• Korean Architects
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• s.605
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• pp.18-19
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• 2019

• Korean Architects
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• s.661
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• pp.15-15
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• 2024

• Korean Architects
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• s.661
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• pp.34-37
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• 2024

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.129-130
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• 2022

This study is conducted to identify improvements in the setting time estimation system through the Mock-up test of the finishing automation system and the setting estimation system. As a result of the study, it is necessary to adjust the spring strength of the setting time estimator and the diameter and length of the estimation needle so that the value of the hardness can be measured from 15HD to around 40HD.

• Journal of The Korea Institute of Healthcare Architecture
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• v.29 no.4
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• pp.45-56
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• 2023

Purpose: Recently, Korea has been promoting smart cities that combine artificial intelligence(AI), big data, ICT, and the Internet of Things(IoT), and these technologies are being applied to housing services and are developing into smart housing services. This study try to analyze what is the most necessary and important the AI smart housing services for the housing disadvantaged persons through a survey of experts and the housing disadvantaged persons. And by collecting these necessary and important services, we aim to present elements and directions for the AI smart housing services policy for the housing disadvantaged persons. Methods: Firstly, we asked 11 experts, Secondly, the desire and necessity for the above smart housing service was identified through an online survey targeting the housing disadvantaged persons. Thirdly, the survey was analyzed and reliability was measured through descriptive statistical analysis using SPSS program. Fourthly, based on the results of descriptive statistics analysis, the necessity and importance of AI smart housing services from the perspective of the housing disadvantaged were derived. Results: The results of this study are that firstly, both experts and the housing disadvantaged persons viewed safety and health-related services as the most important and necessary among AI smart housing services, secondly, there is a difference in perspectives on the services that should be priority between experts and people with disabilities, and lastly there are differences in perspectives and needs for services that should be priority between the disabled and the elderly.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.223-224
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• 2021

The process before the model learning stage in AI R&D can be subdivided into data collection/cleansing-data purification-data labeling. After that, according to the purpose of development, it goes through a stage of verifying the model by performing learning by using the algorithm of the artificial intelligence model. Several studies describe an important part of AI research as the learning stage, and try to increase the accuracy by changing the structure and layer of the AI model. However, if the refinement and labeling process of the learning data is tailored only to the model format and is not made for the purpose of development, the desired AI model cannot be obtained. The latest research reveals that most AI research failures are the failure of the learning data rather than the structure of the AI model. analyzed.

• Journal of KIBIM
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• v.14 no.2
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• pp.13-24
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• 2024

This research suggests a novel visualization approach utilizing Generative AI to render photorealistic architectural alternatives images in the early design phase. Photorealistic rendering intuitively describes alternatives and facilitates clear communication between stakeholders. Nevertheless, the conventional rendering process, utilizing 3D modelling and rendering engines, demands sophisticate model and processing time. In this context, the paper suggests a rendering approach employing the text-to-image method aimed at generating a broader range of intuitive and relevant reference images. Additionally, it employs an Text-to-Image method focused on producing a diverse array of alternatives reflecting architects' styles when visualizing the exteriors of residential buildings from the mass model images. To achieve this, fine-tuning for architects' styles was conducted using the Low-Rank Adaptation (LoRA) method. This approach, supported by fine-tuned models, allows not only single style-applied alternatives, but also the fusion of two or more styles to generate new alternatives. Using the proposed approach, we generated more than 15,000 meaningful images, with each image taking only about 5 seconds to produce. This demonstrates that the Generative AI-based visualization approach significantly reduces the labour and time required in conventional visualization processes, holding significant potential for transforming abstract ideas into tangible images, even in the early stages of design.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.107-114
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• 2023

This study described a method of designing and implementing an artificial intelligence-based learning model for predicting the usage of building members. Artificial intelligence (AI) is widely used in various fields thanks to the development of technology, but in the field of building information management (BIM), the case of utilizing AI technology is very low due to the specificity of the data in the field and the difficulty of collecting big data. Therefore, AI problems for BIM were discovered, and a new preprocessing technique was devised to solve the specificity of data in the field. An artificial intelligence model was implemented based on the designed preprocessing technique, and it was confirmed that the accuracy of predicting the construction component usage of the implemented artificial intelligence model is at a level that can be used in the actual industry.

• Korean Architects
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• s.661
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• pp.24-29
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• 2024

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.127-128
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• 2023

The construction industry is increasingly adopting vision AI technologies to improve efficiency and safety management. However, the complex and dynamic nature of construction sites can pose challenges to the accuracy of vision AI models trained on datasets that do not consider the background. This study investigates the effect of background on object recognition for vision AI in construction sites by constructing a learning dataset and a test dataset with varying backgrounds. Frame scaffolding was chosen as the object of recognition due to its wide use, potential safety hazards, and difficulty in recognition. The experimental results showed that considering the background during model training significantly improved the accuracy of object recognition.