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Title |
AI Application Trends in Concrete Structural Engineering
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Authors |
노병철(Byeong-Cheol Lho) ; 서대원(Dae-WonSeo) |
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DOI |
https://doi.org/10.4334/JKCI.2026.38.2.173 |
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Keywords |
인공지능; 콘크리트 구조공학; 구조 건전성 모니터링; 배합 최적화; 예측적 유지관리 artificial intelligence (AI); structural health monitoring (SHM); concrete structural engineering; mix optimization; predictive maintenance |
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Abstract |
This paper considers the current status and emerging trends of artificial intelligence (AI) in the field of concrete structural engineering. AI applications are categorized into the following four areas and the latest research trends within each are examined: (1) structural health monitoring (SHM), (2) material and mix design, (3) structural design and analysis, and (4) construction and maintenance. In the field of structural health monitoring, significant progress has been made in automated crack and damage detection using deep-learning vision technology, internal defect diagnosis using vibration data, and the prediction of structural durability and remaining service life. In material and mix design, machine learning approaches have improved the accuracy of strength and performance predictions, while optimization techniques such as genetic algorithms have enabled optimal mix design that takes into account both cost and eco-friendliness. In structural design and analysis, AI facilitates structural optimization, reduces analysis time through metamodeling, and supports intelligent design by integrating with digital twin technologies. Finally, in construction and maintenance, AI is leading construction automation through 3D printing and robotics, and contributes to reducing life-cycle costs through predictive maintenance strategies. This paper presents core technologies and application cases for each field and proposes a blueprint for the advancement of concrete structural engineering. It also discusses technical challenges, including data acquisition, model eXplainability (XAI), and field applicability, as well as future research directions.
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