• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.35
• /
• pp.115-122
• /
• 1995

This paper is developed with software system for the construction of periodic maintenance. The system includes records of equipment, maintenance work, failure mode analysis and work standards of maintenability, inspection & repair to establish periodic maintenance system. And the software program is designed with user-oriented to analyze maintenance data and maintenance system of periodic interval times. Also machine operator can easily apply maintenance management system in production & manufacturing field. Visual Basic in the environment of Window system is used as computer program language for graphics and data base management in IBM PC.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2014.05a
• /
• pp.18-19
• /
• 2014

As issued the development of energy resources, EPC work process through the IT technology is essential for efficient business management, and systematic management of data generated in this process is needed. In domestic, the research related to system development for the collection and management of construction data detected in the field has been done continuously, but pipeline business target the long-distance in extreme cold weather, almost no cases have been studied up to now. Therefore, this research is aimed to derive the data item for efficient management in EPC Stage of pipeline business in extreme cold weather. WBS system of EPC work are classified easily at two levels, data items can be divided based on the type of document. In the future I will be expected to be the foundation of the systematic management of data generated in the EPC step-by-step of pipeline business.

• Journal of the Korea Institute of Building Construction
• /
• v.11 no.3
• /
• pp.238-246
• /
• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• Korean Journal of Construction Engineering and Management
• /
• v.5 no.2 s.18
• /
• pp.153-161
• /
• 2004

The systematic material and labor management planning should be established on accomplished EVM data. The matrix method of integrated cost and schedule was used with common category concept according to the construction project control system. The construction project control system was suggested through analyzing process and data modeling based on integrated cost, schedule and material. Information of construction project can be developed the relationship between the field data and the integrated cost, schedule database. Process and data modelling is provide a standard data format which are related to the material, labor management based on integrated cost, schedule database.

• Journal of the Korean Geosynthetics Society
• /
• v.16 no.1
• /
• pp.63-72
• /
• 2017

This study estimates the degree of influence of factors influencing ground surface settlement during tunnel excavation using Shield tunneling trough 3D FE-analyses. Numerical analysis was carried out by considering face pressure, skinplate pressure, excavation length, soil model, element size and soil material properties. Also, Actually constructed shield TBM comparative analysis was conducted by compared with Volume loss model, Pressure model and field measurement data. Skinplate pressure and soil model were the most influential factors, and the analysis results were similar to field measurements when the appropriate skinplate pressure was applied according to the passing stratum.

• Korean Institute of Interior Design Journal
• /
• no.16
• /
• pp.75-81
• /
• 1998

With the rise of the economic level and the improvement of the standard of living the size of the interior work is becoming large and specialized, With the recent opening of the domestic interior decoration market the order of the large-scale interior decoration work is actively received and its efficient construction is vigorously made. Accordingly reliability is required in keeping with all the accuracy of computing interior construction expenses systematically is importantly emerging. The estimation sheet written in a kind of process mode and in an area made as the construction expense breakdown mode were statistically treated and analyzed as well as quantity computation breakdown data. In determing the major factors that expert an influence on the factors of changes in construction expenses as well as the compositional ratio of construction work that becomes basic material for developing the cost model of interior decoration work the following conclusion could be made: Improvement should be made to suit the present situation by synthesizing and arranging the data practically used in current interior construction expenses. Required construction expenses for the kind of work common to each construction field are showing a given proportion and the required construction expenses of rather small scale interior construction work tend to be irregular. It is necessary to compute optimal construction expenses by calculating the optimal period of work and working personnel in consideration of the influential factor in each work.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.736-743
• /
• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.23 no.6
• /
• pp.559-575
• /
• 2021

Due to the increase in ground excavation work, the possibility of ground subsidence accidents is increasing. And it is very difficult to prevent these risk fundamentally through institutional reinforcement such as the special law for underground safety management. As for the various cases of urban ground excavation practice, the ground subsidence behavior characteristics which is predicted using various information before excavation showed a considerable difference that could not be ignored compared to the results real construction data. Changes in site conditions such as seasonal differences in design and construction period, changes in construction methods depending on the site conditions and long-term construction suspension due to various reasons could be considered as the main causes. As the countermeasures, the safety management system through various construction information is introduced, but there is still no suitable system which can predict the effect of excavation and risk assessment. In this study, a web-based system was developed in order to predict the degree of impact on the ground subsidence and surrounding structures in advance before ground excavation and evaluate the risk in the design and construction of urban ground excavation projects. A system was built using time series analysis technique that can predict the current and future behavior characteristics such as ground water level and settlement based on past field construction records with field monitoring data. It was presented as a geotechnical data visualization (GDV) technology for risk reduction and disaster management based on web-based system, Using this newly developed web-based assessment system, it is possible to predict ground excavation impact prediction and risk assessment.

• Journal of Ecology and Environment
• /
• v.47 no.4
• /
• pp.187-192
• /
• 2023

The United States (US) National Science Foundation's (NSF's) National Ecological Observatory Network (NEON) is a continental-scale observation facility, constructed and operated by Battelle, that collects long-term ecological data to better understand and forecast how US ecosystems are changing. All data and samples are collected using standardized methods at 81 field sites across the US and are freely and openly available through the NEON data portal, application programming interface (API), and the NEON Biorepository. NSF led a decade-long design process with the research community, including numerous workshops to inform the key features of NEON, culminating in a formal final design review with an expert panel in 2009. The NEON construction phase began in 2012 and was completed in May 2019, when the observatory began the full operations phase. Full operations are defined as all 81 NEON sites completely built and fully operational, with data being collected using instrumented and observational methods. The intent of the NSF is for NEON operations to continue over a 30-year period. Each challenge encountered, problem solved, and risk realized on NEON offers up lessons learned for constructing and operating distributed ecological data collection infrastructure and data networks. NEON's construction phase included offices, labs, towers, aquatic instrumentation, terrestrial sampling plots, permits, development and testing of the instrumentation and associated cyberinfrastructure, and the development of community-supported collection plans. Although colocation of some sites with existing research sites and use of mostly "off the shelf" instrumentation was part of the design, successful completion of the construction phase required the development of new technologies and software for collecting and processing the hundreds of samples and 5.6 billion data records a day produced across NEON. Continued operation of NEON involves reexamining the decisions made in the past and using the input of the scientific community to evolve, upgrade, and improve data collection and resiliency at the field sites. Successes to date include improvements in flexibility and resilience for aquatic infrastructure designs, improved engagement with the scientific community that uses NEON data, and enhanced methods to deal with obsolescence of the instrumentation and infrastructure across the observatory.

• Journal of the Korean GEO-environmental Society
• /
• v.13 no.8
• /
• pp.25-33
• /
• 2012

Although it is important to reflect the accurate information of the ground condition in the tunnel design, the analysis and design are conducted by limited information because it is very difficult to consider various geographies and geotechnical conditions. When the tunnel is under construction, examination of accurate safety and prediction of behavior are overcome the limits of predicting behavior by Artificial Neural Network in this study. First, construct the suitable structure after the data of field was made sure by the multi-layer back propagation, then apply with algorithm. Employ the result of measured data from database, and consider the influence factor of tunnel, like supporting pattern, RMR, Q, the types of rock, excavation length, excavation shape, excavation over, to carry out the reliable analysis through field applicability of Artificial Neural Network. After studying, using the ANN model to predict the shearing displacement, convergence displacement, underground displacement, Rock bolt output follow the excavation over of tunnel construction field, then determine the field applicability with ANN through field measured value and comparison analysis when tunnel is being constructed.