• Proceedings of the Korean Institute of Building Construction Conference
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• 2011.11a
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• pp.15-16
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• 2011

Construction automation and robotics are being taken notice as an alternative to improve productivity and quality. Reflecting on these purpose, automation system, called RCA(Robotic-crane based Construction Automation) system, is developed and have been verified through a pilot project recently in Korea. To apply the RCA system in high-rise building effectively, each management factors of element technologies in the system is derived and assessed critical factor. The purpose of this study, management factors of element techniques, consisted of RCA system, is classified through questionnaires/interviews targeting system developers and construction managers in pilot project site, and critical factors is selected using AHP.

• Journal of the Korea Institute of Building Construction
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• v.13 no.1
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• pp.90-100
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• 2013

Deck-plates are widely used on construction sites, and have the advantage of enabling contractors to achieve a relatively uniform quality. But nevertheless, quality deviation in deck-plate construction can occur as the result of differences in site conditions and the experience of crew workers. In this study, the authors present a site quality evaluation prototype for building deck-plate construction. Through analyses of case studies and interviews with experts, standard quality check sheets and a quality management index were developed by estimating the importance of quality check items based on a Failure Mode and Effects Analysis (FMEA) method. The applicability and effectiveness of the prototype was evaluated through a case study and interviews with case participants. It was found that the prototype promoted an active quality management as a way of continuously improving quality management in deck-plate construction instead of passive quality inspection practice.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.1048-1052
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• 2005

In Korean cases, the occupation of building construction is below 10% of the total industry workers but from a accident rate point of view, it is a risk serious industry and it's hazard is very considerable which accident rate is up to 30% of the total industry workers. The reason of high accident rate of the building construction is its own characteristic, hazard of the work in which is implicated and the shortage of safety counter plan and information, so it is most required to the building construction that educating, training, short and long period approaching to prevent the hazard. The safety information of building construction which is related to safety accident is divided into human and material such as hazard, work situation, cause and counter plan. The existing hazard cases imply all of the information about the work type and work progress of the building construction, so if the research of the every fact is well accomplished, we can obtain the useful information and prevent the hazard of building construction. From this point of view, the emphasis of this study is based on analyzing the cause of hazard first, suggesting the safety information each part of work type and work progress second, and finally making the all existing hazard cases to the Data Base and developing the safety information system.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.723-727
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• 2015

Building energy use estimation relies on building characteristics, its energy systems, occupants, and weather. Energy estimation of new buildings is considerably an easy task when compared to modeling existing buildings as they require calibration with actual data. Particularly, when energy estimation of existing building stock is warranted at a city-scale, the problem is exacerbated owing to lack of construction drawings and other engineering specifications. However, as collection of buildings and other infrastructure constitute cities, such predictions are a necessary component of developing and maintaining sustainable cities. This paper uses Artificial Neural Network techniques to predict electricity consumption for residential buildings situated in the City of Gainesville, Florida. With the use of 32,813 samples of data vectors that comprise of building floor area, built year, number of stories, and range of monthly energy consumption, this paper extends the prediction to environmental impact assessment of electricity usage at the urban-scale. Among others, one of the applications of the proposed model discussed in this paper is the study of urban scale Life Cycle Assessment, and other decisions related to creating sustainable cities.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.12-16
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• 2011

Building Information Modeling (BIM) is being widely spread in AEC industry worldwide and also in South Korea. Although the creation of digital model is better to be started at design stage, it can also improve the productivity of construction by simulating the actual construction process and environment. This paper presents application of BIM-based simulations related with design changes to transfer floors in 58-storey reinforced concrete office building. Transfer floor is not only a structurally important part of the building but also a challenging part of the actual construction in terms of sequence and period due to the complexity of the work. Preconstruction of rebar, mechanical, and plumbing is performed to review the construction drawings and to perform clash detection. Each item of application is evaluated for its effectiveness on actual construction and for the development potential.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.239-240
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• 2023

Construction site management involves overseeing tasks from the construction phase to the maintenance stage, and digitalization of construction sites is necessary for digital construction site management. In this study, we aim to conduct research on object recognition at construction sites using drones. Images of construction sites captured by drones are reconstructed into BIM (Building Information Modeling) models, and objects are recognized after partially rendering the models using artificial intelligence. For the photorealistic rendering of the BIM models, both traditional filtering techniques and the generative adversarial network (GAN) model were used, while the YOLO (You Only Look Once) model was employed for object recognition. This study is expected to provide insights into the research direction of digital construction site management and help assess the potential and future value of introducing artificial intelligence in the construction industry.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2005.05a
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• pp.157-160
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• 2005

The purpose of this paper is to develop a framework for strategic construction knowledge management. The variables of proposed framework includes knowledge types, strategic business functions, and engineering data types. The Proposed framework and knowledge action Plan for the construction company will contribute to develop knowledge management system related to engineering capabilities of strategic business functions

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.115-116
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• 2012

The number of safety accidents of the construction industry is higher than other industries. To prevent the accidents from hazard factor, the employees are variously trained in avoiding an accident. Also many studies have been conducted to reduced the safety accident. But most of these studies are limited to institutional complements. This study proposes safety management improvement plan of safety management through case studies.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.94-102
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• 2013

The architecture, engineering and construction (AEC) industry is beginning to utilize Building Information Modeling (BIM), benefiting from the resulting improved visualization and productivity, better coordination of construction drawings, building documents that contain embedded links to virtual information, faster delivery, and lower costs. However, construction firms often face challenges when reorganizing their company structure to take full advantage of these benefits. This paper proposes an organizational structure that will enable engineering-construction firms to successfully adopt and implement BIM for major construction projects. A case study research method is utilized based on in-depth interviews with four BIM directors and vice presidents charged with BIM adoption, implementation and education. Organizational transformation challenges and recommendations are discussed in detail for those considering implementing BIM in the engineering-construction industry. Topics such as organizational changes in the firm, costs and benefits of BIM implementation, strategies of BIM implementation (execution) plan, BIM education, risks and opportunities associated with BIM, BIM software, contract methods, human resource management and new hires, and the future implementation and direction of BIM are discussed in detail.

• Journal of Construction Engineering and Project Management
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• v.4 no.4
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• pp.9-16
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• 2014

The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.