last ten years, related disciplines have not shared digital information efficiently. This paper suggests a construction CAD data exchange model that has been developed through investigation and analysis of Korean construction CAD practices and foreign CAD standards. Moreover, this paper investigates a CAD data exchange model using STEP to improve information availability and overcome the limit of CAD systems.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.503-508
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• 2005

In the modern, distributed and dynamic construction environment it is important to exchange information from different sources and in different data formats in order to improve the processes supported by these systems. Previous research has demonstrated that (i) a significant percentage of construction data is stored in semi-structured or unstructured data formats (ii) locating and identifying such data that are needed for the important decision making processes is a very hard and time-consuming task. In this paper, an automated methodology for the classification and retrieval of construction images in AEC/FM model based systems will be presented. Specifically, a combination of techniques from the areas of image processing, computer vision, and content-based image retrieval have been deployed to develop a method that can retrieve related construction site image data from components of a project model.