• Journal of Korean Association for Spatial Structures
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• v.20 no.4
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• pp.6-13
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• 2020

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.217-218
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• 2021

The development of artificial intelligence in the field of construction and construction is revitalizing. The performance and development techniques of artificial intelligence are changing rapidly, but if you look at the cases of domestic construction sites, they are using technologies from 5 to 7 years ago. It is right to follow a stable method in consideration of commercialization, but the previous AI development method requires more manpower and time to develop than the current technology. In addition, in order to actively utilize artificial intelligence technology, customized artificial intelligence is required to be applied to ever-changing changes in construction sites. it is the reality As a result, even if good AI technology is secured at the construction site, it is reluctant to introduce it because there is no advantage in terms of time and cost compared to the existing method to apply it only to some processes. Currently, an AI technique with a faster development process and accurate recognition has been developed to cope with a fluid situation, so it will be important to understand and introduce the rapidly changing AI development method.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.177-178
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• 2022

As part of the study on the development of the setting time estimation system, this study attempted to confirm the change in hardness values for each influencing factor variable and secure its reliability. According to the research results of this paper, the hardness value of the setting time estimation system tended to gradually decrease in the case of the hardness value of the closing time by curing temperature, and the hardness value increased in the concrete state compared to mortar. Therefore, further research on influencing factors will be conducted in consideration of material and statistical factors in the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.649-652
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• 2021

본 논문에서는 노후화된 목조·석조 건축물의 균열을 탐지하는 기법을 소개한다. 본 기법의 목적은 석조·목조 문화재의 시간의 흐름에 따른 관리 소홀, 균열(벌레, 날씨, 기온 등), 배부름 현상에 의한 문화재의 손상을 사전에 방지하기 위함이다. 기존에 존재하는 목조·석조 건축물의 균열, 노후, 배부름 등 다양한 결함과 변형의 탐지 방법은 접촉식 센서를 이용하여 탐지를 해왔지만, 문화재 자체의 미관을 해칠 뿐 아니라 문화재를 추가로 훼손할 가능성이 있다는 문제점이 제시되었다. 이 문제를 해결하기 위해 문화재 비 접촉형 탐지 기법을 사용한다. CCTV 및 DSLR과 같은 관측장비로 촬영한 영상정보를 기반으로 문화재의 결함과 변형을 AI 영상분석 기반 방법으로 판단하는 문제를 제안한다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.405-406
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• 2023

In the era of AI, intelligent construction safety technologies are being introduced to the construction safety environment, but the application of AI has limitations due to the lack of accident images to learn in complex construction sites. In order to overcome this, we will introduce an intelligent risk detection system that dramatically improves risk detection accuracy by combining AI with digital twin technology, and introduce various cases.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.403-404
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• 2023

In an era where everything is digitalized using AI(Artificial Intelligence), such as the ChatGPT craze, the evacuation guidance system still uses an analog and fixed method, so there is a limit to quick response in case of fire. In order to overcome this, we introduce a digitally transformed evacuation guidance system using AI and discuss its effectiveness.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.162-163
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• 2021

Currently, the depth of cracks is measured using ultrasonic detectors in maintenance practice. This method consists of measuring the depth of cracks by attaching ultrasonic depth measuring equipment to the concrete surface, and there are restrictions on the timing and location of the inspection. These limitations can be addressed through the development of image-based crack depth measurement AI technology. If crack depth measurements are made based on images, restrictions on the timing and location of inspections can be lifted because images acquired with simple filming equipment can be used as input information. To efficiently develop these artificial intelligence technologies, it is essential to identify the interrelationship between crack depth measurements and image characteristic information. Thus, this study is a basic study of the development of image-based crack depth measurement AI technology and aims to identify image characteristic information related to crack depth.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.327-337
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• 2021

The paper acknowledges that many human resources are needed on the research and development (R&D) process of artificial intelligence (AI), and discusses on factors to consider on the current method of development. Enfin, in order to enhance efficiency of AI development, it seems possible through labour division of a few managers and numerous ordinary workers as a type of light industry. Thus, the research team names the development process of AI, which maximizes production efficiency by handling digital resources named 'data' with mechanical equipment called 'computer', as digital light industry of fourth industrial era. As experienced during the previous Industrial Revolution, if human resources are efficiently distributed and utilized, digital light industry would be able to expect progress no less than the second Industrial Revolution, and human resources development for this is considered urgent.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.17-18
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• 2023

This study aims to compare the hardness value development characteristics according to the water-cement ratio during a series of experiments to develop a setting estimator for an AI-based concrete finishing automation system. For the test variables, water-cement ratios are varied with 30, 40 and 50%. Proctor penetration test and surface hardness test by setting time estimator are conducted to estimate the setting time. For the effect of water-cement ratios, they did not affect the surface hardness either, while initial set time and final set time are not constant with water-cement ratios.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.