2021 Fiscal Year Final Research Report

Bayesian System Identification and Information Integration for enhancement of structural health monitoring

Research Project

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Project/Area Number	19H02225
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	Kyoto University
Principal Investigator	Kim Chul-Woo 京都大学, 工学研究科, 教授 (80379487)
Co-Investigator(Kenkyū-buntansha)	五井良直京都大学, 工学研究科, 助教 (30831359) 張凱淳京都大学, 工学研究科, 講師 (50751723)
Project Period (FY)	2019-04-01 – 2022-03-31
Keywords	構造ヘルスモニタリング / 長期モニタリング / ベイズ異常検知 / 情報融合 / 高度化
Outline of Final Research Achievements	To improve the identification accuracy of structural health monitoring (SHM) considering uncertainties and to upgrade SHM by information fusion, a Bayesian structural identification and anomaly detection method and Bayesian structural integrity estimation were investigated. Laboratory experiments and measurements on real bridges were conducted to verify the validity of the proposed method. The major outcomes obtained are the proposal of a 'time-domain Bayesian structural identification and anomaly detection method based on Bayesian hypothesis testing'. The integrity assessment of bridges by Bayesian finite element model updating was also investigated. It was found that the integrity assessment of bridges by finite element model updating using only a few vibration sensor information has limitations, and that it is necessary to reduce sparsity by using meta models and different sensor combinations.
Free Research Field	構造動力学
Academic Significance and Societal Importance of the Research Achievements	本研究は，今までのモード特性の変化に着目した損傷検知の限界を乗り越えるべく提案として，なるべく多いモード情報を含む損傷指標の提案と計測やデータ処理または理論の不完全性のような不確定性を考慮するベイズ理論に着目している新しいパラダイムの提案であり，関連分野への波及効果も大きく，学術的にも意義のある成果を示している．実橋を対象とした実践的な検討も行っており，ベイズ仮設検定を行うことで高い確率で異常検知が可能あることを示している．少なくとも目視点検と併用し，点検効率の向上につながることを示している．本研究の成果は，橋梁維持管理の合理化につながる重要な役割を果たすことができ，その社会的意義は大きい．