• Journal of The Korea Institute of Healthcare Architecture
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• v.26 no.2
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• pp.63-69
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• 2020

Purpose: The purpose of this study is to identify the neuro architecture items and detailed elements that can be considered for each detailed space in the future medical space design development through the development of a checklist of neuro architecture elements that can be utilized in medical space design. Methods:: This study first develops the neuro architecture element through theoretical research and prepares the basic plan for the checklist through consultation with the employees of the design company in which the researcher works. Finally, a checklist was developed through a survey of nine experts, including designers, hospital staff, and professors. Results: The result of this study 1) The neuro architecture component was developed in seven categories: light, color, sound, air, image, nature, ergonomic furniture and equipment. 2) Specifically, it consists of 49 elements including 7 light elements, 7 color elements, 5 sound elements, 4 air elements, 11 image elements, 6 elements in nature, 9 elements in ergonomic furniture and equipment. It was. 3) Although each of the detailed elements is more preferred according to the space, in general, all the elements should be considered in the context of the hospital space design. Implications: The checklist on the neuro architecture element will enable the development of the most faithful design as an efficient and useful tool for applying the neuro architecture philosophy that considers human beings in hospital design and pursues healing and happiness.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.2
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• pp.43-52
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• 2020

With the advent of the Fourth Industrial Revolution, people need healing. Research in neuroarchitecture shows that people feel happy and stable when working with nature, and patients heal quickly. Therefore, This study aims to quantitatively analyze the colors that help psychological healing in the painting images depicting nature by setting 'Natural Colors' of Biophilic Design as the subject of research. So the purpose of this study was to measure Biophilic Color and to develop Biophilic Color Palette. We extracted Biophilic colors using Impressionist Monet's Landscape painting. After extracting colors using Photoshop Color Picker, we converted RGB color code to NCS color code and Munsell color code. The results of this study were as follows; The ratio of Y was high in the GY-series and YR-series. This is due to the characteristic of impressionism that expresses the change of color by light in close relationship with light. Y is universally considered to be pleasant, representing happiness, sunshine and optimism. Therefore, it is possible to create an environment that helps psychological healing by utilizing the Y-series color palette. Average Blackness was 28. Average Chromaticness was 34.61. The significance of this study is to propose a biophilic color palette that is useful for psychological healing by quantifying the color code of biophilic colors depicted and expressed with adjective images and idiomatic color names. Quantitative and empirical studies on healing colors are needed continuously and should be actively utilized in healing environment planning.

• Journal of the Korea Institute of Building Construction
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• v.7 no.3
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• pp.131-137
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• 2007

To predict deep excavation wall movements is important in the urban areas considering the cost and the safety in construction. Failing to estimate deep excavation wall movements in advance causes too many problems in the projects. The purpose of this study is to propose the forecast model of deep excavation wall movements using artificial neural networks. The data of the Deep Excavation Wall Movements which were done form Long research is used of Artificial neural networks training and apply the real construction work measured data to the Artificial neural networks model. Applying the artificial neural networks to forecast the deep excavation wall movements can significantly contribute to identifying and preventing the accident in the overall construction work.

• Journal of Korean Association for Spatial Structures
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• v.21 no.4
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• pp.39-48
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• 2021

In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

• Journal of the Korea Institute of Building Construction
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• v.21 no.3
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• pp.195-202
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• 2021

Concrete quality is mainly managed through the reliable prediction and control of compressive strength. Although related industries have established a relevant datasets based on the mixture proportions and compressive strength gain, whereas they have not been shared due to various reasons including technology leakage. Consequently, the costs and efforts for quality control have been wasted excessively. This study aimed to develop a web-based numerical model, which would present diverse optimal values including concrete strength prediction to the user, and to establish a sustainable database (DB) collection system by inducing the data entered by the user to be collected for the DB. The system handles the overall technology related to the concrete. Particularly, it predicts compressive strength at a mean accuracy of 89.2% by applying the artificial neural network method, modeled based on extensive DBs.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.317-326
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• 2022

With the development of various technologies in the area of artificial intelligence, a number of studies to application of artificial intelligence technology in the construction field are underway. Diverse technologies have been applied to the task of predicting construction costs, and construction cost prediction technologies applying artificial intelligence technologies have recently been developed. However, it is difficult to secure the vast amount of construction cost data required for machine learning, which has not yet been practically used. In this study, to predict the construction cost, the latest artificial neural network(ANN) method is used to propose a method to improve the construction cost prediction performance. In particular, to improve predictive performance, a log conversion method of target variables and a feature scaling method to eliminate the difference in the relative influence of each column data are applied, and their performance in predicting construction cost is compared and analyzed.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.665-676
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• 2021

As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.

• Journal of the Korea Institute of Building Construction
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• v.12 no.4
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• pp.369-376
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• 2012

In this study, to reduce the hydration heat velocity (HHV) of high-strength mass concrete at early ages, phase change materials (PCM) that could absorb hydration heat were applied, and the changes in autogenous shrinkage were investigated, as well as the relationship between the hydration temperature and autogenous shrinkage. The acceleration of the cement hydration process by the PCM leads to an early setting and a higher development of the compressive strength and elastic modulus of concrete at very early ages. The function of PCM could be worked below the original melting point due to the eutectic effect, while the hydration temperature and HHV of high-strength mass concrete can be decreased through the use of the PCM. A close relationship was found between the hydration temperature and autogenous shrinkage: the higher the HHV, the greater the ultimate autogenous shrinkage.

• Journal of the Korea Institute of Building Construction
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• v.19 no.1
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• pp.1-7
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• 2019

This study evaluated the mechanical properties of the alpha-calcium sulfate hemihydrate replaced concrete to reduce the cracking in a box culvert power transmission. After setting the replacement ratio of alpha-calcium sulfate hemihydrate at 0, 6, 9, 12, and 15%, the setting time, compressive strength, and drying shrinkage were measured and the microstructure and crystal structure were analyzed. As a result, it was confirmed that as the replacement ratio of alpha-calcium sulfate hemihydrate increased, the setting time decreased and the compressive strength declined. On the other hand, when the alpha-calcium sulfate hemihydrate was set with 15% of replacement ratio, about 60% reduction in the drying shrinkage was shown compared to that of ordinary Portland cement. Therefore, it is thought that when the concrete replacing the alpha-calcium sulfate hemihydrate is applied to a box culvert power transmission, the cracking reduction performance will be improved, and the improvement of compressive strength will be required.

• Journal of the Korea Institute of Building Construction
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• v.19 no.4
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• pp.299-306
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• 2019

In general, repair mortar is used to rehabilitate underground communities, but difficulties are encountered in the execution of long-term construction due to spatial co-operatives. In this study, the engineering properties of repair mortar according to the curing condition and accelerator type were reviewed. The results showed that the aluminate, alkali-free and calcium-aluminate precipitates in the water curing conditions showed higher compressive strength at the beginning of age than mortar specimens under air curing conditions, and increased. Especially in CA and AF test specimen with cement mineral quick setting, a large amount of ettringite products were observed compared with AL, thus reducing the pore volume and increasing the strength of the compound by micro-filling effect were found.