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드론 및 비전 프로세싱 기술을 활용한 디지털 건설현장 관리에 대한 연구
서민조 ( Seo Min Jo ),박경규 ( Park Kyung Kyu ),이승빈 ( Lee Seung Been ),김시욱 ( Kim Si Uk ),최원준 ( Choi Won Jun ),김치경 ( Kim Chee Kyeung ) 한국건축시공학회 2023 한국건축시공학회 학술발표대회 논문집 Vol.23 No.2
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.
박경규 ( Park Kyung Kyu ),이승빈 ( Lee Seung-been ),서민조 ( Seo Min Jo ),김시욱 ( Kim Si Uk ),최원준 ( Choi Won Jun ),김치경 ( Kim Chee Kyung ) 한국건축시공학회 2023 한국건축시공학회 학술발표대회 논문집 Vol.23 No.2
Urbanization and the increase in building scale have amplified the complexity of M.E.P design. Traditional design methods face limitations when considering intricate pathways and variables, leading to an emergent need for research in automated design. Initial algorithmic approaches encountered challenges in addressing complex architectural structures and the diversity of M.E.P types. However, with the launch of OpenAI's ChatGPT-3.5 beta version in 2022, new opportunities in the automated design sector were unlocked. ChatGPT, based on the Large Language Model (LLM), has the capability to deeply comprehend the logical structures and meanings within training data. This study analyzed the potential application and latent value of LLMs in M.E.P design. Ultimately, the implementation of LLM in M.E.P design will make genuine automated design feasible, which is anticipated to drive advancements across designs in the construction sector.
이승빈 ( Lee Seung-been ),박경규 ( Park Kyung Kyu ),서민조 ( Seo Min Jo ),김시욱 ( Choi Won Jun ),최원준 ( Kim Si Uk ),김치경 ( Kim Chee Kyung ) 한국건축시공학회 2023 한국건축시공학회 학술발표대회 논문집 Vol.23 No.2
The process of construction site supervision plays a crucial role in ensuring safety and quality assurance in construction projects. However, traditional methods of supervision largely depend on human vision and individual experience, posing limitations in quickly detecting and preventing all defects. In particular, the thorough supervision of expansive sites is time-consuming and makes it challenging to identify all defects. This study proposes a new construction supervision system that utilizes vision processing technology and Artificial Intelligence(AI) to automatically detect and analyze defects as a solution to these issues. The system we developed is provided in the form of an application that operates on portable devices, designed to a lower technical barrier so that even non-experts can easily aid construction site supervision. The developed system swiftly and accurately identifies various potential defects at the construction site. As such, the introduction of this system is expected to significantly enhance the speed and accuracy of the construction supervision process.