2023 Fiscal Year Final Research Report

A New AI Method for Bridge Inspection and Diagnosis that Combines CNN with Highly Accurate Damage Detection and Expertises

Research Project

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Project/Area Number	21H01417
Research Category	Grant-in-Aid for Scientific Research (B)
Allocation Type	Single-year Grants
Section	一般
Review Section	Basic Section 22020:Structure engineering and earthquake engineering-related
Research Institution	The University of Tokyo
Principal Investigator	Chun Pang-jo 東京大学, 大学院工学系研究科(工学部), 特任准教授 (60605955)
Co-Investigator(Kenkyū-buntansha)	宮本崇山梨大学, 大学院総合研究部, 准教授 (30637989) 浅本晋吾埼玉大学, 理工学研究科, 准教授 (50436333) 党紀埼玉大学, 理工学研究科, 准教授 (60623535)
Project Period (FY)	2021-04-01 – 2024-03-31
Keywords	橋梁点検診断 / 損傷検知 / CNN / Deep learning / 維持管理 / Image captioning / VQA / 画像処理
Outline of Final Research Achievements	This research aims to develop a high-precision AI method for bridge inspection and diagnosis. With aging bridges and a shortage of skilled engineers, an AI that uses Convolutional Neural Networks (CNN) to detect damage and integrate expert knowledge is essential. The research developed a domain-adaptive CNN model combining Self-Training approaches with Bayesian Neural Networks, achieving precise damage detection despite varied bridge environments. Additionally, an Image Captioning model was created to generate texts explaining detected damage, making results understandable for both engineers and non-specialists. The study shows significant improvements in detection accuracy and explanation clarity, enhancing decision-making in maintenance. This AI-based approach automates and improves bridge inspection efficiency, addressing engineer shortages and contributing to better infrastructure maintenance.
Free Research Field	維持管理工学
Academic Significance and Societal Importance of the Research Achievements	本研究の学術的意義は，自己訓練アプローチとベイズニューラルネットワークを組み合わせたドメイン適応型CNNモデルを開発し，異なる環境に対応可能な高精度な橋梁損傷検出手法を実現した点にある．これにより，従来の学習データとの乖離を克服し，検出精度を大幅に向上させた．社会的意義としては，技術者不足の課題に対処しつつ，非技術者にも理解しやすい損傷説明文を生成するImage Captioningモデルを開発した点が挙げられる．これにより，維持管理業務の効率化が図られ，インフラの安全性向上に寄与する．本研究は，橋梁点検診断の自動化と効率化を促進し，将来的なインフラ維持管理の革新に繋がるものである．